Batteries doi: 10.3390/batteries12070267

Authors: Beytullah Erdoğan Güneyhan Taşkaya

In this study, a liquid-cooled Battery Thermal Management System (BTMS) incorporating aluminum heat-conducting blocks was numerically investigated to enhance the thermal performance of lithium-ion battery modules used in electric vehicles. The proposed system was designed for a battery module consisting of cylindrical lithium-ion cells, and the effects of different geometric configurations on thermal behavior were analyzed using the Computational Fluid Dynamics (CFD) method. To efficiently evaluate the multi-parameter design space with reduced computational cost, a Taguchi L9 orthogonal experimental design was employed. The cooling channel configuration, aluminum heat-conducting block height, and battery pack geometry were considered as the primary design variables. The performance of each design configuration was assessed based on maximum temperature (Tmax) and temperature uniformity (ΔT). Furthermore, an Analysis of Variance (ANOVA) was conducted to quantify the influence of the design parameters on the thermal performance of the system. The results revealed that the configuration comprising eight cooling channels, a 65 mm aluminum block height, and a 1 + 8 cylindrical battery arrangement exhibited the best thermal performance, achieving a maximum temperature of 303.45 K and a temperature difference of 1.25 K. The optimal design configuration provided a more uniform temperature distribution within the battery module, thereby enhancing thermal safety and operational reliability. Overall, the integration of CFD and the Taguchi method offers a systematic and efficient optimization framework for BTMS design, enabling effective evaluation of design alternatives with a reduced number of simulations and shorter computational time.

Batteries doi: 10.3390/batteries12070266

Authors: Ibrahim Atakan Kubilay Derya Birant

State of charge (SoC) estimation for an electric vehicle (EV) is critical for range estimation, preventing overcharging or undercharging, energy management, and battery lifespan. The current studies are typically based on single-instance learning, focus on a specific battery chemistry or a single vehicle type, rely on controlled Lab conditions, and often lack explainability mechanisms. To overcome all these limitations, this paper proposes a more practically applicable and explainable SoC estimation framework that jointly addresses vehicle diversity (bus and passenger EVs), battery chemistry variability (Nickel Cobalt Manganese and Lithium Iron Phosphate), collective battery dynamics, and real-world on-road operational uncertainties within a unified learning architecture. This study introduces MIL-LGBM, a specialized method that integrates Multiple Instance Learning with Light Gradient Boosting Machine to effectively capture the complex nonlinear behavior of battery SoC while maintaining a low computational footprint. An experimental study conducted on a real-world dataset demonstrated that the proposed framework achieved a 0.799 MAE for passenger vehicles across a wide range of on-road driving conditions.

Batteries doi: 10.3390/batteries12070265

Authors: Md Sabbir Hossen Gobbi Ramasamy Ngu Eng Eng Marran Al Qwaid

Second-life electric vehicle (EV) batteries are increasingly recognized as valuable resources for stationary energy storage. However, the heterogeneous degradation of retired batteries makes reliable and application-oriented reuse decisions challenging. Existing studies primarily focus on battery health estimation or degradation characterization, while limited attention has been given to systematically translating experimentally measured health indicators into practical second-life deployment decisions. To address this gap, this study proposes an experimental multi-metric battery health assessment and decision-support framework for application-oriented screening and reuse pathway allocation of retired EV batteries. A total of 91 s life lithium-ion battery cells were experimentally characterized through standardized laboratory charge–discharge testing. Multiple complementary health indicators, including State of Health (SoH), discharge capacity, round-trip energy efficiency, and voltage–current time-series characteristics, were extracted and statistically analyzed to evaluate residual battery performance and degradation behavior. The experimental results reveal substantial variability among retired batteries, with SoH values ranging from approximately 22% to 96%, while more than half of the tested cells exhibit SoH below 60%. Furthermore, batteries with comparable SoH frequently demonstrate different energy efficiencies, indicating that capacity retention alone is insufficient for reliable second-life battery assessment. Building upon these findings, a transparent rule-based decision-support framework is developed to map experimentally measured battery health indicators to application-oriented reuse pathways, including grid-support systems, residential energy storage, backup applications, and recycling. The proposed framework establishes a practical bridge between laboratory battery characterization and deployment-oriented second-life decision-making, providing an interpretable and experimentally grounded methodology for scalable battery screening and sustainable reuse planning.

Batteries doi: 10.3390/batteries12070264

Authors: Lincoln Pinoski Blake Latos Devin Marigny Taylor Jensen Aidan De Los Reyes Brian Helwig Pradeep L. Menezes

The accelerating transition toward renewable electricity generation has elevated grid-scale electrochemical energy storage from an ancillary grid service to a foundational infrastructure requirement. This review provides a comprehensive account of rechargeable battery technologies for stationary grid applications, spanning advanced lithium-ion systems, sodium-ion and post-lithium multivalent chemistries, vanadium and organic flow batteries, solid-state architectures, and high-energy-density future systems such as lithium-sulfur and metal-air cells. The techno-economic context of grid-scale storage is systematically examined, including performance metrics, market drivers, and regulatory frameworks. Each battery chemistry is analyzed with respect to electrochemical mechanism, cycle life, energy density, safety profile, material availability, and commercial readiness. Non-electrochemical storage technologies are discussed as system-level alternatives. Battery safety engineering, thermal management system design, thermal runaway mechanisms and prevention, and failure containment strategies are examined in depth, followed by analysis of critical material supply-chain vulnerabilities, life-cycle assessment, and recycling pathways. The expanding role of artificial intelligence, machine learning, and digital twin frameworks in optimizing performance and enabling predictive maintenance is reviewed. Key challenges, including material bottlenecks, manufacturing scalability, long-duration storage gaps, and the absence of harmonized performance standards, are identified, and the review concludes with a techno-economic roadmap toward cost-competitive, resilient, and low-carbon grid storage.

Batteries doi: 10.3390/batteries12070263

Authors: Yan Jiang Jiawei Chen Rui Liu Yupeng Guo Hai Wang Minghan Zhu Jianying Li

Deploying retired lithium-ion batteries in photovoltaic energy storage systems is a promising second-life application, but heterogeneous aging and internal inconsistencies can induce the barrel effect, reducing available capacity and accelerating pack degradation. Existing equalization methods mainly rely on internal battery states and often neglect external irradiance fluctuations. To address this issue, this study proposes an irradiance-aware adaptive fuzzy active equalization strategy based on a multichannel bidirectional flyback converter. A second-order RC equivalent circuit model with a fifth-order OCV–SOC mapping is established to describe the dynamic behavior of retired cells. Then, solar irradiance and its rate of change are introduced into a dual-input fuzzy controller to adaptively regulate the equalization duty cycle according to both SOC inconsistency and PV input fluctuation. A saturation function constrains the active duty cycle below 0.5 to maintain discontinuous conduction mode operation and avoid transformer core saturation. Simulation results under rapid cloud occlusion, stable high irradiance, and persistent weak light show that the proposed strategy reduces equalization time by 13.8%, 4.4%, and 8.4%, respectively, compared with SOC-only fuzzy control. Under a publicly measured irradiance condition, the proposed strategy achieves the shortest equalization time of 3267.4 s, reducing the time by 24.2%, 27.7%, 29.0%, and 32.9% compared with traditional threshold-based, SOC-only fuzzy, maximum–minimum SOC, and PID-based strategies, respectively.

Batteries doi: 10.3390/batteries12070262

Authors: Sihao Zhang Wenbo Hao Kai Zhao Zengzhe Shi Jian Mei Sergey Grigoriev Chuanyu Sun Xuan Meng

Precise prediction of voltage degradation is critical for the prognostics and health management of proton exchange membrane fuel cells (PEMFCs). The performance degradation of PEMFCs is governed by a complex coupling of multiple physicochemical mechanisms, including catalyst layer and proton exchange membrane degradation. Crucially, these internal degradation processes evolve across highly heterogeneous time scales, ranging from transient high-frequency fluctuations to low-frequency and long-term irreversible performance fade. Conventional predictive models, which typically rely on single-scale architectures or fixed receptive fields, are inherently ill-equipped to simultaneously decouple and capture these cross-scale temporal dynamics. To tackle this challenge, this paper innovatively proposes a multi-scale deep learning framework that integrates a multi-scale degradation trend perception module, a long short-term memory (LSTM)-based encoder–decoder architecture, and a multi-head attention mechanism. One-dimensional convolutional layers with different kernel sizes are employed to simultaneously extract local temporal features at multiple granularities, followed by the LSTM encoder–decoder to model long-range temporal dependencies, while the cross-attention mechanism dynamically allocates attention across the encoded context at each autoregressive decoding step. Experimental outcomes indicate that the proposed model realizes excellent predictive accuracy across five evaluation indices in comparison with standard baselines. In particular, the mean absolute percentage error (MAPE) reaches 1.6696%, and the maximum absolute percentage error (Max-APE) is strictly bounded within 5%, substantiating the reliability of the proposed framework for high precision and long-horizon health prognostics for PEMFCs.

Batteries doi: 10.3390/batteries12070261

Authors: Paul Guenther Andreas Huth Kristian Nikolowski Oliver Lohrberg Mareike Partsch Annegret Potthoff Alexander Michaelis

Fast-charging lithium-ion batteries require graphite anodes with low ionic transport resistance, yet systematic links between electrode manufacturing parameters and fast-charge performance remain scarce. This study shows that twin-screw extrusion (TSE) process conditions control electrode tortuosity, the geometric complexity of ionic pathways, by reshaping the binder network architecture without altering active material integrity. A central composite experimental design combined with multi-scale diagnostics identifies pore network tortuosity as the primary transport bottleneck. The optimized mild kneading condition (K4: 60 wt% kneading zone solids, 7% kneading length, gentle screw design) reduces the 8–80% state-of-charge (SOC) charging time by 14.9% relative to the intensive baseline (B1–B3: 70 wt%, 50% kneading length), matching conventional batch mixing. Regression analysis confirms a strong correlation between tortuosity and fast-charge performance (R2=0.86), whereas the correlation with charge-transfer resistance is weaker (R2=0.59). Mechanistically, mild kneading promotes reversible, sterically stabilized carboxymethyl cellulose (CMC) networks consistent with extended “loop-tail” polymer conformations. Intensive kneading is consistent with the formation of bridging gels that fail to arrest binder migration during drying and clog surface pores. A Pore-Homogeneity Index (PHI), derived from mercury porosimetry, quantifies the resulting microstructural heterogeneity, correlates with tortuosity (R2=0.76), and characterizes pore network uniformity. The results identify local stress intensity as a primary factor influencing binder network formation and support tortuosity as an adjustable design parameter in continuous anode processing.

Batteries doi: 10.3390/batteries12070260

Authors: Long Gao Jinbo Long Zhiyu Jia Zhaoyang Zeng Weirong Liu

Power allocation remains a critical challenge in battery-supercapacitor hybrid energy storage systems (HESS), where effective energy management is essential for improving system performance and extending lithium-ion battery lifespan. Most optimal power allocation methods overlook the crucial role of frequency information, while many frequency-based approaches still lack a multi-objective quantitative optimization mechanism that jointly considers battery degradation, supercapacitor SoC regulation, and energy loss. To address this gap, this paper proposes a frequency-aware optimal power allocation method for battery-supercapacitor hybrid storage systems. First, an optimal power pre-allocation strategy is developed by constructing an objective function that simultaneously considers battery degradation, supercapacitor SoC regulation, and energy consumption. A Sparrow Search Algorithm-based heuristic optimization is then employed to determine the quantitative power allocation ratios between the battery and supercapacitor. Next, the power demand is transformed from the time domain into the frequency domain to extract spectral characteristics. According to the optimized pre-allocation ratios, low-frequency components are assigned to the battery and high-frequency components to the supercapacitor in a quantitative manner. Extensive simulation results demonstrate that the proposed method effectively smooths battery current profiles, reducing battery degradation by up to 11.41% and current fluctuation by up to 12.56% compared with conventional power allocation approaches.

Batteries doi: 10.3390/batteries12070259

Authors: Mina Rezaei Anil Kumar Madikere Raghunatha Reddy Jeremy I. G. Dawkins Thiago M. G. Selva Karim Zaghib

The rapid expansion of lithium-ion battery (LIB) use in electric vehicles and large-scale energy storage systems has intensified the need for sustainable end-of-life management. While most research and industrial efforts have focused on recovering valuable metals, graphite anodes, despite constituting a significant portion of battery mass, remain relatively overlooked. This review evaluates current progress in graphite anode recycling, emphasizing technical challenges, scalability, and economic and geopolitical considerations. Conventional recycling methods, including hydrometallurgical, pyrometallurgical, and direct recycling processes, offer viable routes for material recovery but are often constrained by high energy demands, chemical consumption, and degradation of graphite quality. Regenerated graphite exhibits competitive electrochemical performance, with initial Coulombic efficiencies above 90% and reversible capacities comparable to those of commercial materials. In addition, strategies such as surface modification and defect engineering have proven effective in restoring structural integrity and enhancing cycling stability. Despite these advances, major challenges persist in achieving cost-effective, large-scale implementation and consistent material quality suitable for reuse in battery manufacturing. Given increasing supply risks and rapidly rising global demand for graphite, advancing sustainable recycling technologies has become essential. This review emphasizes the need for integrated technological innovation and supportive policy frameworks to enable the development of a circular economy for graphite.

Batteries doi: 10.3390/batteries12070258

Authors: Prarthana Pillai Banuselvasaraswathy Balasubramanian Krishna R. Pattipati Balakumar Balasingam

Electrochemical Impedance Spectroscopy (EIS) is a non-destructive technique for characterizing the battery behavior for estimating the state of health (SOH). EIS provides frequency-domain information on key parameters, including solid electrolyte interphase (SEI) resistance, charge-transfer (CT) resistance, and ohmic resistance, which are sensitive to battery degradation mechanisms. In an EIS test, a sinusoidal excitation signal is applied to the battery, and the corresponding voltage response is analyzed to extract the impedance spectrum. The reliability of SOH estimation therefore depends critically on the accurate and repeatable extraction of impedance features. This paper investigates the variability in impedance spectra arising from the state of charge (SOC), temperature, rest time, and repeated measurements under nominally identical conditions. This variability is identified as drift and represents previously underexplored variations in the impedance spectrum. To quantify these variations, this work proposes a normalized resistance-based index that captures changes in the impedance spectrum using estimated equivalent circuit model (ECM) parameters. The proposed index is applicable across battery chemistries, sizes, and operating conditions. It is evaluated using published datasets spanning different chemistries, SOC levels, and temperatures, as well as laboratory data collected from repeated EIS experiments. The results show that even at fixed SOC and temperature, repeated measurements can produce measurable bias and variance in ECM parameters. These findings highlight the importance of accounting for drift in EIS analysis and motivate uncertainty-aware battery diagnostics for practical SOH monitoring systems.

Batteries doi: 10.3390/batteries12070257

Authors: Mirela Simijdean Diana Ilea Denisa Szabo Ovidiu Aurel Ghiuță Aurel Mihail Țîțu Mihai Dragomir

The article proposes an exploratory approach that combines literature-based analysis of digital transformation and competitiveness in battery manufacturing ecosystems with survey evidence collected from Romanian companies operating in technology-related sectors. The empirical approach described provides indirect ecosystem-level evidence from Romanian firms potentially relevant to future battery value chains, as full-fledged manufacturers only now entering the strategic horizon. The study is founded on the need for companies to achieve a consistent and committed transformation that goes beyond adopting and integrating various digital technologies, reaching aspects related to production facilities, human–machine integration, and smart governance approaches. The objective of the research is to study the mutual impacts between facilities and processes on one hand, and technology on the other hand, in achieving competitiveness and sustainability for battery manufacturing ecosystems. In this regard, the paper investigates organizational capabilities, workforce adaptability, and digital technology deployment as enabling factors for a successful digital transformation. The results point to an improvement potential that may contribute to the resilience of the emerging battery industry under challenging conditions, while preparing for sector expansion brought about by developing electromobility and renewable energy options. The framework developed customizes general digital transformation capabilities into battery-manufacturing-specific requirements such as traceability, circularity, regulatory readiness, user safety, and ecosystem interconnectivity.

Batteries doi: 10.3390/batteries12070256

Authors: Ibrahim Atakan Kubilay Kadriye Filiz Balbal Kokten Ulas Birant Derya Birant

Range estimation for electric vehicles (EVs) is critical for intelligent transportation systems since it directly affects charging planning, route optimization, driver confidence, energy management, battery utilization, and driver decision-making processes. However, current studies still suffer from issues such as limited accuracy, insufficient interpretability, high computational complexity, dependence on simulation environments, or insufficient generalization capability under dynamic driving conditions. To address these limitations, this paper proposes an M5Boost framework that successfully integrates an additive residual learning methodology with the model tree structure. Unlike conventional boosting approaches, M5Boost combines iterative residual-driven learning, multivariate leaf regression models, tailored tree pruning, and specific smoothing mechanisms to improve prediction accuracy, robustness, and generalization capability for EV range estimation. A benchmark dataset was further systematically extended with newly collected real-world battery-related driving records. Experimental validation showed that the developed model significantly outperformed state-of-the-art models reported in the literature on the same dataset.

