Batteries

15 pages, 5476 KB

Open AccessArticle

CFD-Taguchi-Based Geometric Optimization of a Liquid Cooled Battery Thermal Management System

by Beytullah Erdoğan and Güneyhan Taşkaya

Batteries 2026, 12(7), 267; https://doi.org/10.3390/batteries12070267 - 21 Jul 2026

Viewed by 645

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 [...] Read more.

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 (T_max) 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. Full article

(This article belongs to the Section Electric Vehicles and Mobile Energy Storage Systems)

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33 pages, 6183 KB

Open AccessArticle

State of Charge Estimation for Both Electric Bus and Passenger Vehicles with Different Battery Types Using Multi-Instance Learning

by Ibrahim Atakan Kubilay and Derya Birant

Batteries 2026, 12(7), 266; https://doi.org/10.3390/batteries12070266 - 21 Jul 2026

Viewed by 337

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Advanced Intelligent Management Technologies of New Energy Batteries)

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35 pages, 3080 KB

Open AccessArticle

Experimental Multi-Metric Health Assessment of Second-Life Electric Vehicle Batteries for Reuse Pathway Classification

by Md Sabbir Hossen, Gobbi Ramasamy, Ngu Eng Eng and Marran Al Qwaid

Batteries 2026, 12(7), 265; https://doi.org/10.3390/batteries12070265 - 21 Jul 2026

Viewed by 587

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue 10th Anniversary of Batteries: Battery Health, Aging and Degradation Mechanisms)

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23 pages, 970 KB

Open AccessReview

Rechargeable Batteries for Grid-Scale Energy Storage: Technologies, Performance, and Emerging Directions

by Lincoln Pinoski, Blake Latos, Devin Marigny, Taylor Jensen, Aidan De Los Reyes, Brian Helwig and Pradeep L. Menezes

Batteries 2026, 12(7), 264; https://doi.org/10.3390/batteries12070264 - 20 Jul 2026

Cited by 1 | Viewed by 1549

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Battery Manufacturing: Current Status, Challenges, and Opportunities: 2nd Edition)

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36 pages, 24659 KB

Open AccessArticle

An Adaptive Fuzzy Active Equalization Strategy Coupling SOC and Irradiance for Retired Batteries in Photovoltaic Energy Storage Applications

by Yan Jiang, Jiawei Chen, Rui Liu, Yupeng Guo, Hai Wang, Minghan Zhu and Jianying Li

Batteries 2026, 12(7), 263; https://doi.org/10.3390/batteries12070263 - 20 Jul 2026

Viewed by 581

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Advances in Data-Driven and Learning Methods Applied to Battery Safety and Health)

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20 pages, 2537 KB

Open AccessArticle

Multi-Scale Degradation Trend Perception for Voltage Degradation Prediction of Proton Exchange Membrane Fuel Cells

by Sihao Zhang, Wenbo Hao, Kai Zhao, Zengzhe Shi, Jian Mei, Sergey Grigoriev, Chuanyu Sun and Xuan Meng

Batteries 2026, 12(7), 262; https://doi.org/10.3390/batteries12070262 - 19 Jul 2026

Cited by 1 | Viewed by 590

Abstract

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. [...] Read more.

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. Full article

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22 pages, 5876 KB

Open AccessArticle

Continuous Mixing of Graphite Anode Slurry: Fast-Charge Optimization Through Binder Network Tailoring

by Paul Guenther, Andreas Huth, Kristian Nikolowski, Oliver Lohrberg, Mareike Partsch, Annegret Potthoff and Alexander Michaelis

Batteries 2026, 12(7), 261; https://doi.org/10.3390/batteries12070261 - 18 Jul 2026

Viewed by 820

Abstract

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 [...] Read more.

