Batteries

20 pages, 11242 KB

Open AccessArticle

Research on the Characteristics of Internal Short Circuits in Lithium-Ion Batteries Under Complex Condition: Nail Penetration Coupled with Charge/Discharge

by Yong Ding, Wenda Li, Linhui Wang and Zhoujian An

Batteries 2026, 12(5), 166; https://doi.org/10.3390/batteries12050166 - 11 May 2026

Abstract

This study systematically investigates the internal short circuit (ISC) characteristics of lithium-ion batteries (LIBs) for electric vehicles under nail penetration abuse coupled with charge/discharge operations. By establishing a nail penetration coupled with dynamic charge/discharge experimental platform and using commercial NCM pouch cells as [...] Read more.

This study systematically investigates the internal short circuit (ISC) characteristics of lithium-ion batteries (LIBs) for electric vehicles under nail penetration abuse coupled with charge/discharge operations. By establishing a nail penetration coupled with dynamic charge/discharge experimental platform and using commercial NCM pouch cells as test subjects, it comprehensively analyzes the effects of different charge/discharge operations (charging, discharging, resting) and C-rates (0.2 C–3 C) on battery surface temperature, voltage, current, mass loss, and thermal runaway behavior. The research finds that a mutual inhibitory effect exists between discharge operation and ISC, manifested as reduced discharge current along with decreased surge current, temperature rise rate, and voltage drop, significantly lowering the battery’s thermal runaway risk. In contrast, charging operation exacerbates ISC severity, causing increases in surge current, temperature rise rate, maximum temperature, and mass loss with higher C-rates, substantially enhancing thermal hazards. The study further reveals the underlying mechanisms: during discharge, a “Li⁺ competition” effect suppresses the short-circuit current, whereas during charging, the external power source and the battery jointly form the short-circuit current, intensifying heat generation. This research provides important experimental evidence and theoretical support for the thermal safety design and risk assessment of LIBs under operating conditions. Full article

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

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22 pages, 4029 KB

Open AccessArticle

Mechanistic Study of Hydrothermal Management in Air Cooled PEMFCs by Coordinated Ultrasonic Atomization and Fan Regulation Through Three-Dimensional Multiphysics Coupling

by Jing Qin, Haoran Ma, Haotian Yang and Xing Huang

Batteries 2026, 12(5), 165; https://doi.org/10.3390/batteries12050165 - 10 May 2026

Abstract

To address the difficulty of simultaneously achieving effective heat dissipation and adequate humidification in open-cathode air-cooled proton exchange membrane fuel cells (PEMFCs) under medium and high power operation, this study proposes a hydrothermal management strategy based on coordinated ultrasonic atomization humidification and fan [...] Read more.

To address the difficulty of simultaneously achieving effective heat dissipation and adequate humidification in open-cathode air-cooled proton exchange membrane fuel cells (PEMFCs) under medium and high power operation, this study proposes a hydrothermal management strategy based on coordinated ultrasonic atomization humidification and fan speed regulation. A three-dimensional single-cell multiphysics model is developed and validated using a 300 W experimental platform. The effects of atomization frequency and water temperature on stack performance and internal hydrothermal distribution are systematically investigated. Results show that ultrasonic atomization provides inlet precooling, latent heat absorption, and active region humidification, thereby improving hydrothermal uniformity within the stack. Under the optimal condition of 100 kHz and 55 °C, the peak stack power increases by 21.0% to 319.00 W, while voltage consistency and surface temperature uniformity are also improved. Analysis based on the Stokes number and Dalton’s law of partial pressures indicates that the optimum results from a balance between suppressing droplet agglomeration and inertial deposition, and limiting oxygen dilution caused by excessive water vapor. The proposed strategy provides a compact and practical approach for improving the stability, uniformity, and efficiency of air-cooled PEMFCs. Full article

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18 pages, 4174 KB

Open AccessArticle

Multi-Objective Optimization Design of Wavey-Channel Cold Plates for Li-Ion Batteries by Deep Neural Network

by Kun Xi, Zhihui Xie, Xinshan Ni, Min Zhang and Xiaochen Chen

Batteries 2026, 12(5), 164; https://doi.org/10.3390/batteries12050164 - 9 May 2026

Abstract

The continuously improving power density of Li-ion batteries and the widespread application of fast charging and discharging have rendered thermal management an increasingly critical task. Cold plates are among the most important means for such a task, and their channel structure significantly affects [...] Read more.