Batteries doi: 10.3390/batteries12070255

Authors: Stefan Rizanov Anna Stoynova Georgy Mihov

Reliable state of health (SOH) and remaining useful life (RUL) estimation is essential for lithium-ion battery diagnostics, prognosis, and management across cell, module, and pack levels. Yet the reported performance metrics often remain tied to controlled cell datasets, with batteries degrading due to chemistry shifts, protocol variation, temperature changes, inconsistent and insufficient measurements, and pack-level heterogeneity. This critical review investigates what evidence is required before an SOH/RUL estimator can be considered trustworthy, transferable, and suitable for battery management system deployment. Based on a de-duplicated classified set of 176 scientific works and a supplementary evidence audit workbook, this review synthesizes model-based, machine learning, deep learning, transfer learning, physics-informed, impedance-based, thermographic, relaxation-based, and digital twin approaches through observability, robustness, uncertainty calibration, transferability, and deployment feasibility. A compact mathematical framework formalizes the health inference, domain shift, cross-fidelity degradation, calibrated uncertainty, and BMS-facing validation criteria. The analysis argues that deployment-ready battery health intelligence should be evaluated as an evidence system rather than as a point prediction task. The proposed multi-fidelity validation framework links synthetic cells, controlled aging, module (pack) testing, fleet shadow operation, and closed-loop safety-governed deployment using acceptance criteria, based on worst-domain error, calibration data, warning risk, and computational feasibility.

Batteries doi: 10.3390/batteries12070254

Authors: Phonrut Bousungnoen Padej Pao-la-or

This paper presents a battery-aware control framework for a single-phase integrated battery charger (IBC) for electric vehicles, in which the traction system is reused as part of the charging hardware. The proposed charger consists of a stator-assisted bridgeless totem-pole power-factor-correction AC–DC stage and a bidirectional buck–boost DC–DC stage connected to a 48 kWh, 400 V lithium-ion battery pack. The battery pack is modeled using a lookup-table-based equivalent circuit model with state-of-charge- and temperature-dependent open-circuit voltage and impedance parameters. A conventional double-loop PI controller is used as the baseline, while the proposed strategy combines nonlinear model predictive control, an extended Kalman filter, and lookup-table-based battery parameterization to regulate charging current under electrical and thermal constraints. The system is evaluated under 7 kW, 230 V/32 A and 22 kW, 230 V/96 A charging cases using average-model simulations, switching-model transient simulations, and finite element thermal assessment of the induction motor stator. The average-model results show stable charging from 20% to 80% SOC, with charging times of approximately 275 min at 7 kW and 90 min at 22 kW. The EKF provides bounded battery state estimation, with maximum SOC estimation errors of approximately 1.3% and 2.0% for the 7 kW and 22 kW cases, respectively, while the core-temperature estimation error converges close to zero. The switching-model results confirm feasible duty-command behavior, bounded battery-current tracking error, and a representative DC-link ripple of approximately 8 Vpp. During grid-voltage reduction, the charging current is reduced to keep the grid-current envelope within the intended limit. FEM results show that charging-only motor temperatures remain low, reaching approximately 27.39 °C at 7 kW and 38.82–38.85 °C at 22 kW. The most critical charging-related thermal case occurs at 22 kW after one hour of full-load motor operation with a 40 °C initial condition, reaching approximately 92.32 °C. Overall, these simulation-based findings support the feasibility of the proposed NMPC–EKF–LUT framework as a battery-aware supervisory control strategy for single-phase IBC operation. The proposed controller improves constraint-aware, battery state-based decision-making, while switching ripple and motor thermal response are mainly governed by the power stage, feasible current trajectory, and initial thermal condition.

Batteries doi: 10.3390/batteries12070253

Authors: Shuping Wang Xinyue Zhou Yifeng Cheng Changhao Li Guohong Chen Tian Jiang Bangyu Li Feng Ye Xianzhong Sun

As the application scale of battery energy storage gradually increases, the accurate prediction of the remaining service life of large-capacity energy storage batteries is crucial for high-quality development in this field. To address the issues of insufficient reliability and poor generalization in large-capacity energy storage battery life prediction, a deep learning framework based on a long short-term memory (LSTM) neural network is developed. Early aging data from the first 150 cycles is used for the model, with outliers removed and noise reduced through Savitzky–Golay (SG) filtering. Data normalization and a sliding window method are employed for training. The model is validated on two batches of large-capacity batteries under GB/T 36276-2023 conditions at 25 °C and 45 °C, achieving the root mean square errors (RMSEs) of 0.86% and 0.50%, respectively, over 1000 cycles. Additionally, the method is tested on small-capacity batteries from an MIT dataset, achieving an RMSE of 4.3%. A transfer learning module fine-tunes the model using cycles 151–300, reducing RMSEs to 0.18%, 0.10%, and 3.1% for the three battery sets. This enhances the model’s generalization and offers a practical solution for life prediction in battery inspection and evaluation.

Batteries doi: 10.3390/batteries12070252

Authors: Aurélia Ditto Julien Dauchy Rémi Vincent Dimitri Gevet Cédric Payan Céline Bonnaud Clément Weick

Battery pack fires remain a critical safety concern for lithium-ion battery systems. This study presents a comprehensive application of Fault Tree Analysis (FTA) to identify and structure the sequences of failures that may lead to a battery pack fire. A detailed fault tree is developed for a cell–module–pack architecture equipped with a thermal management system, enabling a clear representation of failure pathways. The analysis highlights four main origins of battery pack fire. Each intermediate scenario is described through dedicated branches of the fault tree to enhance clarity and facilitate its adoption for other battery pack designs and use-cases. As most failure modes involved in battery pack fire do not have reliable probability data available or exhibit strong dependency on usage conditions, a fuzzy logic-based expert approach is employed. Probabilistic data are collected through a questionnaire, allowing the assignment of probabilities to undocumented failure events. A quantified use-case is presented for an electric vehicle, illustrating the practical application of the methodology. The objective of this work is to demonstrate a structured and adaptable methodology for applying FTA to lithium-ion battery pack fire risk analysis. The resulting fault tree, provided as open-access supplementary material, aims to support safety analysis, highlight critical protection failures, and identify current limitations in battery pack safety systems. It can also help identify critical components in order to support the development of rapid and targeted diagnostic strategies for battery packs throughout their lifetime.

Batteries doi: 10.3390/batteries12070251

Authors: Lei Xu Peng Sun Nan Zhou

State of Health (SOH) is a critical metric for evaluating the efficient and reliable operation of lithium-ion batteries (LIBs), although it cannot be directly measured. Accurate SOH prediction throughout the entire lifecycle of LIBs remains a significant challenge, primarily due to severe signal fluctuations and complex degradation mechanisms. In this paper, a novel hybrid Transformer-based architecture for SOH prediction is introduced, termed KF–SAMformer–GRU, which integrates a Kalman filter (KF) optimizer, a sharpness-aware minimization Transformer (SAMformer) model, and a multi-layer gated recurrent unit (GRU). To overcome the challenges of multivariate long-term forecasting, we innovatively integrate SAMformer to extract robust feature indicators, actively mitigating data distribution shifts via sharpness-aware minimization. Furthermore, reversible instance normalization (RevIN) is first introduced to tackle non-stationarity in multi-source datasets, effectively eliminating uncertainty and significantly boosting generalization capability. Complementing this, the KF mechanism is uniquely employed to fuse multi-dimensional features, reducing computational overhead while accelerating training. Finally, the multi-layer GRU precisely refines the mapping between SOH metrics and predicted values. Experimental validation on the NASA and CALCE datasets demonstrates the superiority of our approach, achieving a MAPE of 0.01, an RMSE of 0.91%, and an R2 of 0.99. Notably, the method accurately captures phenomena such as battery capacity regeneration, exhibiting superior performance at peaks and valleys while maintaining high computational efficiency.

Batteries doi: 10.3390/batteries12070248

Authors: Jianlin Wang Shenglong Zhou Zijian Yu Minfeng Liu Lang Liu

Low-power electric all-terrain vehicles (ATVs) experience repeated acceleration, grade-driving, and regenerative-braking events that impose high transient current demand on the battery pack. This study presents a reduced-switch battery/supercapacitor hybrid energy storage system (HESS) as a battery-current-stress mitigation architecture for low-power electric ATVs. Converter-level hardware tests are used to verify the voltage-regulation capability of a 500 W reduced-switch prototype, whereas vehicle-level Simulink evaluations are used to compare battery-current-stress indicators under representative ATV-oriented cycles. The proposed mode-constrained Db4 allocation strategy assigns the smoother positive demand component to the battery and fast transient and braking-related power components to the supercapacitor. Under the ATV-oriented complex cycle, the proposed HESS limits the battery current to 15 A, reduces the RMS battery current from 19.31 A to 12.45 A, decreases the maximum DC-bus voltage sag from 1.528 V to 0.523 V, and recovers 1.738 Wh of regenerative braking energy in the evaluated model. These results indicate reduced battery-current-stress indicators and improved DC-bus regulation within the evaluated operating range; direct battery aging, thermal, and cycle-life validation are outside the scope of the present work.

Batteries doi: 10.3390/batteries12070250

Authors: Wei Lin Lingcheng Zeng Junyu Liu Zhiying Ding

In this work, a fire dynamics simulator numerical model of an industrial and commercial energy storage cabinet equipped with 280 Ah lithium iron phosphate cells is established; full-process quantitative analysis of heat dissipation and the total released mass of CO and H2 is realized; and the spatial–temporal evolution of the cabin temperature field, CO/H2 concentration field and flame spread is systematically captured. The results show that under fully closed conditions, the local peak temperature exceeds 700 °C; additionally, CO and H2 continuously accumulate inside the cabin, with their concentrations rising to a magnitude of 1000 ppm within 60 s after thermal runaway initiation. In contrast, the open-top structure forms an unobstructed buoyancy-driven venting channel, which guides high-temperature flue gas, CO and H2 to efficiently discharge outward. The results indicate that the peak temperature and peak concentrations of CO and H2 in the opened condition drop by more than 80% compared with the closed case. The designated top vent channel effectively cuts down the total residual mass of toxic and combustible gases inside the cabin and suppresses continuous heat accumulation, remarkably mitigating explosion and poisoning risks triggered by trapped heat and hazardous gas mixtures.

Batteries doi: 10.3390/batteries12070249

Authors: Yongfang Zhu Qing Xie Jingli Jia

Electrode-particle fracture and thermal runaway remain major safety and durability challenges for lithium-ion batteries. Deterministic degradation models are limited in representing random crack nucleation, long-range crack interactions, and critical transitions from stable operation to failure. A computational framework is proposed that combines a Lévy-jump-driven nonlocal stochastic partial differential equation (SPDE) model with a Bifurcation-Aware Physics-Informed Neural Network (BA-PINN). The framework couples fractional diffusion, peridynamic damage evolution, thermal feedback, state-space eigenvalue tracking, and damage-variance monitoring. Evaluation is conducted on controlled synthetic fracture simulations, Oxford battery cycling records, and open-access abuse-test records from the Battery Failure Databank. The damage-field results are interpreted as numerical consistency and surrogate-learning evidence, with direct experimental crack-map validation remaining outside the present dataset scope. On the simulated fracture dataset, the proposed method obtains a damage-field mean squared error of 0.023 ± 0.002 and a structural similarity index of 0.962 ± 0.006. For the evaluated thermal-runaway warning task, it achieves an AUC-ROC of 0.987 ± 0.004 and an average model-inferred warning lead time of 5.2 ± 0.2 h. These results demonstrate the methodological feasibility of combining stochastic nonlocal fracture modeling with bifurcation-aware learning. However, broader validation remains necessary, particularly using particle-resolved experiments and larger event-level thermal-runaway datasets.

Batteries doi: 10.3390/batteries12070247

Authors: Hanfei Luo Hao Zhang Rui Wang Zhiping Song

Organic cathode materials (OCMs) are widely regarded as promising candidates for sustainable rechargeable batteries; however, their practical application is hindered by insufficient electrochemical performance and a lack of scalable synthesis methods. Building on our previous study of poly(o-phenylenediamine) (PoPDA), we herein present a single-precursor, solid-phase synthesis of poly(o-phenylenediamine) sulfide derivatives (PoPDAS). Using o-phenylenediamine sulfide (oPDAS) as the sole precursor, thermal treatment at 300–350 °C triggers H2SO4 and its decomposition products to simultaneously drive oxidative polymerization forming a conjugated PoPDA backbone, and in situ sulfurization introducing polysulfide (–Sn–) linkages. The dual redox activity of C=N bonds in phenazine repeating units and S–S bonds in –Sn– linkages enables a high theoretical capacity, while the robust polymer matrix effectively confines soluble sulfur species during cycling. To optimize the trade-off between reversible capacity and long-term stability, a secondary sulfurization step has been implemented. Among fourteen samples prepared via varied synthetic routes and conditions, PoPDAS-B-350-0.5 with a moderate sulfur content of 27 wt% exhibits the best performance, delivering a reversible capacity of 358 mAh g−1 and 88% capacity retention after 800 cycles. Electrochemical analysis and ex situ characterization confirm the redox mechanism involving both C=N and S–S groups, and reveal the excellent cycling stability attributed to the robust polymer backbone that confines dissociated sulfur species. These results highlight the potential of integrating multiple redox-active moieties into a polymer architecture via a scalable solid-phase synthesis to afford practical OCMs.

Batteries doi: 10.3390/batteries12070246

Authors: Lu Lu Xunzhao Zheng Shaojin Wang Binyu Xiong Jun Feng Jinrui Tang Feifei Dong Chonghui Liu

Vanadium redox flow batteries (VRBs) are a promising technology for large-scale energy storage because of their high safety, long cycle life, and flexible capacity design. However, pump malfunctions during operation may disturb electrolyte flow distribution, induce electrochemical instability, and, under severe conditions, accelerate stack degradation, thereby reducing system safety and operational reliability. Restricted by factors including the nonlinear coupling between sensor signals and operating conditions, as well as the intricate electrochemical processes triggered by pump faults, effective fault diagnosis for VRB pumps remains a prominent challenge. The paper proposes a novel Temporal Convolutional Network (TCN)–Long Short-Term Memory (LSTM)–Multi-Head Self-Attention (MATT) deep learning framework for intelligent pump fault diagnosis. The framework operates through three complementary stages. Comprehensive experimental validation is conducted using a purpose-built VRB fault experimental platform under various current conditions. The results show that the proposed model achieves diagnostic accuracies exceeding 90% for all three investigated pump fault types, namely bilateral pump fault, positive pump fault, and negative pump fault. Comparative analysis confirms that the proposed model significantly outperforms other architectures. The effectiveness of the MATT in enhancing temporal feature extraction and fault diagnosis accuracy for VRB systems is validated.

Batteries doi: 10.3390/batteries12070245

Authors: Teng Liu Wei Li Zhiqiang Li Shangbo Wu

The global adoption of electric vehicles (EVs) highlights the critical role of lithium-ion battery packs in ensuring safety and performance, while voltage outliers as precursors to thermal runaway pose significant risks. Existing fault detection methods suffer from limited adaptability, poor uncertainty quantification, and inadequate handling of long-term temporal dynamics. To address these gaps, this study proposes a Probabilistic Hierarchical Adaptive Framework (PHAF) for early, uncertainty-aware detection of impending voltage outliers. PHAF integrates three core innovations: (1) the Weighted Outlier Depth (WOD) metric, which fuses Boltzmann-weighted voltage deviations and gradient-based thermal penalties to sensitively capture electro-thermal anomalies, especially under thermal stress (>45 °C); (2) the Learnable Spectral Convolution Network (LSCN), a novel architecture that combines adaptive spectral modulation and dual-path convolutions to model long-range frequency patterns and local temporal dependencies in voltage sequences; and (3) a hierarchical multi-model system that dynamically selects specialized models (LSCN, GRU, and LSTM) across four prediction horizons (160–40 min), leveraging quantile regression for uncertainty quantification and an early-termination mechanism to optimize computational efficiency. Evaluated on real-world data from 60 AITO EVs, PHAF achieves 95.4% classification accuracy for Level 1 (early-stage) faults at the 160 min horizon, >90% accuracy for critical Level 3 faults within 80 min, and a maximum AUC of 0.943 for long-term anomaly detection. This framework enables a transition from passive remediation to active prevention of battery thermal runaway, providing reliable, confidence-aware monitoring for safety-critical EV applications.

Batteries doi: 10.3390/batteries12070244

Authors: Zhou He Michael Osterman Michael Pecht

While lithium-ion batteries are rated for specific operational and storage limits, their performance degrades over time, even when operated within these rated conditions. To meet the target lifetime requirements, designers operate and store batteries at derated capacity, voltage, current, and temperature. Although derating strategies and battery life-extension models have been reported in the literature, they do not specify what degradation data are required or how the datasheet-rated limits can be converted into quantitative derating margins. This paper presents a battery derating method that includes identifying critical datasheet-rated parameters, specifying required degradation data, defining analysis procedures, and assessing the effects on battery performance and lifetime. The method defines the minimum information required for derating analysis and introduces quantitative metrics to evaluate both the magnitude of stress reduction and the resulting degradation reduction. The developed approach is intended for product design-stage decision-making, enabling engineers to determine appropriate derating for their target application requirements and evaluate the expected degradation reduction and lifetime implications based on degradation data.