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 (

R^{2} = 0.86

), whereas the correlation with charge-transfer resistance is weaker (

R^{2} = 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 (

R^{2} = 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. Full article

(This article belongs to the Special Issue Battery Manufacturing: Current Status, Challenges, and Opportunities: 2nd Edition)

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26 pages, 3814 KB

Open AccessArticle

Frequency-Aware Optimal Power Allocation for Battery-Supercapacitor Hybrid Energy Storage System

by Long Gao, Jinbo Long, Zhiyu Jia, Zhaoyang Zeng and Weirong Liu

Batteries 2026, 12(7), 260; https://doi.org/10.3390/batteries12070260 - 17 Jul 2026

Viewed by 472

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Hybrid Energy Storage and Integrated Systems)

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32 pages, 3151 KB

Open AccessReview

A Review of Graphite Anode Recycling in Lithium-Ion Batteries: Technical Challenges and Geopolitical and Economic Implications

by Mina Rezaei, Anil Kumar Madikere Raghunatha Reddy, Jeremy I. G. Dawkins, Thiago M. G. Selva and Karim Zaghib

Batteries 2026, 12(7), 259; https://doi.org/10.3390/batteries12070259 - 17 Jul 2026

Cited by 1 | Viewed by 1690

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Sustainable Manufacturing and Circular Economy)

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20 pages, 1232 KB

Open AccessArticle

Variability Analysis of Battery EIS Measurements

by Prarthana Pillai, Banuselvasaraswathy Balasubramanian, Krishna R. Pattipati and Balakumar Balasingam

Batteries 2026, 12(7), 258; https://doi.org/10.3390/batteries12070258 - 17 Jul 2026

Viewed by 549

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Battery Management Systems Based on Electrochemical Impedance Spectroscopy—2nd Edition)

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18 pages, 778 KB

Open AccessArticle

A Survey-Informed Digital Competitiveness Framework for Emerging Battery Manufacturing Ecosystems: Evidence from Romanian Cross-Sectoral Industrial Firms

by Mirela Simijdean, Diana Ilea, Denisa Szabo, Ovidiu Aurel Ghiuță, Aurel Mihail Țîțu and Mihai Dragomir

Batteries 2026, 12(7), 257; https://doi.org/10.3390/batteries12070257 - 17 Jul 2026

Viewed by 519

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Battery Manufacturing: Current Status, Challenges, and Opportunities: 2nd Edition)

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33 pages, 3373 KB

Open AccessArticle

M5Boost: A Machine Learning Approach for Driving Range Estimation in Electric Vehicles Considering Battery-Related Factors

by Ibrahim Atakan Kubilay, Kadriye Filiz Balbal, Kokten Ulas Birant and Derya Birant

Batteries 2026, 12(7), 256; https://doi.org/10.3390/batteries12070256 - 16 Jul 2026

Viewed by 463

Abstract

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, [...] Read more.

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. Full article

(This article belongs to the Special Issue Artificial Intelligence-Enhanced Battery Modeling and Management for Electric Vehicles)

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43 pages, 2848 KB

Open AccessReview

Toward Trustworthy and Transferable SOH/RUL Estimation for Lithium-Ion Batteries: A Critical Review and Multi-Fidelity Validation Framework from Laboratory Cells to Real-World Packs

by Stefan Rizanov, Anna Stoynova and Georgy Mihov

Batteries 2026, 12(7), 255; https://doi.org/10.3390/batteries12070255 - 15 Jul 2026

Cited by 2 | Viewed by 851

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Thermal Runaway and Thermal Management: Toward Safe and Reliable Batteries)

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46 pages, 9008 KB

Open AccessArticle

Battery-Aware Control of a Single-Phase Integrated Battery Charger Using NMPC, EKF, and LUT-Based Lithium-Ion Pack Modeling

by Phonrut Bousungnoen and Padej Pao-la-or

Batteries 2026, 12(7), 254; https://doi.org/10.3390/batteries12070254 - 14 Jul 2026

Viewed by 385

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Advances in Charging Systems and Charging Management Strategies for Battery Electric Vehicles)

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21 pages, 13685 KB

Open AccessArticle

Early Prediction of Commercial Energy Storage Battery Cycle Life Based on Health Features and Transfer Learning

by Shuping Wang, Xinyue Zhou, Yifeng Cheng, Changhao Li, Guohong Chen, Tian Jiang, Bangyu Li, Feng Ye and Xianzhong Sun