The continuously improving power density of Li-ion batteries and the widespread application of fast charging and discharging have rendered thermal management an increasingly critical task. Cold plates are among the most important means for such a task, and their channel structure significantly affects battery performance. Aiming to further improve the thermohydraulic performance of cold plate, this study proposes a cold plate with sinusoidal wave-shaped channel. Using channel quantity, amplitude, wavelength, diameter, and coolant mass flow rate as variables, the orthogonal experimental scheme is employed to design combinations of different variables for numerical simulation. The numerical simulation results are used to train a deep neural network for cold plate performance prediction. The trained neural network can accurately predict the maximum temperature, comprehensive performance indicators, and entropy generation rate with errors below 5.0%, 5.0%, and 10.0%, respectively. Multi-objective optimization design (MOOD) is implemented by combining a deep neural network with the NSGA-II genetic optimization, yielding two sets of Pareto fronts as follows: one for maximizing comprehensive performance indicator and minimizing entropy generation rate, and the other for minimizing maximum temperature and entropy generation rate, and TOPSIS decision points are provided. This study provides a new method and valuable MOOD results for the thermal management of Li-ion batteries and cold plate engineering while offering theoretical guidance for practical applications. Full article

(This article belongs to the Special Issue Thermal Management System for Lithium-Ion Batteries: 3rd Edition)

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28 pages, 5905 KB

Open AccessArticle

Impacts of EV Usage Patterns on Battery Pack Medium-Term Degradation

by Clemente Capasso, Luigi Iannucci, Stanislao Patalano, Ottorino Veneri and Ferdinando Vitolo

Batteries 2026, 12(5), 163; https://doi.org/10.3390/batteries12050163 - 9 May 2026

Abstract

Lithium-ion battery (LiB) ageing is a critical challenge that requires in-depth investigation to extend the useful life of electric vehicles (EVs). This phenomenon drastically impacts cell performance and is primarily influenced by environmental factors and operating conditions, such as charge/discharge rates and the [...] Read more.

Lithium-ion battery (LiB) ageing is a critical challenge that requires in-depth investigation to extend the useful life of electric vehicles (EVs). This phenomenon drastically impacts cell performance and is primarily influenced by environmental factors and operating conditions, such as charge/discharge rates and the State of Charge (SoC) during rest periods. This study investigates the impact of vehicle operational duty cycles on battery pack (BP) longevity through combined experimental and numerical evaluations. To this end, a lumped electro-thermal BP model was developed and validated at the single-cell level. Furthermore, a capacity fade model, customized for the specific cell chemistry and capacity, was implemented based on the literature benchmarks. The analysis considers user-related parameters, including driving style, charging strategies, and ambient temperatures. The results suggest that aggressive driving significantly accelerates BP ageing when combined with conservative charging strategies in warm climates. Additionally, adopting high DoD values can reduce useful life by up to 30%, while high temperatures can double the rate of capacity fade. Regarding C-rates, fast-charging operations predominantly impact degradation when non-conservative strategies are employed, particularly in cold environments. Full article

(This article belongs to the Collection Advances in Battery Energy Storage and Applications)

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33 pages, 7680 KB

Open AccessArticle

RUL Prediction in LFP Batteries: Comparison of Gompertz, LSTM and Gompertz-Informed LSTM Models for Interpretability and Accuracy

by Yuri Njathi, Ciira wa Maina and Edwell T. Mharakurwa

Batteries 2026, 12(5), 162; https://doi.org/10.3390/batteries12050162 - 7 May 2026

Abstract

Lithium iron phosphate batteries have seen a recent rise in usage in electric vehicles and battery energy storage systems. For these applications, reliability is of paramount importance, influences long-term adoption and high return on investment, especially regarding battery replacement. Remaining Useful Life (RUL) [...] Read more.

Lithium iron phosphate batteries have seen a recent rise in usage in electric vehicles and battery energy storage systems. For these applications, reliability is of paramount importance, influences long-term adoption and high return on investment, especially regarding battery replacement. Remaining Useful Life (RUL) prediction is at the core of avoiding unexpected failure and enabling proactive battery maintenance. Physics-based and data-driven methods have been explored by researchers, whilst Physics-Informed Neural Networks (PINNs) can combine their strengths in estimating battery RUL. This paper investigates the integration of the Gompertz function, an inherently interpretable white-box model, into Long Short-Term Memory (LSTM) networks to follow the physical laws of degradation, capture downward monotonic behavior and long-term dependencies from data resulting in Gompertz-Informed LSTMs (GILSTMs). Pure LSTMs are regarded as black box systems and critical infrastructure operators such as battery energy storage system (BESS) operators may refrain from using such systems. Gray-box models such as GILSTMs may get over this hurdle by increasing model interpretability and helping industry adopters know when they will benefit from data-driven modeling. This study explores two GILSTM architectures. The first uses an LSTM to predict Gompertz parameters, which are then converted into RUL via the inverse Gompertz equation. The second uses the inverse Gompertz equation as a verification step to cross-check the RUL values generated by the LSTM. The first type of GILSTM was constrained by both a physics loss and an inverse Gompertz layer to predict RUL while the second verified the results of an LSTM, despite that the GILSTMs failed to generalize. The first type of GILSTM achieved an average RMSE of 22.97%, while the second type achieved an average RMSE of 26.99%. The models in this paper are also benchmarked on the first 100 cycles, a current state of art for battery degradation testing. The best overall implementation was an LSTM that predicted RUL by recursively predicting SoH achieving an average RMSE per cycle of 9.18% and a 100th cycle RMSE of 17.02%. This study evaluates the trade-off between the predictive accuracy of black-box LSTMs and physical interpretability of Gompertz models. While pure LSTMs provide superior accuracy, the Gompertz parameters stabilize by 85% SoH. This 85% threshold serves as an interpretable confidence trigger, informing BESS operators when to rely on LSTM RUL forecasts. Full article