Batteries doi: 10.3390/batteries12070243

Authors: Michal Fišer Martin Kozelka Pavla Hošková Přemysl Jedlička Martin Kotek Milan Straka Luboš Buzna Martin Libra

Understanding and managing the electric vehicle (EV) charging network is expected to become a major challenge for future electricity grids, driven by the growing penetration of battery electric vehicles. This study analyzes two real-world datasets from the Czech Republic, representing public and workplace charging sessions, each further categorized into AC and DC charging, with a focus on their key operational differences. Workplace charging is characterized by significantly longer session durations, higher energy delivered per session compared to public charging, and a distinct peak in energy use on Mondays. In contrast, public charging sessions peak on Fridays. Cross-country comparisons highlight substantial differences in charging behavior, driven primarily by local charging infrastructure conditions and EV fleet composition. To our knowledge, this is the first in-depth analysis comparing public and workplace charging based on real-world data from charging stations. The scientific novelty of the study lies in showing that charging-session parameters are shaped not only by charging location and AC/DC technology, but also by battery electric vehicle (BEV)/plugin-hybrid-electric-vehicle (PHEV) fleet composition and provider-specific pricing strategies, including overstay-fee policies. The findings suggest that EU- and national-level policies and subsidy schemes should consider not only the total number and installed power of charging points, but also the composition of the charging mix, including workplace charging and different forms of public charging such as on-street AC, commercial charging, and high-power DC charging. Such differentiation is particularly important for smart grid integration, demand flexibility, and the development of grid-compatible charging infrastructure.

Batteries doi: 10.3390/batteries12070242

Authors: Bingqian Zhang Lin Fu Jingqian Wang Menglan Lv Tong Shu Guocheng Li Yuanjian Li Juan Du Mintao Wan

Sodium (Na) metal anodes suffer from poor processability, severe volume fluctuation, unstable interfacial chemistry, and uncontrolled dendrite growth during cycling, which significantly hinder their practical application. Herein, a Na/Na2In composite foil is fabricated through an in situ spontaneous alloying reaction enabled by a simple rolling–folding process using Na and indium (In) foils as precursors. Structural characterizations confirm the complete conversion of metallic In into the Na2In alloy phase, forming a continuous architecture with uniformly distributed Na2In networks embedded within the Na matrix. Owing to the sodiophilic and mechanically robust Na2In framework, the Na/Na2In composite anode effectively regulates Na plating/stripping behavior and suppresses dendritic growth, thereby maintaining a dense and stable electrode morphology during repeated charge/discharge processes. As a result, the Na/Na2In symmetric cell exhibits stable cycling for over 900 h at 0.5 mA cm−2 and 1 mAh cm−2 with low polarization hysteresis, whereas the pure Na counterpart fails after only 143 h. Moreover, full cells paired with NaFe1/3Ni1/3Mn1/3O2 cathodes deliver enhanced cycling stability, retaining 87% of the initial capacity after 100 cycles at 0.5 C, together with improved rate capability. This work demonstrates a scalable mechanical fabrication strategy for high-stability Na metal composite anodes and provides new insights into the practical development of high-energy-density Na metal batteries.

Batteries doi: 10.3390/batteries12070241

Authors: Yanli Miao Xiao Tan Chenying Li Jianjun Liu Ling Sa Xiaohan Li Zongjia Qiu

The accuracy of lithium-ion battery thermal-runaway early warning is strongly affected by the State of Charge (SOC). To improve the adaptability of fixed-threshold strategies, this study investigated SOC-dependent temperature and gas responses of 18650 LiNi1/3Co1/3Mn1/3O2/graphite cells under thermal abuse at 50%, 75%, and 100% SOC, representing limited and complete thermal-runaway scenarios respectively, using a sealed pressure-resistant chamber. Temperature and chamber concentrations of characteristic gases, including CO2, CO, C2H4, and CH4, were monitored. The results show that higher SOC lowers the critical temperature for rapid self-heating, advances characteristic gas appearance, and increases the measured chamber gas concentrations by approximately 2.1–2.8 orders of magnitude. Reaction-kinetics analysis indicates that stronger electrolyte reduction by highly lithiated graphite at high SOC is the main reason for the different gas-evolution patterns. Based on these findings, an SOC-adaptive dual-parameter threshold model combining temperature and CO2 concentration was established and retrospectively evaluated. The model provides earlier and more balanced warnings than fixed-threshold strategies, while the limitations associated with discrete GC-MS sampling and practical BMS implementation are discussed.

Batteries doi: 10.3390/batteries12070240

Authors: Felix Elsner Stefan Pischinger

Thermal runaway (TR) in batteries remains a key safety challenge, yet its prediction is hindered by strongly coupled physics and many interdependent influencing factors. This review bridges the gap between mechanistic TR overviews and narrowly scoped experimental studies by conducting a broad database-driven review of published TR experiments. Therefore, the largest publicly available TR database to date is curated. It comprises 1703 tests from 257 papers and 203 variables describing cell properties, test conditions, and TR outcomes. Descriptive and pairwise inferential methods are applied to identify recurring patterns reported across the literature and to enable structured description of observed trends. Cathode chemistry, specific energy, and state of charge (SOC) emerge as the key associates of characteristic TR temperatures, with oxygen release from nickel-rich cathodes significantly amplifying TR severity. Aging-related effects strongly depend on the specific aging history and remain insufficiently characterized. Relative mass loss can reach 90% and is linked to the severity of TR reactions and the associated gas generation. On average, vent gas volume scales at 1.7 L/Ah, but capacity-normalized volume varies significantly with cell chemistry and SOC. H2, CO, and CO2 dominate vent gas compositions, with dependence on chemistry, SOC, and overall explosivity, while toxic and condensable species are clearly under-reported. The influence of abuse type and test setup on measured TR characteristics is highlighted, and emerging battery technologies are discussed. The database and derived trends provide a basis for benchmarking cell safety, informing pack-level design and modeling, suggesting future research directions, and supporting the development of standardized TR test protocols.

Batteries doi: 10.3390/batteries12070239

Authors: Mircea Nicolaescu Carmen Lazau Corina Orha Cosmin Codrean Cornelia Bandas

Recently, numerous synthesis methods have been developed for the preparation of nanostructured materials for supercapacitor applications, and top-down strategies have gained increasing attention due to their relative simplicity and reduced processing complexity. In particular, the combustion method is recognized as one of the simplest and most rapid approaches for producing a wide range of materials. Within this study, the combustion of Cu48Zr47Al5 amorphous ribbons was employed, and the supercapacitor electrodes based on Cu-Cu2O/ZrO2 mixed oxide were developed. The morpho-structural properties of the materials were investigated by X-ray diffraction (XRD) and scanning electron microscopy (SEM), and the electrochemical performance, particularly for supercapacitor applications, was evaluated by cyclic voltammetry (CV) and galvanostatic charge–discharge (GCD) measurements. The CV curves indicate that the Cu–Cu2O/ZrO2 mixed oxide structure acts as a positive electrode and exhibits a non-rectangular shape, confirming pseudocapacitive behavior of the as-synthesized material. A maximum areal specific capacitance of 472.7 mF cm−2 was obtained at a scan rate of 5 mV s−1. From GCD analysis, an areal specific capacitance of 336.5 mF cm−2 was achieved at a current density of 1 mA cm−2. Cycling stability was evaluated over 1000 charge–discharge cycles, showing an increase in capacitance to 135.14% after the 1000th cycle, attributed to the progressive activation of the electrode material. This study highlights the potential of Cu–Cu2O/ZrO2 mixed oxides prepared via self-sustained combustion as efficient and durable electrode materials for supercapacitors. The findings provide a starting point for the future optimization of amorphous alloys for the synthesis of mixed-oxide materials through a scalable fabrication process, paving the way for advanced energy storage applications.

Batteries doi: 10.3390/batteries12070238

Authors: Justin An Aigbe E. Awenlimobor Jiajun Xu Miaomiao Ma

Lithium-ion batteries experience structural degradation during operation and storage, which can negatively impact performance, safety, and service life. Early identification of these degradation-induced structural changes is important for battery health assessment and reliability monitoring. This study proposes a deep learning-based framework for classifying the structural condition of 18650 lithium-ion batteries using X-ray micro-computed tomography (µCT) images. The proposed approach combines centroid-based core cropping, image normalization, three-slice stacking, and transfer learning using a fine-tuned InceptionResNet-V2 architecture. Three adjacent µCT slices are stacked into an RGB-like representation to preserve local three-dimensional structural information while maintaining compatibility with a two-dimensional convolutional neural network. The original classification head of InceptionResNet-V2 was replaced with a custom classification block consisting of dropout layers, fully connected layers, and a SoftMax classifier optimized for battery condition recognition. The framework was evaluated using four battery structural conditions: pristine, cycle-aged, calendar-aged, and thermally cycled cells. Experimental results demonstrated an overall classification accuracy of 96.62%, with a precision of 95.62%, sensitivity of 96.94%, specificity of 98.92%, and F1-score of 96.20%. Comparative analysis with previously reported battery imaging studies demonstrated that the proposed framework achieves competitive performance while addressing the challenging task of structural condition classification from µCT imagery. The results demonstrate the potential of combining advanced X-ray imaging and transfer learning for automated lithium-ion battery structural health assessment and degradation monitoring.

Batteries doi: 10.3390/batteries12070237

Authors: Xuan Wang Yixi Zhao Rui Yin Yunsong Zhang Yaoqi Feng Qing Yan Yijie Wang Ling Chen

With increasingly stringent global requirements on emission reduction and environmental protection in the maritime industry, lithium battery-powered ships have developed rapidly due to their near-zero emissions and low noise characteristics. However, the transition to power systems also introduces new safety challenges, particularly the risk of thermal runaway in lithium batteries under mechanical abuse conditions. To address the complex external loads encountered during actual ship operations, this study establishes a mechanical simulation model of a marine battery rack and battery modules based on Abaqus finite element software. By applying longitudinal, transverse, and vertical accelerations, the stress responses and failure characteristics of the battery rack under different operating conditions are systematically investigated.

Batteries doi: 10.3390/batteries12070236

Authors: Yuxiang Kuang Dongxu Guo Yuejiu Zheng

Early-cycle lifetime prediction of lithium-ion batteries is important for rapid cell screening, battery development, and manufacturing quality control. However, accurate prediction at the early stage remains difficult because capacity fade is usually very limited during the initial cycles, and the available degradation signals are weak. In this study, an early degradation voltage morphology (EDVM)-based framework is proposed for early cycle-life prediction. Two statistical features and one degradation mode voltage signature (DMVS) feature are extracted from the discharge capacity-difference profiles between the 10th and 3rd cycles and combined with an extreme gradient boosting (XGBoost) model. Validation on 138 commercial NCM811 cylindrical cells shows that the proposed framework achieves a mean absolute percentage error (MAPE) of 12.29% using only the first 10 cycles of data. In addition, the DMVS feature identifies three groups of early degradation behavior and provides physically interpretable information on degradation heterogeneity. These results indicate that the proposed method is an efficient and interpretable approach for early cycle-life prediction and has practical potential for battery evaluation and screening.

Batteries doi: 10.3390/batteries12070235

Authors: Yong Ding Ruixin Jia Zhongzheng Huang Zhoujian An

The pouch cells are typically assembled into modules with mechanical preload to meet voltage/capacity requirements, and the stress state is a critical factor influencing the failure behavior of lithium-ion batteries during external short circuits. This study comparatively analyzes performance differences between mechanically preloaded and unconstrained batteries during external short circuits, quantitatively investigating dynamic trends and safety boundaries of electro-thermo-mechanical signals during short circuits in fully charged (100% SOC) batteries across preloads of 500~3500 N. Key findings indicate that under the 50C external short-circuit (ESC) condition, mechanical constraint significantly reduces the central peak temperature of the 100% SOC battery, with a measured reduction of 31.6 °C. Moreover, constrained cells exhibit well-defined lamellar graphite structures, unlike the surface cracking observed in unconstrained anodes, confirming enhanced safety. Rupture temperatures consistently ranged between 112.00 and 124.00 °C across all conditions, with stable temperature rise rates (~0.5 °C·s−1) during short circuits indicating minimal preload impact on heat generation, though excessively high or low preloads accelerated physical damage. Further SOC investigations (10%~100%) demonstrate that lower SOC increases temperature rise rates due to polarization-induced resistance rise, resulting in shorter discharge durations with lower peak temperatures/swelling forces without leakage, while high-SOC cells exhibit prolonged discharge, yielding higher peak temperatures/swelling forces at rupture. This study provides critical insights for enhancing process safety in lithium battery energy storage systems. These findings collectively guide safer battery pack design, module constraint strategies and emergency response protocols to reduce cascading failure risks in stationary energy storage applications.

Batteries doi: 10.3390/batteries12070233

Authors: Muzamil Hussain Wadho Alessandro Serpi Mario Porru

Hybrid Energy Storage Systems (HESSs) have emerged as an inevitable solution in modern power systems and transport electrification. An HESS combines two or more complementary storage technologies—such as Batteries (BTs) with Supercapacitors (SCs), or BTs with thermal or mechanical energy storage, etc., to leverage their virtues. The robustness of HESS configurations is of utmost importance for exploring failure analysis and resilience approaches in BT-SC-based HESSs, which are crucial for long-term reliability, safety, and contributions towards future decarbonization goals. Hence, based on this motivation, this work focuses on the study of conventional and advanced HESS configurations, together with a method of configuration selection. Subsequently, the review aims to obtain a systematic identification, characterization, and understanding of the reasons behind HESS failures. This paper thus defines what HESS failures are and their possible mitigations, discussing many state-of-the-art research studies that may help researchers in finding correct and updated literature content concerning this research area. Finally, future trends and developments in BT-SC-based HESSs are discussed.

Batteries doi: 10.3390/batteries12070234

Authors: Chang Liu Zhifeng Zheng Guodong Xu

Lithium-ion batteries are the core energy storage devices for electric vehicles, and accurate state of charge (SOC) estimation is critical to ensuring their safe and reliable operation. Most existing SOC estimation methods are only suitable for constant-temperature scenarios and cannot adapt to the dynamic temperature variations in actual charging and discharging processes. To address the issue of insufficient estimation accuracy under complex conditions such as high and low temperatures, this study proposes a Window Attention Sinks Transformer (WASFormer) model. Based on the PatchTST framework, the model integrates Rotary Positional Encoding (RoPE) and Window Attention Sinks (WAS) mechanisms, and combines Huber Loss with Reversible Instance Normalization (RevIN) to establish a full-chain robustness enhancement scheme from feature preprocessing to loss optimization, which effectively suppresses the interference of noise and distribution shift on estimation stability. Comparative experiments, generalization tests, and ablation studies under various temperatures and working conditions show that the proposed model achieves higher estimation accuracy, stronger generalization ability, and robustness. It provides an effective and stable new approach for high-precision SOC estimation of lithium-ion batteries over a wide temperature range and under complex operating conditions.

Batteries doi: 10.3390/batteries12070232

Authors: Chao Chen Lifeng Deng Hao Li Jing Zhou

Accurate prediction of remaining useful life (RUL) for lithium-ion batteries remains a critical yet complex challenge due to highly non-linear degradation dynamics and profound data heterogeneity across varying operational profiles. While convolutional neural networks (CNNs) have shown promise in battery health management, traditional architectures struggle with gradient vanishing in deep feature spaces and lack the adaptive capacity to filter early-cycle noise under diverse degradation conditions. To improve robust RUL estimation across heterogeneous benchmark datasets, this paper proposes a deep learning framework that integrates residual connections with dual-attention mechanisms (ResCNN). Specifically, the residual structures effectively mitigate gradient degradation during the extraction of abstract degradation patterns. Concurrently, a synergistic Squeeze-and-Excitation (SE) and Multi-Head Attention module adaptively calibrates channel-wise feature importance and captures long-range temporal dependencies inherent in complex capacity fade processes. The proposed framework is evaluated under a wide spectrum of degradation conditions and distinct cathode systems (LFP and LCO) using both dataset-specific train/validation/test protocols and strict source-to-target cross-dataset transfer tests. Experimental results demonstrate that ResCNN achieves consistently lower prediction errors than baseline models across the evaluated datasets and maintains positive explanatory power on unseen target datasets without target-domain training. Ablation studies further validate the synergistic contribution of each architectural component toward capturing intrinsic battery aging phenomena.

Batteries doi: 10.3390/batteries12070231

Authors: Alexey Tsyganov

Recently, aqueous asymmetric supercapacitors (ASCs) have attracted considerable attention as safe and high-power energy storage devices. However, achieving high energy density while maintaining long-term cycling stability remains a significant challenge. Herein, an aqueous ASC employing a Mo1.33CTx/CNT negative electrode and a hydrated V2O5·nH2O/CNT positive electrode in a 5 M LiCl electrolyte is reported. The Mo1.33CTx i-MXene was synthesized via hydrothermal selective etching of an i-MAX precursor, whereas hydrated V2O5·nH2O nanoflakes were prepared with peroxide-assisted hydrothermal treatment. The ordered-vacancy Mo1.33CTx i-MXene provides a stable negative potential window, redox-active sites, and favorable conditions for reversible Li+ intercalation/deintercalation, thereby contributing to pseudocapacitive charge storage. The assembled ASC delivered a stable operating voltage of 1.7 V, a specific capacitance of 61 F·g−1 at 1 A·g−1, an energy density of 25.2 Wh·kg−1 at 883 W·kg−1 and 86% capacitance retention after 10,000 cycles. Electrochemical impedance spectroscopy revealed relatively low internal resistance and efficient ion transport within the layered electrode architectures. These results highlight the strong potential of ordered-vacancy MXene/vanadium oxide systems for advanced aqueous energy storage applications.