Batteries 2026, 12(7), 253; https://doi.org/10.3390/batteries12070253 - 13 Jul 2026

Viewed by 609

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries: 3rd Edition)

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14 pages, 2890 KB

Open AccessArticle

Fault Tree Analysis of Lithium-Ion Battery Pack Fire Risk for Electric Vehicle Applications

by Aurélia Ditto, Julien Dauchy, Rémi Vincent, Dimitri Gevet, Cédric Payan, Céline Bonnaud and Clément Weick

Batteries 2026, 12(7), 252; https://doi.org/10.3390/batteries12070252 - 13 Jul 2026

Viewed by 710

Abstract

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 [...] Read more.

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. Full article

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20 pages, 17118 KB

Open AccessFeature PaperArticle

A Hybrid Transformer Network Optimized by Kalman Filter for State of Health Prediction on Lithium-Ion Battery

by Lei Xu, Peng Sun and Nan Zhou

Batteries 2026, 12(7), 251; https://doi.org/10.3390/batteries12070251 - 13 Jul 2026

Viewed by 463

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Advanced Intelligent Management Technologies of New Energy Batteries)

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15 pages, 24085 KB

Open AccessArticle

Numerical Study on Effect of Ventilation on Fire Characteristics of Lithium-Ion Battery in Energy Storage Cabin

by Wei Lin, Lingcheng Zeng, Junyu Liu and Zhiying Ding

Batteries 2026, 12(7), 250; https://doi.org/10.3390/batteries12070250 - 12 Jul 2026

Viewed by 575

Abstract

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 [...] Read more.

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. Full article

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36 pages, 5109 KB

Open AccessArticle

Lévy Jump Nonlocal SPDE and BA-PINN Modeling for Battery Fracture and Thermal-Runaway Warning

by Yongfang Zhu, Qing Xie and Jingli Jia

Batteries 2026, 12(7), 249; https://doi.org/10.3390/batteries12070249 - 12 Jul 2026

Viewed by 989

Abstract

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 [...] Read more.

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. Full article

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25 pages, 10363 KB

Open AccessArticle

A Reduced-Switch Battery/Supercapacitor Hybrid Energy Storage System for Battery Current Stress Mitigation in Low-Power Electric ATVs

by Jianlin Wang, Shenglong Zhou, Zijian Yu, Minfeng Liu and Lang Liu

Batteries 2026, 12(7), 248; https://doi.org/10.3390/batteries12070248 - 12 Jul 2026

Viewed by 337

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Hybrid Energy Storage and Integrated Systems)

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19 pages, 7318 KB

Open AccessArticle

Single-Precursor Solid-Phase Synthesis of Poly(o-phenylenediamine) Sulfide Derivatives as Cost-Effective Organic Cathode Materials

by Hanfei Luo, Hao Zhang, Rui Wang and Zhiping Song

Batteries 2026, 12(7), 247; https://doi.org/10.3390/batteries12070247 - 9 Jul 2026

Viewed by 433

Abstract

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 [...] Read more.

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 (–S_n–) linkages. The dual redox activity of C=N bonds in phenazine repeating units and S–S bonds in –S_n– 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. Full article

(This article belongs to the Section Electrode Materials and Advanced Characterization)

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20 pages, 4225 KB

Open AccessArticle

Intelligent Pump Fault Diagnosis for Vanadium Redox Flow Battery Using Deep Learning with Multi-Head Self-Attention

by Lu Lu, Xunzhao Zheng, Shaojin Wang, Binyu Xiong, Jun Feng, Jinrui Tang, Feifei Dong and Chonghui Liu

Batteries 2026, 12(7), 246; https://doi.org/10.3390/batteries12070246 - 9 Jul 2026

Viewed by 454

Abstract

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 [...] Read more.