Batteries doi: 10.3390/batteries12070230

Authors: Luhang Yuan Suzhen Liu Yulin Ma Shibo Shang Zhicheng Xu Liang Jin

State of Health (SOH) serves as a key metric in assessing the performance of lithium-ion batteries. It is challenging for a single sensing signal to fully characterize the multi-physics evolution characteristics during battery degradation, which limits the accuracy and robustness of SOH estimates. Therefore, a lithium-ion battery SOH estimate method combining electrical and ultrasonic features with a frequency-enhanced decomposed Transformer (FEDformer) is proposed. To begin with, a multi-condition battery aging dataset is constructed through experiments, comprising electrical and ultrasonic signal data from 7828 cycles. Subsequently, 20 electrical and ultrasonic features are extracted from multiple perspectives, and 12 strongly correlated features are selected via the Spearman correlation coefficient. Finally, the FEDformer is employed to establish the SOH estimate model, where the accuracy, robustness, and generalization of the SOH estimates are comparatively analyzed across different input features, models, and cross-aging conditions. The results demonstrate that, compared to using electrical features alone, the combined ultrasonic features improve the estimation performance by more than 40% on average. Furthermore, in the cross-aging datasets, the mean absolute error and root mean square error of the SOH estimates are 0.52% and 0.63%, respectively, validating the robustness and generalization capability of the proposed method.

Batteries doi: 10.3390/batteries12070229

Authors: Yanbo Jia Chaohui Shi Lei Zhang An Tao Sen Hu Huang Li

In order to investigate the suppression effect of different extinguishing agents on lithium-ion battery fires in real confined spaces, a comparative experiment was conducted using aerosols, heptafluoropropane, and perfluorohexanone. In tests without any fire suppression measures, the peak heat release rate reached up to 69.09 kW, and a total of 8.05 MJ of heat was generated along with multiple deflagration events. Moreover, the heptafluoropropane and perfluorohexanone both effectively extinguished the flames with extinguishing times of 12 and 20 s, respectively. The aerosol agent caused a significant contraction of the flames, but it was unable to achieve complete extinguishment. Regarding cooling performance, the heptafluoropropane decreased the front surface temperature of the battery by 147 °C, while perfluorohexanone achieved a reduction of 230 °C. Additionally, the liquid-phase adhesion characteristics of perfluorohexanone enabled sustained cooling. A comprehensive comparison indicates that the perfluorohexanone agent exhibits outstanding performance in flame extinguishment, cooling efficiency, and the suppression of thermal propagation. Heptafluoropropane demonstrates rapid fire suppression and is suitable as a fast-response agent, whereas the aerosol requires a multi-discharge design to achieve reliable performance. Based on these findings, it is recommended that energy storage systems adopt a composite suppression strategy for fire protection.

Batteries doi: 10.3390/batteries12070228

Authors: Tobias G. Bergmann Xinyang Liu-Théato Binbin Zhu Lea Leuthner

Metallic particle contaminants have been shown to have a detrimental effect on the reliability, performance and capacity of lithium-ion battery cells. In addition, they pose a significant safety risk. Typical contaminants, such as iron (Fe), copper (Cu) and aluminium (Al), often enter the cell via mechanical abrasion from production equipment, as burrs during electrode cutting, or through environmental exposure during handling. In such instances, the degradation mechanisms are known to accelerate, dendrite formation is increased, and, in the most unfavourable circumstances, thermal runaway is the likely outcome. Contaminants that do not affect cell behavior during formation and the initial cycles, yet only compromise safety at a subsequent stage, are of particular concern. Affected cells are known to pass end-of-line testing and make their way into the market as latent safety risks. Consequently, there is an urgent requirement for non-destructive diagnostic methods that are capable of identifying latent defects. The issue under discussion is approached in the present paper through the utilization of an innovative methodology that integrates the distribution of relaxation time (DRT) analysis of electrochemical impedance spectroscopy (EIS) data with machine learning techniques. The objective of this integrated approach is to facilitate the detection of critically contaminated pouch cells with a high degree of sensitivity.

Batteries doi: 10.3390/batteries12070227

Authors: Xinhai Li Wei Lin Wei Hou Zhiying Ding

In this study, we investigate the effect of aging on the thermal runaway characteristics of 314 Ah lithium iron phosphate batteries with different cycles (0, 400, and 1000 cycles), with the batteries being overcharged to thermal runaway with a 0.5 C charging rate. The results indicate that aging significantly reduces the severity of thermal runaway for a battery. Fresh batteries exhibited intense jet fires with a peak temperature of 501.4 °C, while aged batteries produced only heavy smoke without obvious flames, with peak temperatures dropping to 401.2 °C. Aging leads to the thickening of the SEI film, increased internal resistance, and an unstable voltage response, extending the thermal runaway trigger time from 1979 s to 4039 s, but with a lower trigger temperature. The negative tab consistently remained the core heat accumulation point, with temperature differences of 10–30 °C compared to other wall surfaces, and the core temperature during thermal runaway exceeded 500 °C. The transition from casing rupture to jet fire occurred within only 2 s, indicating an extremely short safety response window. Through this research, we provide critical insights for the aging assessment and thermal safety management of energy storage batteries.

Batteries doi: 10.3390/batteries12070226

Authors: Sotir Sotirov Nadezhda Kafadarova

This study presents a novel and accessible method for determining the specific heat capacity of cylindrical lithium-ion batteries without the need for specialized equipment for cell disassembly, climatic chambers, or expensive calorimeters. The proposed approach does not require disassembly of the cell. Since specific heat capacity is a key parameter in thermal modeling and is often unavailable in manufacturer datasheets, the method addresses an important practical gap. The measurement principle is based on recording the change in surface temperature caused by a short 30 s discharge pulse of 9 A. A thermographic camera captures infrared images at fixed time intervals, while an electromechanical module rotates the battery around its longitudinal axis, providing an accurate estimation of the average surface temperature during and after the pulse. The resulting temperature–time profiles are used to evaluate heat losses and compute the specific heat capacity. To validate the methodology, experiments were conducted on an aluminum cylinder of identical dimensions to an 18650 cell, made of Al 6082-T6 (Cp ≈ 896 J·kg−1·K−1). The results show a maximum deviation of 2.68% from the reference value, confirming the reliability of the proposed method for determining the specific heat capacity of cylindrical Li-ion batteries.

Batteries doi: 10.3390/batteries12070225

Authors: Dongqi Gu Yanfang Liang

Rechargeable zinc-based batteries (ZBBs) have attracted considerable attention for use in large-scale energy storage systems due to their inherent high safety, low cost, and environmental friendliness. However, the practical applicability of ZBBs is limited by challenges related to the anode—such as uncontrollable zinc dendritic growth, the hydrogen evolution reaction (HER), and corrosion—which lead to significant polarization, capacity degradation, and unsatisfactory Coulombic efficiency of the ZBBs. Polyacrylamide (PAM)-based hydrogels have emerged as promising electrolyte materials to address these challenges due to their superior mechanical properties, flexibility, high ionic conductivity, and structural designability. Considering the rapid increase in research attention regarding this topic, we comprehensively summarize recent progress in PAM-based hydrogels as electrolytes for ZBBs in this study. First, we discuss the key challenges associated with Zn anodes in ZBBs, together with corresponding optimization strategies. Next, we detail the fundamental structure, properties, and synthesis of PAM-based hydrogels. Then, the relationships among synthetic methods, nano/microstructures, and electrochemical properties are systematically reviewed and discussed. Finally, prospects for the rational design and application of PAM-based hydrogels in ZBBs are summarized.

Batteries doi: 10.3390/batteries12060224

Authors: Xingzhen Zhou Chenhui Gao Weige Zhang Caiping Zhang Qinhe Huang Lei Zhang Yusheng Li Ling Chen Dongzhong Hu Jinhan Qiu

Accurate prediction of external short-circuit (ESC) current is important for battery safety analysis and protection design, but conventional equivalent circuit models have difficulty reproducing the strongly nonlinear current evolution under ESC conditions. This study proposes a reaction-process-corrected second-order RC model for ESC current prediction, based on ESC experiments on a 37 Ah commercial NCM pouch cell at different initial SOCs. The ESC process is described by three successive stages: bottleneck control, concentration-difference control, and separator pore closure. To represent the transport-related resistance deviation during this process, an additional correction resistance Rx and a queued-charge descriptor Q are introduced into the equivalent circuit framework. A segmented closed-loop simulation strategy is then developed to update Rx and predict the ESC current. Using the 50% SOC case as an unseen validation case, the proposed model captures the main nonlinear characteristics of ESC current, including rapid initial decay, secondary rebound, and subsequent attenuation. The proposed framework improves the physical interpretability of equivalent-circuit-based ESC simulation while retaining engineering simplicity, providing a practical approach for safety-boundary assessment and protection-oriented battery system design.

Batteries doi: 10.3390/batteries12060223

Authors: Masaharu Nakayama Wataru Yoshida Yasuhiro Shioji

Aqueous zinc–manganese dioxide (Zn–MnO2) batteries are undergoing a paradigm shift from traditional ion-insertion mechanisms to a reversible deposition/dissolution process. By leveraging a two-electron transfer (Mn2+/MnO2), this electrolytic system achieves a high theoretical capacity of 616 mAh g−1 and a theoretical operating voltage of 1.99 V. However, the accumulation of dead Mn, electrically isolated inactive phases, and dynamic interfacial pH fluctuations remain critical barriers to cycle life and practical energy density. This review systematizes a trinitarian strategy to overcome these bottlenecks, focusing on interfacial engineering, redox mediator-assisted recovery, and advanced electrode architectures. We evaluate how anion engineering and pH-buffering stabilize reaction pathways, and how diverse mediators (e.g., halogens, metal ions, and organic molecules) chemically rescue inactive manganese. Furthermore, we examine the integration of 3D carbon networks and low-cost hybrid electrodes to sustain high-areal-capacity deposition. To elucidate these complex mechanisms, we highlight multiscale analytical approaches combining synchrotron X-ray techniques and density functional theory (DFT). Finally, we outline a roadmap for applications ranging from grid-scale flow batteries to flexible wearable electronics. This work provides a comprehensive perspective on realizing sustainable, safe, and high-performance zinc-based energy storage.

Batteries doi: 10.3390/batteries12060222

Authors: Florian Wätzold Anton Schlösser Sven Beger Daniela Schröder Julia Kowal

This study investigates the temperature-dependence performance of high-power lithium iron phosphate (LFP) cells for automotive starter batteries. Temperature effects on high-power LFP cells are contextualised based on pertinent literature in order to compare the typical capacity behaviour of lead–acid batteries with LFP. Experiments were conducted on five cylindrical LFP cell types in a thermal chamber across ambient temperatures from +45 °C to −30 °C using a 9 C discharge regime aligned with automotive standards. Electrical and thermal behaviours were analysed, including energy yield, power output, and surface temperature monitored by sensors and thermal imaging for room temperature. Energy output decreased exponentially with temperature but remained above 70% for most LFP cells at −18 °C, while only one cell type was functional at −30 °C. Thermal analysis at ambient temperature confirmed homogeneous temperature distribution without hotspots and low overall heating (from 2 °C to 14 °C), indicating no need for additional cooling for starter battery applications. A conservative power analysis indicated that 4 kW at −30 °C would require a 28P4S 26650 configuration, representing a lower-bound estimate. We argue that even this conservative figure suggests a potential for weight reduction compared with lead–acid systems. Energy-based Pb-equivalence factors of approximately 1.2 at −18 °C and 3 at −30 °C were derived. A preliminary guideline for cell dimensioning based on measurements at 25 °C is proposed to address discrepancies between data sheet specifications and actual performance for pack configuration based on required power.

Batteries doi: 10.3390/batteries12060221

Authors: Mateusz Sztuka Mohammad Musameh Asma Ali Nicholas Richardson Alessandro Di Nuovo Walid Issa

Cell degradation in second-life battery packs introduces heterogeneous capacity and internal resistance mismatch, reducing the effectiveness of conventional balancing approaches and limiting available pack runtime. Although equal state of charge (SoC) does not necessarily imply equal usable capacity, SoC-based control remains attractive for runtime-oriented operation. This paper proposes a target-mean controller for heterogeneous reconfigurable battery packs under constant-bus constraints that aims to improve runtime and achieve the cutoff-defined theoretical maximum capacity utilization limit. Using only real-time cell SoC measurements and legal switching actions, the controller selects the configuration that best reduces deviation from the pack-average SoC while preferentially loading cells above the mean. The online action selection requires no active balancing hardware, no explicit capacity or state of health (SoH) estimation, and no offline optimization; experimentally measured capacities are used only for calibrated Coulomb-counting SoC estimation. Simulation results on a heterogeneous five-cell reconfigurable battery pack show that the proposed controller reaches the cutoff-defined 90% theoretical utilization limit in the full-initial-SoC cases, while also extending runtime and reducing switching activity by up to 11.75% relative to the comparison methods. Hardware validation on a five-cell prototype further confirms this trend, achieving 89.12% experimental utilization, zero final SoC spread, and higher delivered energy than both comparison methods. A stepped-load hardware test further achieved 88.19% utilization from current integration, corresponding to 97.99% of the cutoff-defined 90% theoretical limit. The results suggest that, for heterogeneous second-life packs, SoC-based reconfiguration control can achieve both runtime improvement and near-maximum utilization without the added complexity of explicit SoH-aware balancing.

Batteries doi: 10.3390/batteries12060220

Authors: SeYi Kwon Jun-Ki Lee

Silicon (Si) is a leading anode candidate for next-generation lithium-ion batteries owing to its high theoretical capacity (~4200 mAh/g), but its >300% volumetric expansion during lithiation causes particle pulverization, loss of electrical contact, and continuous solid electrolyte interphase (SEI) reformation, resulting in rapid capacity fade. Here, we report a single-walled carbon nanotube (SWNT)-templated porous Si/SiO2 core–shell cylindrical hybrid anode synthesized by combining block copolymer-directed sol–gel assembly with controlled magnesiothermic reduction. SWNT bundles act as a three-dimensional structural template that directs the formation of a continuously interconnected cylindrical porous network, a geometry difficult to obtain by conventional particle-based compositing. The controlled, partial magnesiothermic reduction intentionally preserves residual amorphous SiO2 within the porous shell as an electrochemically inactive mechanical buffer that suppresses Si volume expansion and stabilizes the electrode. A side-by-side comparison with a fully reduced, SiO2-free counterpart of identical architecture isolates the role of the SiO2 buffer in achieving long-term cycling stability. The SWNT-porous Si/SiO2 hybrid delivers a reversible capacity of 1133 mAh/g in the first cycle and retains 90% of its initial capacity after 200 cycles at 1 C with 99.7% Coulombic efficiency, together with a rate capability of 482 mAh/g at 5 C. Post-cycling cross-sectional analysis confirms minimal electrode-level swelling (~2 μm) after 200 cycles, demonstrating the structural efficacy of the SWNT-templated porous architecture combined with the SiO2 buffer for structurally stable Si anodes.

Batteries doi: 10.3390/batteries12060219

Authors: Pengfei Yan Fang Wang Tianyi Ma Liduo Chen Gaiyun He Liqiong Han Zhipeng Sun

As the global automotive industry is transitioning toward sustainable development, new energy vehicles (NEVs) have experienced rapid global growth due to their environmental friendliness and high efficiency. Global sales of NEVs are projected to reach 50 million units by 2030. Nevertheless, safety incidents caused by impacts on traction batteries remain a major factor restricting the development of NEVs. Prismatic batteries, which account for over 90% of the traction battery market owing to their high energy density and structural robustness, nevertheless continue to face significant safety challenges under mechanical loading conditions. Typical failure modes involve structural damage induced by external compressive forces during severe vehicular collisions, which can subsequently result in the tearing of internal electrode layers and rupture of the separator, thereby initiating internal short circuits and leading to severe incidents. Accordingly, this research focuses on the mechanism of structural damage transmission for prismatic lithium-ion batteries under compression conditions. By integrating a refined mechanical model, it further elucidates the structural failure mechanisms and conducts a microscopic analysis of the damaged battery structure to investigate the effects of varying damage levels on battery safety performance, providing significant guidance for the safety and reliability of new energy vehicles.

Batteries doi: 10.3390/batteries12060218

Authors: Apurba Ray Neil Wood Emre Guney Bilal Tasdemir Kamil Burak Dermenci Maitane Berecibar Bilge Saruhan

To date, lithium–ion batteries (LIBs) are considered one of the most promising and market-leading energy storage systems due to their high theoretical capacity and energy density. However, poor thermal and cyclic stability, low electrolyte uptake, and the possibility for frequent short circuits of typical separators and evolution of several gases during long cycle operation pose several problems for LIBs. Metal-organic frameworks (MOFs) have attracted widespread interest as a promising material for improving the cycle stability and safety of rechargeable batteries due to their inherent surface and structural properties such as high specific surface area, high porosity, and ionic conductivity. In this work, the aim is to provide detailed descriptions of the synthesis routes and parameters for obtaining various MOFs such as Zr-MOF-808 and Ni-MOF-74 nanoparticles and the fabrication of those MOF-integrated separators. To optimize the crystallinity, morphological and compositional characteristics, and several material characterizations such as XRD, SEM, and EDX have been applied. Afterwards, the synthesized MOF-integrated glass fiber (GF) separators have been developed for lithium–ion battery (LIB) applications. To investigate the electrochemical performance and the effect of MOF integration into the separators, electrochemical studies in the form of galvanostatic charge–discharge (GCD), electrochemical impedance spectroscopy (EIS) have been evaluated by preparing CR2032-type half-coin cells. This MOFs-integrated GF-separators and synthesized LiNi0.6Mn0.2Co0.2O2 (NMC622) cathode materials-based coin cell LIB exhibited higher cycle stability than bare GF-separator based LIB. This novel approach and extensive research suggest that development of MOF-integrated separators could significantly improve cycle stability by reducing the internal cell degradation for next generation energy storage devices.

Batteries doi: 10.3390/batteries12060217

Authors: John Sherman Anthony Bombik

Lithium-ion batteries (LIBs) are subject to mechanical abuse both in electric vehicles and consumer electronic applications when dropped, which can lead to capacity degradation even if the cells survive the impact. This study investigates the impact of mechanical damage on the electrochemical performance of LIBs, focusing on capacity retention and internal resistance changes. The batteries were subjected to dynamic mechanical impact using varying impact energies (3J, 5J, and 7J) while measuring internal resistance and capacity before and after the impact. Hybrid Pulse Power Characterization (HPPC) was employed to assess internal resistance and capacity degradation across multiple cycles. Our results demonstrate that even minor mechanical damage can cause significant performance decay, especially after several cycles. The study also reveals that the state of charge (SOC) prior to impact has a minimal effect on the survival rate of the cells but influences the extent of damage observed. Post-impact analysis using optical microscopy indicates structural damage, including separator tears and delamination, contributing to capacity fade. This work highlights the importance of considering intermediate mechanical damage in LIB safety and performance assessments.

Batteries doi: 10.3390/batteries12060216

Authors: Lennart Kuhr Sajedeh Haghi Matthias Leeb Alexander Schoo Mark Mennenga Arno Kwade Rüdiger Daub Christoph Herrmann

The battery cell production, a cornerstone of the net-zero vision, is a multifaceted process chain involving diverse processes, spanning from batch to continuous to single-unit steps. The quality of the battery cell as the final product is affected by various product and process parameters along this process chain. In the era of Industry 4.0, data-driven approaches have emerged as a promising solution to navigate these complexities and derive effective quality management practices. A key prerequisite for the successful implementation is the availability of accurate data. A tracking and tracing system in battery cell production provides the foundation to acquire such data. It supports the development of a digital twin of the product, enabling real-time monitoring of key performance indicators, in-line quality control, resource optimization, and compliance fulfillment, among others. This article presents an implementation methodology and discusses the key aspects to consider for upscaling such a system focusing on data management, including relevant parameters, data acquisition, and storage, as well as data structuring and mapping. It highlights the advantages of using ontology-based data descriptions, enabling semantically mapped production environments. Lastly, this article explores potential use cases facilitated by a traceability system, emphasizing its potential to realize intelligent, data-driven production.

Batteries doi: 10.3390/batteries12060215

Authors: Jose Fernando Florez Gomez Songyang Chang Irfan Ullah Juan C. Velez Reyes Lisandro Cunci Gerardo Morell Xianyong Wu

The zinc–copper (Zn-Cu) voltaic battery is the first battery made in human history, but the Cu2+ dissolution issue leads to the reaction’s irreversibility. To tackle this challenge, solid-state electrolytes, ion exchange membranes, and functional electrolytes have been proposed to mitigate the Cu2+ dissolution; however, these approaches incur limitations like cell complexity, high cost, and anode corrosion. Herein, we develop a simple yet effective strategy to mitigate Cu2+ dissolution and build a rechargeable voltaic battery from cost-effective materials, including commercially available micro-copper powders and non-corrosive zinc acetate electrolyte. Importantly, the near-neutral Zn(Ac)2 electrolyte provides some amounts of hydroxide and facilitates the Cu2O/Cu solid–solid conversion reaction, thereby inhibiting the generation of soluble Cu2+ ions. As a result, the Zn-Cu battery exhibits a reversible capacity of ~130 mAh g−1, a feasible voltage of 0.87 V, and a stable cycling life over 100 cycles. Our work provides a feasible strategy for developing rechargeable and cost-effective Zn-Cu batteries.

Batteries doi: 10.3390/batteries12060214

Authors: Ote Amuta Julia Kowal

The adoption of lithium-ion batteries (LIBs) as secondary rechargeable batteries across many industries, including consumer electronics, electromobility, industrial tools, and electrical energy storage, is on the rise. As lithium-ion batteries approach the end of their life, there is a need to assess them for the possibility of a secondary application or reuse for a less demanding application. The extra connections of individual cells, BMS, temperature sensors, and other components to form a compact battery pack pose a challenge for second-life assessment, which usually prefers to separate individual cells for testing before discarding very bad cells for recycling and grading cells with substantive capacity based on their remaining capacity. This is a high cost for the second-life assessment. This work seeks to investigate an approach that avoids dismantling the battery pack into individual modules, cells, and BMS by including a BMS feature that allows the capacity and power ratings to be rescaled onboard after its first use. A set of cells with different chemistries was used in this work: a nickel–cobalt–aluminium oxide cathode with a silicon-doped graphite anode (NCA-GS), a nickel–cobalt–aluminium oxide cathode and graphite, and a lithium–nickel–manganese–cobalt oxide (NMC) cathode with a graphite anode (NMC-G) with various ageing states and behaviours. Their internal resistance and capacity at the beginning and end of life were compared. The scaling factor was obtained by finding the square root of the ratio of the internal resistance at EOL to that at BOL. With the current obtained by multiplying the cycling current rate by the rescaling factor, the surface temperature profile of the aged cells during cycling became the same as the temperature at the beginning of life. The relaxation voltage after discharge to 0% SOC and charge to 100% SOC was used to set the low and high cut-off voltages, respectively. This contributed significantly to reduced ageing and to a lower temperature rise in the spent cells. This set the stage for rescaling or derating battery systems without separating the individual cells, which is a huge cost for second-life use of lithium-ion batteries. BMS can be designed with configurable voltage and current limits, so that when repurposed for a second life, only a simple configuration or firmware update may be necessary.

Batteries doi: 10.3390/batteries12060213

Authors: Bin Ma Yajin Liu Dongyang Ma Guoliang Liu Changjian Ji Bosong Zou

In electric vehicles, power batteries consist of numerous individual cells connected in series or parallel. Variations in manufacturing, operating conditions, and aging can lead to differences among these cells. Such inconsistencies can compromise the battery pack’s performance, safety, and overall service life. Therefore, accurately diagnosing inconsistencies among battery cells is of great significance for enhancing the reliability of the battery system and ensuring the operational safety of the vehicle. To address the limited computational resources available in vehicles, this paper proposes an end-cloud collaborative fault diagnosis framework and validates its effectiveness using real-world vehicle driving data. On the cloud side, a deep learning-based reconstruction network is developed to enable high-precision reconstruction of cell voltages. On the vehicle side, a second-order equivalent circuit model is used to represent battery dynamics. An adaptive forgetting factor recursive least squares method is introduced for online estimation of the model parameters, enabling accurate local prediction of individual cell voltages. Using the cloud-reconstructed and vehicle-predicted cell voltages, the extreme difference value of voltage for each cell is computed. A comprehensive diagnosis of inconsistency faults is then performed by fusing the extreme difference in voltage results from both the cloud and vehicle sides via the Extended Kalman Filter (EKF); threshold judgment is conducted based on the fused results, and the Cumulative Sum (CUSUM) algorithm is designed to identify cell inconsistency faults. Experimental results show that the proposed method effectively detects battery inconsistency faults and demonstrates strong engineering applicability and practical potential.

Batteries doi: 10.3390/batteries12060212

Authors: Tarek Ibrahim Muhammad Usman Tahir Mohamed Abdel-Monem Erik Schaltz Vaclav Knap Daniel Ioan Stroe Tamas Kerekes

Real-time monitoring of lithium-ion capacitors (LICs) is crucial for ensuring reliability and predictive maintenance in dynamic applications such as electric transportation. However, traditional electrochemical impedance spectroscopy (EIS) techniques are complex and costly for onboard diagnostics due to their reliance on external excitation signals and dedicated hardware. Therefore, this paper presents an innovative framework for online state of health (SOH) estimation that bypasses these limitations by utilizing fast Fourier transform (FFT)-based passive impedance extraction directly from operational current and voltage signals. From experimental data, the equivalent circuit model (ECM) is developed, as well as its parameters, such as ohmic resistance, charge-transfer resistance, and Warburg diffusion. These parameters are identified through the extraction of impedance points in the low frequency region through FFT and the series resistance point using ohmic measurement, then performing a periodic curve fitting to these points. These curve fittings provide extracted ECM parameters. These parameters are used with a trained model to estimate the SOH of the monitored cell and are updated online. The proposed method was experimentally validated on five LIC cells aged under various C-rates (1C, 4C, 7C) and temperatures (35 °C, 40 °C, 50 °C), showing consistent impedance evolution with capacity fade. Validation of the utilized machine learning models, such as Polynomial Regression (PR), principal components analysis (PCA), and random forest (RF) regression, achieved SOH prediction errors as low as 2.23% compared to experimental results. The developed framework is particularly suitable for applications such as flash-charged electric buses but is broadly applicable across other energy storage systems as well. This advanced method enables real-time diagnostics without hardware modification, offering significant potential for integration into existing battery management systems (BMSs).

Batteries doi: 10.3390/batteries12060211

Authors: Long Ying Shanglong Xiao Yulong Zhang Jianquan Xu Jieliang Fan Jiashen Lin

Lithium-ion batteries are prone to internal short-circuits and subsequent thermal runaway under compression and impact loads during electric vehicle crashes, posing a critical safety challenge for the industry. However, existing studies lack systematic comparative analysis between quasi-static and dynamic loading conditions. In this study, ternary pouch lithium-ion batteries were used as research objects. A test platform for the synchronous acquisition of mechanical load, electrical voltage and thermal temperature was established. Quasi-static compression and drop-weight impact tests were conducted to investigate the effects of indenter diameter, impact velocity and state of charge (SOC) on the multiphysics responses of batteries. The results show significant differences in failure modes between the two loading conditions: quasi-static loading causes progressive plastic deformation and stable short-circuit voltage decay, while dynamic loading is likely to induce brittle shear fracture and soft short-circuit voltage rebound. Dynamic loading reduces peak load by 61.5% and raises peak temperature by up to 46.7%, while also decreasing failure displacement and advancing time-to-peak. Additionally, a high SOC (50% and 100%) alters the heat-release pathway during thermal runaway, leading to deviations in surface temperature measurements. These findings provide critical experimental support for the crash safety design of power batteries and the formulation of thermal runaway prevention and control strategies.

Batteries doi: 10.3390/batteries12060210

Authors: Nur Mohamed Mohamud Shahrin Md Ayob Siti Mahfuza Saimon Ahmed M. Nahhas Zeeshan Ahmad Arfeen Muhammad I. Masud Mohammed Aman

The accurate estimation of lithium-ion battery state of health (SOH) is crucial for battery monitoring, safety, and degradation assessment; however, it remains challenging because of the nonlinear nature of battery degradation, measurement noise, and variability in the battery aging trajectory. This study aims to solve these problems by proposing a hybrid attention-based BiLSTM–RF model, which combines wavelet-based signal denoising, incremental capacity analysis (ICA)-based feature extraction, stacked Bidirectional Long Short-Term Memory (BiLSTM) networks, multi-head self-attention, principal component analysis (PCA)-based feature compression, and ensemble regression using a Random Forest (RF) model with adaptive weighted fusion. The proposed framework was tested on the NASA battery datasets (B0005, B0006, B0007 and B0018) and was further validated on the Oxford Battery Degradation Dataset using leave-one-battery-out cross validation conditions. Experimental results indicated that, in general, the proposed framework outperformed the evaluated benchmark models (CNN-LSTM, BiLSTM, and RF models) in terms of the prediction error, with a minimum RMSE value of 0.0229 for NASA battery B0007 and 0.0024 for Oxford Cell3. Ablation analysis also showed that the combination of wavelet denoising, PCA compression, temporal sequence learning and ensemble regression played a role in the overall SOH estimation performance. These results show that the proposed hybrid approach is effective and stable for SOH estimation in different battery degradation trajectories under the tested experimental conditions.

Batteries doi: 10.3390/batteries12060209

Authors: Felix Mannerhagen Elena Simona Udrescu Erik Hultman Mats Leijon

This paper presents a battery cell joining solution leveraging adhesively bonded lithium-ion cells as a foundation for scalable, serviceable, and recyclable energy storage platforms. The proposed design methodology enables mechanically and electrically functional connections and supports a design concept intended for compatibility with automated manufacturing and future robotic disassembly. A123 26650-format cells were tested using Epo-Tek 430 conductive adhesive, with performance evaluated through ESR and G-force measurement experiments. The results indicate that no measurable change in electrical performance was observed within the resolution of the measurement system, while supporting a design concept intended to improve modularity and serviceability. The proposed system shows potential for further investigation in electric vehicle and industrial energy system applications, although further validation under realistic operating conditions is required.

Batteries doi: 10.3390/batteries12060208

Authors: Aleksandar Mijajlović Dušan Mladenović Kristina Radinović David Tomić Ana Nastasić Dalibor Stanković Jadranka Milikić

The oxygen reduction reaction (ORR) is a key process in electrochemical energy conversion technologies such as fuel cells and metal–air batteries; however, its sluggish kinetics and reliance on precious metal catalysts limit large-scale application. This review provides a comprehensive overview of recent advances in non-precious nanoscale electrocatalysts for ORR in alkaline media. Particular emphasis is placed on reaction mechanisms, including dominant pathways, kinetics, and key intermediates, as well as the advantages of alkaline electrolytes over acidic systems. The performance of various catalyst classes is systematically discussed, including transition metal-based materials (Fe, Co, Zn, Cu, and bimetallic systems) and metal-free carbon-based electrocatalysts. Special attention is given to heteroatom-doped carbon materials, carbon nanostructures, and emerging hybrid systems such as MXene-based composites. Comparative analysis highlights the relationship between catalyst composition, structure, and electrochemical performance metrics, including half-wave potential, onset potential, Tafel slope, number of electron transfer, and operational stability. Overall, non-precious catalysts demonstrate promising activity and durability, approaching that of noble metals under alkaline conditions. The insights summarized in this review guide the rational design of efficient, cost-effective ORR electrocatalysts and support the development of sustainable energy technologies.

Batteries doi: 10.3390/batteries12060207

Authors: Han Tian Mingfei Yang Shanhua Zhang

To address the thermal management challenges of electric vehicle power batteries under complex operating conditions, this study proposes a biomimetic honeycomb-shaped liquid cooling plate and conducts a systematic analysis of its cooling performance along with machine learning-based prediction for CTP lithium iron phosphate battery packs. A fluid–solid coupling numerical model was developed using ANSYS Fluent, employing the control variable method to investigate the effects of coolant flow rate (0.2–4.2 m/s), coolant inlet temperature (5–32 °C), ambient temperature (15–39 °C), and battery heating power (1000–5500 W/m3) on the maximum battery temperature. Simulation results demonstrate that the honeycomb structure leverages its hexagonal channel geometry and large specific surface area to achieve rapid and uniform heat transfer, with no localized hot spots observed across all operating conditions. The maximum battery temperature exhibits a marginal decreasing trend as coolant flow rate increases, with 1.4 m/s approaching the optimal flow rate; it rises approximately linearly with elevated inlet temperature, ambient temperature, and heating power—each 3 °C increase in inlet or ambient temperature raises the maximum temperature by approximately 1.98 °C and 3 °C, respectively, while a 500 W/m3 increase in heating power corresponds to an approximately 2.8 °C rise. Under standard conditions (heating power: 3000 W/m3; inlet temperature ≤23 °C; ambient temperature ≤27 °C), the maximum battery temperature remains below 45 °C; high-heating (≥3500 W/m3) or high-temperature (≥30 °C) scenarios require coordinated control strategies. Furthermore, based on simulation data, seven machine learning models—BPNN, GA-BP, PSO-BP, SVM, RBFNN, RF, and LSTM—were constructed and evaluated for their performance in predicting the maximum temperature of battery packs. The results showed that the LSTM model achieved the highest prediction accuracy on the validation set, with RMSE, MAE, MAPE, and R2 values of 0.8068, 0.6891, 1.5653%, and 0.9865, respectively, while models such as SVM and RBFNN exhibited severe overfitting. This study validated the engineering effectiveness of the honeycomb structure liquid cooling plate and identified LSTM as the optimal model for predicting battery pack maximum temperature, providing a theoretical foundation and data support for the structural design and intelligent control of power battery thermal management systems.

Batteries doi: 10.3390/batteries12060206

Authors: Hector Daniel Lema Chicaiza Alexander Aguila Téllez

This study presents an Ant Colony Optimization (ACO)-based methodology for the optimal placement of lithium-ion battery energy storage systems (BESSs) in radial electrical distribution networks. The proposed framework integrates base-case power-flow assessment, critical-bus identification, discrete BESS siting, technical–economic objective evaluation, and post-optimization validation. The methodology is applied to the IEEE 33-bus radial distribution test system, where the initial operating condition is characterized in terms of nodal voltage profile, voltage deviation, voltage-stability index, active-power losses, and annual loss cost. The optimization process identifies buses 13 and 31 as the most suitable locations for two identical BESS units, with the reported validation case evaluating each unit at upper admissible capacity limits of 1000kW and 4000kWh. The obtained results show that the optimized BESS allocation increases the minimum voltage profile to values above 0.94p.u., raises the voltage-stability index to more than 0.88, reduces active-power losses to approximately 0.0166p.u., and decreases the annual cost associated with active-power losses by more than 66% relative to the base case. Additional validation through sensitivity analysis, repeated stochastic runs, operating-mode evaluation, and comparison against a genetic algorithm confirms the consistency and robustness of the proposed ACO-based methodology. The results demonstrate that the proposed framework provides a technically consistent and computationally accessible solution for improving voltage regulation, reducing feeder losses, and lowering loss-related operating costs in radial distribution systems.