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. Full article

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31 pages, 6652 KB

Open AccessArticle

Early and Uncertainty-Aware Detection of Impending Voltage Outliers in Battery Packs via a Probabilistic Hierarchical Adaptive Framework

by Teng Liu, Wei Li, Zhiqiang Li and Shangbo Wu

Batteries 2026, 12(7), 245; https://doi.org/10.3390/batteries12070245 - 6 Jul 2026

Viewed by 405

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue AI-Powered Battery Management and Grid Integration for Smart Cities)

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17 pages, 1746 KB

Open AccessArticle

Derating Approach for Lithium-Ion Batteries

by Zhou He, Michael Osterman and Michael Pecht

Batteries 2026, 12(7), 244; https://doi.org/10.3390/batteries12070244 - 6 Jul 2026

Cited by 1 | Viewed by 647

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Lithium-Ion and Solid-State Batteries)

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23 pages, 2885 KB

Open AccessArticle

An Analysis of the Charging Behavior of Electric Vehicle Users Based on Charging Station Data: A Case of Central Europe

by Michal Fišer, Martin Kozelka, Pavla Hošková, Přemysl Jedlička, Martin Kotek, Milan Straka, Luboš Buzna and Martin Libra

Batteries 2026, 12(7), 243; https://doi.org/10.3390/batteries12070243 - 6 Jul 2026

Viewed by 881

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Advances in Charging Systems and Charging Management Strategies for Battery Electric Vehicles)

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11 pages, 19012 KB

Open AccessArticle

Scalable Fabrication of a Na/Na₂In Composite Anode with Enhanced Processability and Cycling Stability for Sodium Metal Batteries

by Bingqian Zhang, Lin Fu, Jingqian Wang, Menglan Lv, Tong Shu, Guocheng Li, Yuanjian Li, Juan Du and Mintao Wan

Batteries 2026, 12(7), 242; https://doi.org/10.3390/batteries12070242 - 4 Jul 2026

Viewed by 440

Abstract

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 [...] Read more.

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 NaFe_1/3Ni_1/3Mn_1/3O₂ 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. Full article

(This article belongs to the Special Issue Design and Optimization of Critical Materials for Lithium or Sodium Batteries)

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15 pages, 3628 KB

Open AccessArticle

Impact of State of Charge on Gas Generation Characteristics During Thermal Runaway of Lithium-Ion Batteries and Early Warning Strategy Research

by Yanli Miao, Xiao Tan, Chenying Li, Jianjun Liu, Ling Sa, Xiaohan Li and Zongjia Qiu

Batteries 2026, 12(7), 241; https://doi.org/10.3390/batteries12070241 - 3 Jul 2026

Viewed by 512

Abstract

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 LiNi₁/₃Co₁/₃Mn [...] Read more.

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. Full article

(This article belongs to the Special Issue Advances in Lithium-Ion Battery Safety and Fire: 2nd Edition)

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70 pages, 29508 KB

Open AccessReview

Thermal Runaway in Batteries: A Database-Driven Literature Review and Exploratory Statistical Analysis

by Felix Elsner and Stefan Pischinger

Batteries 2026, 12(7), 240; https://doi.org/10.3390/batteries12070240 - 2 Jul 2026

Cited by 1 | Viewed by 1347

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Thermal Runaway and Thermal Management: Toward Safe and Reliable Batteries)

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14 pages, 8056 KB

Open AccessArticle

Cu-Cu₂O/ZrO₂ Mixed Oxide by Self-Sustained Combustion of Amorphous Ribbons as Electrode Material for Supercapacitor

by Mircea Nicolaescu, Carmen Lazau, Corina Orha, Cosmin Codrean and Cornelia Bandas

Batteries 2026, 12(7), 239; https://doi.org/10.3390/batteries12070239 - 30 Jun 2026

Viewed by 373

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue High Energy Density Supercapacitors: Acquisition, Characterization, and Application: 2nd Edition)

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19 pages, 5632 KB

Open AccessArticle

Deep Learning-Based Image Classification of 18650 Lithium-Ion Battery Structural Health Using X-Ray Micro-Computed Tomography

by Justin An, Aigbe E. Awenlimobor, Jiajun Xu and Miaomiao Ma

Batteries 2026, 12(7), 238; https://doi.org/10.3390/batteries12070238 - 30 Jun 2026

Viewed by 756

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Energy Storage System Aging, Diagnosis and Safety)

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