Batteries doi: 10.3390/batteries12060205

Authors: José E. Arevalo-Fester Magnus Larsson Sofia Öiseth Jonas Löfvendahl Mykhailo Zhybak Erik Khranovskyy Martina Petranikova

Recovered graphite from spent lithium-ion batteries is an important secondary resource that can reduce reliance on primary graphite and lower the environmental footprint of battery production. In this work, graphite obtained as a carbon-rich residue after industrial hydrometallurgical leaching of NMC111 black mass (2 M H2SO4 + 3% H2O2) is subjected to three post-leaching washing treatments to assess how far simple, low-intensity steps can further clean the leach residue while preserving the carbon structure. The washing routes are water washing (GW), water washing with ultrasonication (GU) and mild sulfuric-acid washing with 0.1 M H2SO4 (GA). ICP-OES and SEM–EDX show that, relative to the leached black mass, all washing treatments reduce residual transition-metal contents by two to three orders of magnitude, and that the mild acid wash provides the lowest bulk metal levels, with several elements at or below detection limits. X-ray diffraction and Raman spectroscopy indicate graphite-dominated patterns and improved structural order, with the ID/IG ratio decreasing from 0.62 (GW) to 0.11 (GA) and the corresponding in-plane crystallite size increasing from 30.6 nm to 168 nm. Overall, the mild acid washing step is the most effective low-impact post-leaching purification route, yielding a thoroughly cleaned low-metal graphite fraction that preserves the graphite framework and constitutes a suitable intermediate for further upgrading or reuse in secondary applications.

Batteries doi: 10.3390/batteries12060204

Authors: Jie Hu Haowen Zu Yaran Zhao Siyu Zhao Te Ma Libo Zhang Yulong Zhang Hongwentao Yu Yalun Li

As lithium-ion batteries are widely deployed, thermal runaway (TR) poses severe safety risks, making early and accurate warning systems critical. While machine learning (ML) has advanced data-driven TR prediction, challenges remain regarding model interpretability, generalization under unseen conditions, and real-time deployment. This review evaluates recent progress in ML-driven TR warning technologies, moving beyond a mere compilation of algorithms to provide an organized synthesis of the field. As a key contribution, we critically analyze the paradigm shift toward physics-informed ML, demonstrating how embedding electrochemical and thermodynamic principles into neural networks reduces prediction errors by 40–60% while enhancing robustness. Furthermore, we synthesize a Battery Digital Twin (BDT) framework integrating Internet of Things (IoT), cloud computing, and on-board master BMS for closed-loop collaboration, effectively balancing low-latency control with high-precision health assessment. Finally, we outline strategic pathways for future breakthroughs: advancing physics-informed cross-scale modeling, optimizing cloud-edge architectures, and establishing open access benchmark databases. By calling for standardized evaluation protocols to break down data silos, this review provides a comprehensive roadmap and actionable insights to accelerate the industrial implementation of next-generation intelligent battery safety management.

Batteries doi: 10.3390/batteries12060203

Authors: Klara Midander Anneli Julander Erik Rosengren Sandra Johannesson Florencia Harari

Achieving the EU’s climate goals by 2050 requires a rapid transition to a resource-efficient and circular economy. The electrification of transport increases the demand for rechargeable lithium-ion batteries (LiBs), where lithium–nickel–cobalt–manganese (Li-NMC) is the predominant cathode technology in the European automotive sector. Large-scale facilities for LiB production and recycling are emerging worldwide, bringing not only technical challenges but also challenges regarding healthy and safe working environments. Current knowledge on occupational exposure and health risks in the LiB industry is limited and largely based on evidence from other occupational settings. However, the LiB industry involves legacy and new combinations of metals and chemicals in novel contexts. Some of these substances have well-known adverse health effects, and combined exposure may increase their absorption and toxicity. Although processes are often highly specialised and automated, manual handling tasks remain, which put workers at risk of exposure. Important knowledge gaps remain regarding exposure levels, exposure pathways, dermal and systemic uptake, combined exposures, and potential health effects among workers. This perspective paper discusses current exposure scenarios and health risks in LiB production and recycling, identifies key knowledge gaps, and highlights future research needs to support evidence-based occupational risk management. To address several of these challenges, the GreenWorkLiB initiative applies a multidisciplinary approach combining exposure assessment, biomonitoring, and occupational medicine. The initiative investigates exposure pathways via air and skin, internal dose through biomonitoring, and potential health effects among workers in LiB production and recycling. The results can support the assessment of human health and safety within the EU’s Safe and Sustainable by Design (SSbD) framework and contribute to safe and sustainable working environments in the LiB industry.

Batteries doi: 10.3390/batteries12060202

Authors: Yao Li Rong Wang Yi Jin Zhenxin Sun Hui Liu Yu Liu Yanhui Liu Jiahuan Xu Ye Tao Zhaoyu Jiang Yue Ma Jiuchun Jiang

To address the issue of thermal runaway in ternary lithium-ion batteries under overcharging conditions, this paper establishes a multi-physics simulation model based on electrochemical–thermal coupling theory to systematically investigate the thermal behavior and runaway mechanisms of the battery. A P2D electrochemical model and the Bernardi heat generation model were combined to construct an electrochemical–thermal coupling model suitable for overcharging conditions. Simulation results indicate that under normal charging conditions, the battery temperature rise is small and uniformly distributed; however, under overcharging conditions, side reactions significantly intensify, leading to a rapid increase in heat generation. The battery temperature exhibits a distinct inflection point and rises rapidly, displaying typical thermal runaway characteristics. Charging rate and ambient temperature have a significant impact on the thermal runaway process; both high charging rates and high ambient temperatures accelerate heat accumulation and reduce battery thermal safety. The study demonstrates that the established model effectively reveals the evolution of thermal runaway in overcharged ternary lithium-ion batteries, providing a theoretical basis for battery thermal management design and safety early warning systems.

Batteries doi: 10.3390/batteries12060201

Authors: Elif Kaya Alessandro d’Adamo

Thermal management is critical for the safety, performance, and life cycle of lithium-ion (Li-ion) batteries. This study aims to determine the optimum settings and contribution levels of key parameters affecting the operating temperature of a three-dimensional (3D) thermal model of a cylindrical Li-ion battery. A Taguchi L9 orthogonal array was designed with four: (A) base fluid and (B) Al2O3volume fraction (Φ-Al2O3) of the nanofluid coolant, (C) battery–battery distance, and (D) inlet temperature (Tinlet), each varied on 3-level control factors. To minimize the maximum battery temperature (Tmax), the “smaller-is-better” signal-to-noise (S/N) ratio approach and Analysis of Variance (ANOVA) were applied. The S/N analysis and ANOVA revealed that the base fluid (A: 44.96%) and Tinlet (D: 36.00%) were the most dominant factors influencing the Tmax. The optimal design identified by the Taguchi method (A3-B3-C3-D1) successfully reduced the Tmax to 33.5 °C, a 29.0 °C reduction compared with the initial air-cooled reference model (62.5 °C). Furthermore, the maximum temperature rise during the 2100 s operation was reduced by approximately 62%. This optimal Tmax of 33.5 °C was even lower than the best result in the L9 array (35.5 °C), validating the strong predictive capability of the method.

Batteries doi: 10.3390/batteries12060200

Authors: Rouz Barjoud Veronika Moiseja Davis Gavars Margarita Volkova Artis Kons Jana Andzane

This work presents advanced binder-free self-assembling Zn nanowire anodes synthesized by an easy-to-handle one-step low-pressure physical vapor deposition method. The morphology and structure of zinc nanowire networks are controlled and altered by the substrate temperature during deposition. Electrochemical performance of two types of Zn nanowire network samples of different morphology is studied in alkaline and mildly acidic aqueous electrolytes using cyclic voltammetry and electrochemical impedance spectroscopy techniques and compared to that of Zn foil electrodes. It is found that the morphology and structure of the Zn nanowire electrodes are directly related to their electrochemical performance and can be tuned for the type and concentration of the electrolyte to reach optimal electrochemical performance. The resulting binder-free self-assembled Zn nanowire anodes significantly outperform traditional Zn-based electrodes in both mild acidic and alkaline electrolytes, showing an areal capacitance of ~3.3 F/cm2 and 3.5 F/cm2 for acidic and alkaline electrolytes, respectively, and stability up to 1000 h of cycling in mild acidic electrolytes. These findings provide a pathway to fabricate and optimize binder-free zinc anodes for a variety of efficient and long-lasting aqueous zinc-based batteries and supercapacitors.

Batteries doi: 10.3390/batteries12060199

Authors: Pierpaolo Dini Davide Paolini

The accurate estimation of State of Charge (SOC) and Remaining Useful Life (RUL) is a key challenge in lithium-ion battery management systems, due to the nonlinear, time-varying, and multi-physics nature of battery dynamics. This work presents a systematic comparative study for SOC and RUL estimation based on the analysis of the NASA battery dataset, characterized by significant heterogeneity in operating conditions, temperature regimes, and cycle durations. The study combines a physically informed feature engineering process with machine learning models, including tree-based ensembles, kernel methods, and neural networks. The dataset is analyzed from an electrochemical, thermal, and impedance perspective, highlighting the role of internal resistance evolution, SOC–voltage characteristics, and temperature dynamics as indicators of battery degradation. Based on these observations, two regression problems are formulated: a local window-based representation for SOC estimation and a cycle-level representation for RUL prediction. Particular attention is devoted to the impact of dataset heterogeneity, feature construction, and target representation on the predictive behavior of the considered models. In addition, the work investigates the effect of normalized RUL representations and provides an interpretability-oriented comparison of the learned regressors through feature-importance analysis and parity plots. Experimental results show that SOC estimation is a comparatively well-conditioned problem, achieving high accuracy across nonlinear models, although the dominant role of temporal and current-derived features highlights the strong dependence of the prediction task on the structure of the experimental protocol. In contrast, RUL prediction exhibits significantly higher complexity due to long-term degradation uncertainty and inter-battery variability. The introduction of a normalized RUL representation substantially improves prediction accuracy and stability, particularly for ensemble-based approaches. Feature importance analysis confirms that capacity-related variables dominate RUL estimation, while voltage, temporal, and current-derived features play a central role in SOC prediction. Overall, the results show that physically interpretable feature construction combined with ensemble learning methods provides an effective framework for battery state estimation and degradation analysis under heterogeneous operating conditions.

Batteries doi: 10.3390/batteries12060198

Authors: Chuhan Yang Boyang Gu Xudong Li Xinke Zhang Xuejun Zhang

Smartphone battery drain is governed by coupled effects of workload, electrochemical aging, and thermal feedback. Nonlinear behaviors such as voltage collapse remain challenging for traditional models. An electrochemical-informed equivalent-circuit and lumped-thermal continuous-time framework is developed by integrating an equivalent-circuit voltage model with lumped thermal dynamics, aging-aware resistance and capacity evolution, driven by a modular decomposition of smartphone power into CPU load, screen power, network power and base power. Time-to-empty (TTE) is defined using the practical voltage collapse rather than the SOC to zero assumption. The model is assessed via local and global sensitivity analysis, and power-saving strategies are derived using an AHP multi-criteria decision-making framework. The SOC fitting quality reaches R2=0.979, and rank-correlation-based importance analysis identifies CPU-related workload factors as the dominant contributor to endurance variation, with a normalized importance score of approximately 40%. The model is evaluated using a train/test-separated validation protocol rather than relying only on fitting quality. Prediction errors are reported separately for SOC, terminal voltage, temperature, and voltage-cutoff-defined TTE. On the unseen test segments, the proposed model achieves SOC RMSE of 0.0402, terminal-voltage RMSE of 0.162 V, temperature RMSE of 1.954 K, and TTE RMSE of 0.34 h under the controlled simulation-based validation setting. These findings support strategies that prioritize CPU-load reduction and usage-aware control, and motivate voltage-collapse-aware power management for heavy workloads and aged batteries. Overall, the main message of this work is that reliable smartphone TTE prediction requires voltage-collapse-aware modeling rather than SOC-only extrapolation.

Batteries doi: 10.3390/batteries12060197

Authors: Nasim Saber Runar Unnthorsson Christiaan Petrus Richter

Second-life lithium-ion batteries offer strong potential for sustainable stationary energy storage, but their practical reuse is limited by cell-to-cell heterogeneity, non-uniform heat-generation, and the resulting thermal safety risks. Conventional battery thermal management systems (BTMSs), which rely on fixed and uniformly distributed coolant flow, are not well-suited to the asymmetric thermal behaviour of aged battery packs. In this study, an adaptive liquid-cooling framework with locally regulated branch-level flow allocation is proposed for second-life prismatic LiFePO4 battery modules. A three-dimensional transient conjugate heat transfer model was developed in COMSOL Multiphysics. The analysis was conducted on a 3 × 3 battery module under nine thermal heterogeneity scenarios, followed by a larger 5 × 4 module to evaluate scalability. The results show that thermal severity depends not only on heat-generation magnitude but also on the spatial arrangement of degraded cells. Under the most critical 3 × 3 configuration, the adaptive BTMS reduced the maximum temperature from 37.16 °C to 28.77 °C, corresponding to a reduction of about 8.38 °C, while limiting the cell-to-cell temperature difference to approximately 1.16 °C. A comparison with a conventional constant-flow cooling configuration in the larger 5 × 4 module further showed that adaptive branch-level coolant redistribution improves thermal uniformity under heterogeneous thermal loading by selectively directing cooling capacity toward thermally stressed regions. The results demonstrate the potential of demand-driven flow allocation as a distributed thermal-management strategy for heterogeneous second-life battery systems.

Batteries doi: 10.3390/batteries12060196

Authors: Yawei Meng Qiang Sun Jianping Xu Antai Bian Qizheng Yang Zhi Wang Zijian Yang Maoyong Zhi

Existing deep learning methods for lithium-ion battery State of Health (SOH) prediction rely almost exclusively on discharge voltage–current curves, ignoring electrochemical impedance spectroscopy (EIS) data that directly reflects internal degradation mechanisms. Fusing these two modalities is non-trivial: discharge curves are high-dimensional temporal sequences residing on a continuous dynamical manifold, while impedance features are low-dimensional static snapshots with fundamentally different statistical distributions. However, naive concatenation introduces modal conflicts rather than complementary gains. We propose the Hybrid Sensing Synergy Architecture (HSSA), which combines a Mamba backbone (O(L) complexity) for discharge curve modeling with a Q-former module that aligns impedance features into the temporal representation space via learnable query tokens and cross-attention. A prepend fusion strategy injects the aligned queries as prefix tokens, enabling the backbone to condition on internal electrochemical context from the first time step. On the NASA battery dataset, HSSA achieves MAE of 0.887 (large-scale, 11 batteries, a 9.8% improvement over unimodal Mamba), 1.457 (medium-scale, five batteries, a 28.0% improvement), and 2.705 (small-scale, four batteries, an 8.7% improvement), demonstrating consistent improvements across all data regimes. On out-of-sample battery B28, HSSA achieves 65.3% improvement. Ablation studies confirm that Q-former alignment is essential and prepend fusion significantly outperforms concatenation-based alternatives.

Batteries doi: 10.3390/batteries12060195

Authors: Kai Cao Hao Zheng Xu Wu Yuqi Ding Ye Lu

The extensive utilization of LiFePO4 (LFP) batteries in energy storage facilities has been impeded by the inherent property of thermal runaway (TR). This study examines the TR propagation characteristics of 280 Ah LFP batteries and their module through the application of dual-side heating to trigger TR. Experimental investigations on single battery TR reveal that the timing and temperature at which the battery safety valve opens exhibit stochastic behavior. Moreover, a correlation is observed between the time required for the safety valve to open and the average surface temperature of the battery, with longer durations corresponding to higher temperatures. Surface temperature variations in batteries manifest in three primary phenomena: temperature decline, abrupt temperature spikes, and peak temperatures. In TR experiments involving packs, it is depicted that temperature signals can detect internal development processes earlier than smoke signals when TR initiates within the module. Heat transfer within batteries of the same sub-module primarily occurs through conduction, exhibiting an average heat transfer fraction of 25.8%. These findings hold significant implications for enhancing early detection systems for TR in both batteries and modules.

Batteries doi: 10.3390/batteries12060194

Authors: Wen Li Penghui Zhu Wilhelm Pfleging

Over the past few years, laser structuring of electrodes has been shown as a powerful tool to significantly improve the rate capability and cycling ability of lithium-ion batteries. However, the impact of anode/cathode pattern combinations on electrochemical performance in full-cell configurations remains poorly understood. This work investigated for the first time the influence of laser structuring strategies and pattern combinations on the laser processing rate as well as the electrochemical performance of full cells containing NMC 811 cathodes and graphite anodes. Meanwhile, the mass losses due to laser ablation with different strategies were kept similar for cathodes and anodes. The line-patterning process exhibited a processing rate that was an order of magnitude higher than that for blind hole drilling. Moreover, line patterning of graphite anodes with an average laser power of 5.0 W showed a two to five times higher laser processing rate than with 2.5 W. Subsequently, the structured electrodes were cross-combined and assembled into full cells. All cells with laser-structured electrodes exhibited improved rate performance, reduced ionic resistance, and a shift in the onset of lithium plating to higher C-rates in comparison to the reference cells with unstructured electrodes. In particular, the cells with “Line 5 W” electrodes demonstrated excellent rate performance, delivering an increase of 72 mAh g−1 in discharge capacity compared to the reference cells at 5C and achieving 80% state of charge in 18 min. The results indicated that line patterns enhance rate performance more effectively than hole patterns. Furthermore, wider grooves in the electrodes were produced using higher average laser power, which may provide larger electrolyte reservoirs. This could support the rewetting processes of the electrolyte in the electrodes during electrochemical cycling and thus significantly improve rate performance and cell lifetime.

Batteries doi: 10.3390/batteries12060193

Authors: Matthew Claassen Bjoern Bingham Joseph Ammatelli Judith C. Chow John G. Watson Yan Wang Xiaoliang Wang

Lithium-ion batteries (LIBs) release significant amounts of toxic, corrosive, and flammable gases when they enter thermal runaway (TR). These emissions can be hazardous to human health, damage nearby equipment, pose fire and explosion risks, and degrade air quality. This study measured concentrations for a range of hazardous gases released from TR-driven combustion of cylindrical lithium iron phosphate (LFP) and pouch-style lithium cobalt oxide (LCO) LIB cells. Gas emissions were measured by dedicated analyzers and Fourier transform infrared spectroscopic (FTIR) analysis, and emission factors were calculated. Dangerous concentrations of hydrogen fluoride (HF) were observed, reaching up to 50 ppm from the combustion of single LIB cells. Large amounts of combustible electrolyte solvents and light hydrocarbons were released in some cases, depending on cell combustion behavior. Electrolyte solvents, hydrogen chloride (HCl), and particles were released earlier than other species and should be targeted for early TR detection. Gas emissions were correlated with cell state of charge (SOC) and combustion behavior. Cells at high SOCs had higher peak concentrations of HF, HCl, CO, and flammable hydrocarbons, and these peaks happened sooner after cell failure than for low-SOC tests.

Batteries doi: 10.3390/batteries12060192

Authors: Shuyi Wang Leyan Zhang Zichuan Ni Lei Li

Reliable estimation of battery remaining useful life (RUL) becomes difficult when the capacity trajectory contains regenerative rebounds, short-term oscillations, and long-range temporal dependence. To address this problem, an adaptive decomposition and hybrid deep-learning framework is proposed. First, the phototropic growth algorithm (PGA) is used to tune variational mode decomposition (VMD), allowing the capacity series to be separated into low-frequency trend information and high-frequency fluctuation information so that the influence of regeneration and noise is weakened. Next, a component-level predictor combining a temporal convolutional network (TCN), an attention mechanism (AM), and a Transformer is constructed. In this architecture, TCN learns multi-scale local features, AM enhances salient degradation cues, and the Transformer captures global long-horizon dependencies. To deduce the future capacity degradation path and the associated RUL, these estimated elements are synthesized. Results on the NASA, CALCE, and BIT datasets verify the effectiveness of the proposed framework. On NASA dataset, the average root mean square error (RMSE), mean absolute error (MAE), and absolute error (AE) reach 0.0123 Ah, 0.0073 Ah, and 0.5 cycles, respectively, improving on the strongest baseline by 11.9%, 19.7%, and 50.0%. On CALCE dataset, the corresponding values are 0.00695 Ah, 0.00499 Ah, and 1.75 cycles, and all R2 values are higher than 0.9989, indicating strong accuracy and robustness in the presence of complex regeneration behavior. Supplementary BIT validation on three higher-capacity cells further achieves average RMSE, MAE, and AE of 0.01201 Ah, 0.00771 Ah, and 1.0 cycle, respectively.

Batteries doi: 10.3390/batteries12060191

Authors: Liang Tong Chengfu Xie Hanwei Xu Linzhi Xu Min Liu Lingyu Chen Qianqian Xin Tianqi Yang Hengyun Zhang Jinsheng Xiao

With the widespread application of lithium-ion battery energy storage stations, thermal runaway (TR) of energy storage batteries has evolved into a safety issue that cannot be overlooked. To prevent the propagation of thermal runaway, this study proposes a thermal runaway protection strategy for prismatic battery modules based on the sodium acetate trihydrate-expanded graphite (SAT-EG), aerogel felt (AEGF) and liquid cooling. The study also investigates the impact of factors such as the thickness of the SAT-EG, the thickness of the AEGF, and the area of the AEGF on the protection performance. The results show that compared with the conventional paraffin-expanded graphite (PA-EG), SAT-EG can block the propagation of thermal runaway, but the maximum temperature of adjacent batteries still approaches T2 (T2 denotes the battery thermal runaway triggering temperature). After introducing AEGF to form a sandwich structure, the maximum temperature of adjacent batteries can be effectively controlled below T1 (T1 denotes the temperature at which heat generation from battery side reactions intensifies). However, the utilization rate of SAT-EG is relatively low, and the thermal runaway trigger time of the thermal runaway battery is advanced. By reducing the AEGF area, the overall utilization rate of SAT-EG can be effectively improved, and the thermal runaway trigger time of the thermal runaway battery can be significantly delayed, gaining time for the detection and handling of thermal runaway and ensuring the safety of energy storage power stations.

Batteries doi: 10.3390/batteries12060190

Authors: Junzheng Li Shiladitya Paul

The need for the rapid advancement of lithium-based energy storage technologies continues to outpace progress in materials development and manufacturing, creating a widening gap between laboratory-scale innovation and industrial deployment. There is a need to examine the key materials and processing challenges that limit the performance, cost-effectiveness, and sustainability of next-generation lithium batteries. For material considerations, many commonly used electrodes face issues of volumetric expansion and performance degradation over charging cycles. To address these issues, binders are a crucial component to consider as they adhere active materials to the electrodes, and their structure can be altered to mitigate undesirable effects from these components. Hence, the selection and exploration of alternative binders are becoming increasingly important in the pursuit of longer-lasting and safer Li-batteries. From a manufacturing perspective, current production lines rely on multistep, energy-intensive processes, e.g., from slurry-mixing to cell assembly, that elevate costs and complicate scale-up. Emerging chemistries incorporating nanomaterials or solid-state components face additional barriers related to yield, process control, and defect management, all of which can exacerbate safety risks related to processing during production and thermal runaway in produced batteries. End-of-life considerations, including disassembly, recycling, and the safe handling of toxic materials, further contribute to the technological and logistical complexity of large-scale deployment. The field is moving toward sustainable material alternatives, more efficient and adaptive manufacturing routes, and advanced technologies such as solid-state electrolytes and nanostructured electrodes. Together, these developments provide a roadmap for overcoming current bottlenecks and enabling the next generation of high-performance, safe, and sustainable lithium battery technologies. This review examines the progress made in finding alternative materials and synthesis methods for the optimization of lithium battery cells, with a focus on the development of novel binders, slurry synthesis and manufacturing framework. In addition, the advantages and limitations of the alternative binder materials and processes are also explored, with a focus on scalability for manufacturing, safety concerns, sustainability and end-of-life challenges.

Batteries doi: 10.3390/batteries12060189

Authors: Zhengchen Liu Yan Wang Ping Gao Hangyu Luo Tao Cai Gen Su Zhanqiang Wang Yuxin Meng

Accurate estimation of internal battery temperature is critical for the safety and state-of-health assessment of lithium-ion batteries, yet it remains challenging due to the trade-off between model accuracy and computational feasibility on resource-constrained edge hardware. This work targets stationary large-scale battery energy storage stations (BESS), where ambient temperatures are actively regulated within a narrow range (typically 15–35 °C), and is developed and validated on large-format prismatic LFP cells. We propose ThermaPhysLite, a lightweight physics-informed neural network (PINN) framework with three innovations: (i) a lightweight PINN architecture tailored for edge devices; (ii) integration of a simplified electro–thermal model—a lumped-parameter thermal circuit coupled with the Bernardi heat generation equation—into a multi-scale temporal convolutional network (MS-TCN) through the PINN paradigm; and (iii) real-time online deployment on the ESP32-S3 embedded platform. Ground-truth internal temperatures were obtained via side-drilled thermocouple embedding in disassembled cells. Offline validation under three operating conditions demonstrates RMSE values of 0.15–0.20 °C. Following INT8 quantization (compressed to 84.29 KB), online deployment yields RMSE values of 0.17–0.24 °C with single-cell inference latency of 120 ms, demonstrating practical viability for BMS in large-scale energy storage systems.

Batteries doi: 10.3390/batteries12060188

Authors: Ruqi Guo Qian Yu Hao Li Zilong Pu Mingzhi Jiao

As new energy vehicles and energy storage systems become more common, safety accidents caused by lithium-ion batteries overheating have become more of a concern. Early detection based on distinctive gases (such as H2 and CO) can give an earlier warning than typical monitoring methods like temperature, voltage, or impedance. Nonetheless, attaining high-precision identification in intricate mixed-gas settings continues to be difficult because of the considerable cross-sensitivity of metal oxide semiconductor (MOS) gas sensors. This research presents an ISA-LSTM-TCN multi-task learning model utilizing an enhanced spatial attention mechanism for the swift identification and concentration forecasting of distinctive gases during lithium-ion battery thermal runaway. The model improves key feature extraction and anti-noise performance by combining the long-term temporal modeling ability of the Long Short-Term Memory (LSTM) network with the multi-scale feature extraction ability of the Temporal Convolutional Network (TCN). It also adds an Improved Spatial Attention (ISA) module with a residual multiplication structure. Moreover, in a multi-task learning framework, joint optimization of gas categorization and concentration regression is facilitated using a hard parameter-sharing method. Tests using a built MOS sensor array dataset show that the model is 99.23% accurate at classifying gases and that the R2 values for predicting H2 and CO concentrations are 0.9510 and 0.8400, respectively. Tests on public datasets and in different noisy environments show that the model is even better at generalizing and is more robust. The results show that the suggested method allows for quick, accurate detection of thermal runaway gases. This makes it an effective and smart way to monitor battery safety warning systems.

Batteries doi: 10.3390/batteries12060187

Authors: Yonggang Wang Kai Cui Haoran Chen

Accurate estimation of the state of health (SOH) and prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) are critical for ensuring system reliability and safety across diverse energy storage applications. This paper proposes a hybrid deep learning framework that integrates an axial-embedding Transformer (AxEmbTrans) encoder and a bidirectional LSTM (BiLSTM) module for the joint estimation of SOH and RUL. The AxEmbTrans encoder employs axial attention with abstract embeddings to capture global dependencies among multidimensional health features at reduced computational complexity compared to standard self-attention, while the BiLSTM models local temporal dynamics and short-term degradation fluctuations across consecutive cycles, with its bidirectional structure enhancing robustness against transient noise. Informative health features are extracted from charge–discharge curves, grouped into temporal, energy, and thermal categories, and fused using local linear embedding (LLE) for nonlinear dimensionality reduction. An improved Newton–Raphson-based optimizer (INRBO) is introduced to automatically tune the framework’s key hyperparameters, including the hidden dimension, number of attention heads, number of BiLSTM units, and learning rate, incorporating directional similarity modulation and multi-elite guidance to overcome the convergence instability of the standard NRBO. Extensive experiments on NASA and Maryland datasets demonstrate that the proposed method consistently outperforms baselines in both SOH and RUL prediction, achieving higher accuracy, improved robustness, and better cross-condition generalization.

Batteries doi: 10.3390/batteries12060186

Authors: Alberta Carella Francesco Rossella Claudio Fontanesi

The chiral-induced spin selectivity (CISS) effect enables the spin-selective transport of electrons through chiral systems, linking handedness with spin polarization. This review provides a comprehensive examination of the emerging field of chiral electrocatalysis, detailing also the extensive experimental and theoretical endeavor conducted to gain a deeper understanding of the fundamental physical principles and mechanistic characteristics of this phenomenon. In particular, the CISS effect has garnered significant attention within the scientific community due to its potential for broad applicability across several fields, ranging from spintronics to biology. Among them, the prospective harnessing of the CISS effect into electrocatalytic processes offers an innovative strategy to improve the performance of energy conversion and storage technologies. This review deeply examines the practical applications of the CISS effect across different electrocatalytic reactions, with particular emphasis on its influence on the oxygen reduction reaction (ORR) and its critical role in energy conversion systems where the ORR reaction is a key process, such as in metal–air batteries, whose safety and performance can be enhanced through spin-selective electron transport.

Batteries doi: 10.3390/batteries12060185

Authors: Montaha Khedhiri Rim Slama Eduardo Redondo-Iglesias Rochdi Trigui

Recently, numerous approaches have been proposed to improve State of Charge (SoC) prediction, demonstrating the potential of deep learning (DL) techniques for accurate battery state estimation. However, most of these methods are validated on laboratory-controlled or synthetic datasets and do not sufficiently consider real-world battery operating conditions. In practice, batteries operate under highly diverse usage patterns, environmental conditions, and user profiles, which can significantly affect SoC estimation accuracy. In this paper, we address this limitation by leveraging real-world data, which contains measurements from vehicle batteries under heterogeneous user behaviors and operating scenarios. The proposed methodology includes a data cleaning and filtering preprocessing stage, followed by an original DL framework designed to evaluate SoC estimation under different learning conditions. The framework is data driven and built upon a TimerV2-based architecture capable of capturing long-term temporal dependencies and nonlinear relationships in battery signals. Furthermore, transfer learning strategies are explored to enhance adaptability across different battery configurations and datasets for efficient knowledge transfer. Extensive experiments show that the proposed approach achieves high estimation accuracy and strong generalization performance, demonstrating its suitability for reliable real-time SoC estimation in practical battery management systems.

Batteries doi: 10.3390/batteries12050184

Authors: Xinghuan Xia Chaohui Shi An Tao Lei Zhang Sen Hu Keshang Jiang Huang Li

A thermal abuse model of a single lithium-ion battery, coupling the electric–chemical reaction model and heat transfer model condition, is presented in this work to predict the battery’s thermal response. This model was validated by the experimental results, and it was found that it can predict the battery’s thermal runaway in adiabatic conditions well. It was found that a local hot spot is formed first on the cell nearest the air gap inside the battery. A thermal runaway propagation model was constructed based on this thermal abuse model of a single battery. In addition, the effect of four different modes on the mitigation of thermal runaway propagation is also discussed, including the air gap, cooling plate and insulation layer. The thermal runaway propagation event is successfully prevented when the aerogel is placed between adjacent batteries. However, low-thermal-conductivity insulation material has a negative effect on the heat sink of the battery in thermal runaway, which may aggravate this behavior. This study demonstrates that the model can be used to predict thermal runaway propagation event in battery modules with different prevention measures, and also contributes to the design of safe lithium-ion battery systems.

Batteries doi: 10.3390/batteries12050183

Authors: Tien Dung Nguyen Dong Nguyen Trong Duong Do Dinh Hoan Vu Yeong-Hwa Chang Bao Viet Le

This study proposes two enhanced configurations for a parallel-channel cold plate in battery thermal management systems to improve thermo-hydraulic performance through the introduction of cylindrical and fin-type baffles. A three-dimensional computational fluid dynamics (CFD) model was developed in ANSYS to simulate fluid flow and heat transfer within the cold plate. A Poly-Hexcore meshing strategy with local refinement and near-wall inflation layers was employed to ensure numerical accuracy while maintaining computational efficiency. A parametric investigation involving 150 cases was conducted to identify the optimal channel configuration. The results indicate that, among the investigated configurations and under the present numerical operating conditions, the fin-type baffle exhibits the most balanced thermo-hydraulic behavior by achieving an effective balance between heat-transfer enhancement and pressure-drop penalty. The present study provides a CFD-based framework for the design and optimization of parallel-channel cold plates for battery thermal management applications.

Batteries doi: 10.3390/batteries12050182

Authors: Woongchul Choi Younggill Son Jiwon Kwon

Accurate battery state-of-charge (SOC) estimation is a core function of battery management systems (BMSs) for electric vehicles (EVs), as it directly affects energy management, safety, and reliability. However, battery aging significantly degrades the accuracy of conventional SOC estimation methods by causing capacity loss, increased internal resistance, and changes in voltage response characteristics. To address these issues, this study proposes an aging-aware SOC estimation method that combines an equivalent-circuit model (ECM) with an extended Kalman filter (EKF). In the proposed framework, aging effects are explicitly incorporated by using offline-identified SOH-dependent model parameters, including effective capacity, RC parameters, and SOC–OCV characteristics, and scheduling these parameters within the EKF prediction and correction process according to the available SOH information. Furthermore, the performance of the proposed method is experimentally validated under an Urban Dynamometer Driving Schedule (UDDS) using cylindrical lithium-ion cells with large current fluctuations. The experimental results demonstrate that the proposed aging-aware EKF maintains stable SOC estimation performance not only in the initial battery state but also throughout the gradual aging process and up to the end of battery life. These results demonstrate the potential of SOH-scheduled, aging-aware EKF-based SOC estimation to improve SOC accuracy in aged batteries under the investigated laboratory and dynamic load conditions.

Batteries doi: 10.3390/batteries12050181

Authors: Sascha Speer Christoph Terbrack Christian Endisch

Cascaded multilevel inverters constitute a promising system concept for battery electric powertrains due to their high efficiency, low harmonic distortion, and advanced battery management capabilities. This study presents a novel electro-thermal simulation framework for the symmetrical Parallel Enhanced Commutation Integrated Nested (PECIN) multilevel inverter. The proposed model employs a control-oriented approach that enables the development and evaluation of advanced inverter and battery control algorithms, which exploit the extensive series-parallel reconfiguration capabilities of the PECIN topology. The framework is based on electrical and thermal equivalent circuit models to capture physical behavior and cross-domain interactions. Electrical network analysis employs algorithms that iterate over each phase-arm network, replacing high-dimensional matrix inversions and thereby enhancing computational efficiency. The overall model is readily adaptable to various system configurations, including different AC and DC charging modes, and scalable with respect to the number of submodules and phases. Simulation results for a 31-level multilevel inverter in a three-phase AC charging configuration demonstrate the model’s operational capabilities. Execution time analysis shows that the current distribution calculation is the key contributor to computational effort as the number of submodules increases, resulting in a quadratic growth of the overall computational time.

Batteries doi: 10.3390/batteries12050180

Authors: Subin Antony Jose Blake Latos Alvaro Hurtado Jaylen Hurtado Jacob Jenkins Pradeep L. Menezes

Sodium-ion batteries (SIBs) are emerging as a viable alternative to lithium-ion batteries (LIBs) due to their material sustainability and cost-effectiveness, helping address the high costs, supply limits, and environmental concerns associated with lithium. This paper reviews SIB materials, designs, and applications, and surveys their electrochemical performance, challenges, and future prospects. Recent advances in electrode materials (e.g., layered oxides, hard carbon composites, metallic alloys) are greatly improving SIB stability, conductivity, capacity, and cycle life. Improvements in both solid-state and liquid electrolytes have likewise enhanced ionic conductivity, capacity retention, thermal stability, and safety. Despite their lower energy density, SIBs tolerate wider temperature ranges and carry a significantly lower risk of thermal runaway compared to lithium-based systems, making them attractive for industrial, transportation, and large-scale power storage. Continuous progress in materials and cell engineering is narrowing the performance gap between SIBs and LIBs. Meanwhile, nascent battery recycling strategies for SIBs show promise for economic and environmental viability. Overall, SIBs represent a promising option for safer, more accessible, and more sustainable energy storage technology.

Batteries doi: 10.3390/batteries12050179

Authors: Richard Polzer Eva Heider Christian Ellersdorfer Ute Golla-Schindler Carlos Antônio Rufino Júnior Sergej Diel Hans-Georg Schweiger

Electrochemical aging can alter the mechanical abuse tolerance of lithium-ion cells, but evidence for large-format tabless cylindrical cells remains limited. This study investigates commercial steel-cased 4695 cells under quasi-static hemispherical indentation at 100% State-of-Charge. Fresh cells were compared with two aged groups brought to 80% remaining capacity by either room-temperature cycling at 20 °C and 20 A or low-temperature/high-current cycling at −10 °C and 45 A. Aging shifted the onset of a major internal short circuit to lower displacement, force, and absorbed mechanical work. Relative to fresh cells, the mean displacement at internal short-circuit onset decreased by 15.9% after aging at 20 °C and 20 A and by 22.1% after aging at −10 °C and 45 A, while the corresponding force decreased by 10.8% and 19.4%. The absorbed mechanical work to short-circuit onset decreased by 24.0% and 37.3%, respectively. Peak surface temperatures did not clearly separate the groups, whereas integrating the surface-temperature rise over the first 30 s after short-circuit onset revealed an increase from 6894 Ks in fresh cells to 9872 Ks and 13,777 Ks in the two aged groups. The experiments also revealed a change in observed venting behavior, with top-terminal venting occurring more frequently in aged cells. These results indicate that, under the tested conditions, aging history can reduce mechanical abuse tolerance and modify post-failure severity in large-format cylindrical cells.

Batteries doi: 10.3390/batteries12050178

Authors: Narasimharao Kitchamsetti Ana L. F. de Barros Sungwook Mhin HyukSu Han

Prussian blue (PB) nanocubes have been explored as promising cathode materials for high-performance sodium-ion (SIBs) and lithium-ion batteries (LIBs). These nanostructures exhibit good cycling stability and electrochemical resilience. They are synthesized through a co-precipitation method followed by vacuum drying, resulting in a porous and conductive nanocube framework. This architecture facilitates efficient ion diffusion, enhanced electrolyte accessibility, and effective mitigation of volume changes during cycling. In SIB applications, the PB nanocubes maintain stable performance over 300 and 400 cycles at current densities of 0.05 and 0.1 A g−1, respectively, and deliver a capacity of 26.2 mAh g−1 at 2.0 A g−1. For LIBs, they exhibit sustained cycling over 200 and 300 cycles under similar conditions, with a capacity of 20.2 mAh g−1 at 2.0 A g−1. These findings underscore the structural benefits of PB nanocubes for dual-ion battery systems.

Batteries doi: 10.3390/batteries12050177

Authors: Johanna Montserrat Naranjo-Balseca Cynthia Susana Martínez-Cisneros Esperanza Batuecas Bidhan Pandit Belen Levenfeld Alejandro Varez Jean-Yves Sanchez

Solid-state sodium batteries based on polymer electrolytes offer a sustainable solution to overcome current and near-future needs regarding the growing energy and transport electrification issues. In this work, we propose the development of solvent-free polymer electrolytes based on an unsaturated polyether, which, once cross-linked, leads to an amorphous structure at room temperature that favors ionic transport towards reliable and robust solid-state sodium batteries operative at moderate temperatures. Using NaClO4 and NaPF6 as sodium salts, the best polymer electrolyte reaches an ionic conductivity in the range of 0.02 mS·cm−1 (30 °C)–0.90 mS·cm−1 (100 °C) with a lifetime superior to 2000 h after plating and stripping. Regarding electrochemical performance, a maximum specific capacity of 110.2 mAh·g−1 (C/20) is obtained for the polymer electrolyte including NaClO4, using Na and C/FePO4 as anode and cathode, respectively, which represents about 65% of the theoretical value expected for FePO4. In view of more sustainable energy storage devices, a life cycle assessment is also applied. While the polymer matrix is identified as the main environmental hotspot, the choice of Na salt significantly affects the overall impact, with NaClO4 exhibiting lower climate change and particulate matter impacts than NaPF6.

Batteries doi: 10.3390/batteries12050176

Authors: Yujie Sun Zigen Li Jingrong Tang Zishun Wang Jiaxue Dong Jing V. Wang

Accurately estimating the state of health (SOH) of lithium-ion batteries remains challenging for battery management systems. Traditional data-driven methods, such as long short-term memory (LSTM), lack physical interpretability and often fail to generalize across varying operating conditions. To address this, a physics-informed SP-LSTM framework is proposed that integrates the single particle model (SPM) with a bidirectional LSTM network. A hybrid optimization strategy combining particle swarm optimization and the limited-memory Broyden–Fletcher–Goldfarb–Shanno with bounds (L-BFGS-B) is first used to identify key SPM parameters, which are then combined with macro external features (charging time, discharge energy, IC peak) to form a seven-dimensional fusion vector. A dual-stream Bi-LSTM architecture separately models fast-varying macro trends and slow-varying physical parameters, achieving robust SOH mapping. Validated on the NASA PCoE dataset, the proposed SP-LSTM achieves a root mean square error (RMSE) of 0.0136 and a mean absolute error (MAE) of 0.0089 on an independent test set (B0018), outperforming the baseline LSTM by 38.2% in RMSE. Noise robustness tests (0–3% voltage noise) and Sobol global sensitivity analysis further confirm its stability and interpretability. By embedding electrochemical priors into the data-driven pipeline, this work provides a practical physics-data collaborative framework for accurate and trustworthy battery SOH estimation.

Batteries doi: 10.3390/batteries12050175

Authors: Matthieu Dubarry Alexa Fernando David Beck

Temperature affects the battery voltage response, and it is essential to take this influence into consideration for diagnosis purposes, as it could be misinterpreted for degradation. Temperature affects cell kinetics, and a good proxy to emulate this impact is to use electrode data at different C rates. This work further validates this concept by analyzing the relationship between temperature and rate at the electrode level for commercial graphite//LiFePO4 and (silicon, graphite)//LiNi0.8Mn0.1Co0.1O2 cells. It will be shown that excellent emulation accuracy for both the voltage response and the capacity retention can be obtained for temperatures varying between −14 °C and 55 °C.

Batteries doi: 10.3390/batteries12050174

Authors: Ren Zhu Hamza Shaukat Fatima Zahira Hafiz Muhammad Huzefa Muaaz Bin Kaleem Heng Li

The accurate estimation of State of Health (SoH) for lithium-ion batteries in real-world electric vehicles (EVs) is critical for ensuring safety, reliability, optimal energy management, and lifecycle sustainability. Unlike laboratory-controlled conditions, real-world EV batteries operate under highly dynamic loads, irregular charging behaviors, diverse environmental conditions, and user-dependent driving patterns. This review provides a comprehensive and structured overview of recent progress in SoH estimation for real-world EV applications. The fundamentals of battery aging mechanisms are summarized, with a clarification of key SoH definitions, metrics, and influencing factors under practical operating conditions. Subsequently, existing methodologies are systematically categorized into physics-based models, data-driven approaches, hybrid/model-assisted frameworks, and uncertainty-aware probabilistic methods, with a focus on their strengths and limitations in real-world deployment. Key challenges, including domain shift, computational constraints, explainability, thermal variability, and data heterogeneity, are critically and systematically analyzed. Finally, future research directions are outlined, emphasizing transfer learning, foundation models, physics-informed AI, self-supervised learning, digital twins, and the need for standardized benchmarks. This review aims to provide researchers and practitioners with a clear roadmap toward reliable, scalable, and trustworthy SoH estimation for next-generation intelligent battery management systems in electric vehicles.

Batteries doi: 10.3390/batteries12050173

Authors: Yue Wang Dakang Peng Peng Zhang Jianquan Liang Shilin Yang Hanwen An Jiajun Wang

The burgeoning commercial space sector demands next-generation energy systems that offer ultra-high specific energy, wide operational temperature ranges, intrinsic safety, and vacuum compatibility. These requirements severely challenge conventional lithium-ion batteries due to issues like electrolyte leakage and thermal instability. This perspective examines solid-state batteries (SSBs) as a potential solution, leveraging their inherent leak-proof design, superior thermal tolerance, and robust solid electrolytes. We suggest that SSBs could become a key technology for applications such as lunar surface operations, deep-space probes, and high-speed vehicles. By addressing certain limitations of current power sources, SSB technology may help shape the energy architecture for future space exploration and commercialization.

Batteries doi: 10.3390/batteries12050172

Authors: Juntao Wu Yafei Fan Haojun Zhang Ziheng Wang Jianhong Du Diping Yuan

To address the critical industry challenges of insufficient thermal safety and reliability in the stacking design of lithium-ion battery (LIB) modules, as well as the lack of accurate traceability methods for LIB fire accidents, this study takes commercial 18650-type lithium, nickel cobalt aluminum (NCA) LIBs as the research object. First, we systematically investigated the thermal runaway (TR) behavior of single cells under thermal and electrical abuse conditions, identified the significant discrepancies in TR behavior between the two abuse scenarios, quantitatively revealed a positive correlation between TR risk and state of charge (SOC), and determined that the core feature is that the maximum heat release occurs in the negative electrode of the battery. Subsequently, we quantitatively analyzed the influences of the initial TR trigger position and module stacking structure on the TR propagation characteristics within the module, and obtained the key conclusions that center-triggered TR exhibits a faster propagation rate and that the vertical stacking structure significantly aggravates the TR chain reaction. Finally, based on the TR process, this paper summarizes the burn mark characteristics caused by different triggers of thermal runaway in LIBs. The results of this study provide critical fundamental data for optimizing the thermal safety design of LIB modules, and offer a scientific basis for the formulation of LIB fire rescue schemes and the implementation of fire investigation.

Batteries doi: 10.3390/batteries12050171

Authors: Guanyi Wang Byeongdu Lee Devon Powers Meghan Burns Young-Geun Lee Michael C. Tucker Jeong Seop Yoon Pallab Barai Yuzi Liu Venkat Srinivasan Sanja Tepavcevic Yuepeng Zhang

This study investigates the formation mechanism of non-doped cubic lithium lanthanum zirconium oxide (c-LLZO) nanofibers using in situ synchrotron X-ray scattering techniques. Electrospun polymer precursor nanofibers were annealed at temperatures up to 800 °C, enabling real-time tracking of phase transitions via simultaneous small-angle X-ray scattering (SAXS), wide-angle X-ray scattering (WAXS), and evolved CO2 gas analysis. The results reveal a three-step transformation pathway: polymer decomposition, formation of La2Zr2O7 (LZO), and direct conversion of LZO to c-LLZO without intermediate tetragonal phases detected within the sensitivity of our in situ WAXS measurement. Cryo-electron energy loss spectroscopy (EELS) further elucidates the role of lithium diffusion, showing Li enrichment at fiber surfaces and Li deficiency in the interior, which stabilizes the cubic phase. This Li segregation effect in nanostructured LLZO materials extends beyond the previously reported size effect. This work advances the understanding of c-LLZO formation mechanisms and provides practical insights for optimizing synthesis routes to achieve phase-pure c-LLZO for solid-state battery applications.

Batteries doi: 10.3390/batteries12050170

Authors: Siyuan Yang Jinlei Sun Yaodong Wang Yu Chen Meng Li Jiuyu Du Xiaogang Wu

Efficient and reliable cooling is essential for ensuring the safety and performance of battery packs in electric vertical takeoff and landing (eVTOL) aircraft. To address the limitations of existing cooling methods in cooling capability and structural integration, this study proposes a hybrid cooling system combining air cooling, high-thermal-conductivity plates (HCPs), and phase-change material (PCM). The power demand in different eVTOL flight phases is first analyzed. A single-cell simulation model is then developed and validated through experiments. The effects of three key structural parameters on system performance are investigated, and their relative importance is quantified using sensitivity analysis. A multi-objective evaluation framework is further established to compare the proposed system with no cooling, passive cooling, and liquid cooling strategies. The adaptability of the hybrid cooling system under different operating conditions is also evaluated. Finally, an air-cooling intervention strategy is proposed based on the PCM liquid fraction. The results show that the optimized hybrid cooling system limits the maximum battery temperature and maximum temperature difference to 37.9 °C and 3.1 °C, respectively. Compared with passive cooling, the proposed system improves temperature stability by 44.6%. Compared with the liquid cooling system, space occupancy is reduced by 19.5%, and the grouping efficiency is increased by 22.4%. The adaptability analysis indicates that the optimized system is suitable for ambient temperatures not exceeding 30 °C. In addition, the proposed air-cooling intervention strategy reduces the air-cooling energy consumption by 43.3% compared with continuous air cooling, while maintaining temperature uniformity. These findings provide a numerical reference for the preliminary design of eVTOL battery cooling systems.

Batteries doi: 10.3390/batteries12050169

Authors: Hao Li Shuo Shi Binghui Xu Xiu-Song Zhao

Hard carbon (HC) materials are widely recognized as one of the most promising anode candidates for sodium-ion batteries (SIBs). Biomass-derived HC materials particularly possess the advantages of abundant sources, low cost, and high sodium-ion (Na+) storage capacity. In this work, the agricultural byproduct corn cob is employed as a raw material to prepare HC samples via a facile two-step approach of pre-carbonization and Joule heating treatment. Among the prepared HC samples, the CHC-1400 sample exhibits the optimal physiochemical properties. As a result, the corresponding CHC-1400 electrode not only delivers the highest initial reversible capacity of 263 mAh g−1 with a corresponding initial coulombic efficiency (ICE) of 72% at 0.2 C, but maintains a high capacity retention of 91% after 300 cycles. The Na+ storage mechanism for the HC samples has thus been revealed. This study introduces a novel, time-saving, and cost-effective protocol for synthesizing biomass-derived HC anode materials, which is of great significance to the advancement of SIBs.

Batteries doi: 10.3390/batteries12050168

Authors: Jiang Huang Haoran Zhang Yunjia Deng Chi Ouyang Yuanhua He

The low-pressure environment at aircraft cruising altitudes severely degrades lithium battery performance, yet the effectiveness and mechanisms of air-cooling thermal management under such conditions remain poorly understood. This study systematically investigates the coupled thermal, electrical, and material responses of NCM523/graphite ternary batteries under forced air-cooling at three pressures (96 kPa, 77 kPa, 58 kPa) and varying wind speeds (0–10 m/s) during 4C charge/6C discharge cycling. Air cooling reduces the maximum surface temperature by up to 14.2 °C and maintains the temperature difference below 5 °C, even at 58 kPa. An optimal wind speed of 6 m/s extends cycle life by 71% at 58 kPa (from 45 to 77 cycles), suppresses resistance growth, and preserves discharge capacity. Further increasing the wind speed paradoxically accelerates degradation. Post-mortem analyses reveal that appropriate air cooling mitigates cathode particle fragmentation, restores cation mixing (I003/I104 from 1.07 to 1.63 for 58 kPa), reduces transition metal dissolution, and suppresses solid electrolyte interface (SEI) thickening. This work establishes an optimum air velocity for low-pressure battery cooling and provides mechanistic insights into preserving electrode structural integrity, offering design guidelines for safe battery thermal management in electric aircraft.