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

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21 pages, 7540 KB

Open AccessArticle

Investigation of Structural-Dependent Critical Lithium Plating Charging-Rates and Optimization of Electrode Architecture

by Zhaoyang Li, Rui Zhang, Yue Li, Xingai Wang, Ning Wang, Lei Wang, Haichang Zhang and Fei Ding

Batteries 2026, 12(5), 161; https://doi.org/10.3390/batteries12050161 - 3 May 2026

Abstract

Achieving the coexistence of high energy density and fast-charging capability remains a fundamental challenge for lithium-ion batteries. Increasing electrode thickness and compaction density enhances energy density but simultaneously alters the pore structure and restricts lithium-ion transport, leading to concentration polarization, increased resistance, and [...] Read more.

Achieving the coexistence of high energy density and fast-charging capability remains a fundamental challenge for lithium-ion batteries. Increasing electrode thickness and compaction density enhances energy density but simultaneously alters the pore structure and restricts lithium-ion transport, leading to concentration polarization, increased resistance, and lithium plating. In this work, we employ X-ray computed tomography (X-CT) and 3D reconstruction to establish quantitative relationships between particle size, compaction density, and key structural parameters (porosity, tortuosity, effective proportion of lithium-ion flux (f_eff)). Then, an electrochemical model is used to link the liquid-phase kinetic parameters (ionic conductivity (k₀) and liquid-phase diffusion coefficient), as corrected by the effective proportion of lithium-ion flux f_eff, to polarization and lithium-plating behavior, and the maximum current density without lithium plating under various fabrication conditions is finally determined. Results show that small-particle electrodes exhibit superior rate capability at moderate compaction levels, but suffer from rapidly increasing tortuosity and reduced transport efficiency under high compaction and large thickness. Moreover, a double-layer gradient electrode design effectively integrates the advantages of both large- and small-particle architectures, enabling high-rate operation without lithium plating. The double-layer gradient electrode (ρ = 1.6 g/cm³) exhibited ~50% higher performance at 1.5 C compared to the small-particle anode and enabled 2 C charging without lithium plating. This study offers a robust structural design strategy for optimizing thick-electrode architectures toward high-energy, fast-charging LIBs. Full article

(This article belongs to the Section Battery Materials and Interfaces: Anode, Cathode, Separators and Electrolytes or Others)

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13 pages, 5381 KB

Open AccessFeature PaperArticle

Comparative Study on the Physicochemical and Electrochemical Properties of Mg/Ti Co-Doping LiMn_0.6Fe_0.4PO₄/C Cathode Materials Synthesized via CVD Using Diverse Manganese Precursors

by Sha Li, Yizhou Cao, Xinyi Wang, Keyuan Feng, Hongxu Li, Youyuan Zhou and Suqin Liu

Batteries 2026, 12(5), 160; https://doi.org/10.3390/batteries12050160 - 2 May 2026

Abstract

This study investigates the influence of various manganese sources—specifically MnCO₃, Mn₃O₄, and MnO₂—on the performance of lithium manganese iron phosphate (LMFP) synthesized through a combined spray-drying and chemical vapor deposition (CVD) strategy. The synthesis protocol [...] Read more.

This study investigates the influence of various manganese sources—specifically MnCO₃, Mn₃O₄, and MnO₂—on the performance of lithium manganese iron phosphate (LMFP) synthesized through a combined spray-drying and chemical vapor deposition (CVD) strategy. The synthesis protocol involved the initial formation of a precursor through the co-sintering of manganese, phosphorus, iron, and dopant sources via CVD, followed by secondary spray-drying and carbon thermal reduction with Li₂CO₃ and carbon additives. Morphological analysis via Scanning Electron Microscopy (SEM) and laser diffraction indicates that Mn₃O₄-derived LMFP possesses highly spherical secondary structures comprising well-crystallized, uniformly distributed primary particles. Elemental mapping via Energy Dispersive Spectroscopy (EDS) confirms a homogeneous distribution of stoichiometric elements without localized segregation, alongside the successful lattice integration of dopants. In contrast, the MnCO₃-derived samples exhibited deleterious carbon accumulation on the primary particle surfaces. Consequently, the Mn₃O₄-based LMFP demonstrated superior electrochemical kinetics, delivering a remarkable initial discharge capacity of 148.9 mAh g⁻¹ at 1C, with an exceptional capacity retention of 97.9% after 100 cycles. These findings underscore the critical role of precursor selection in optimizing the interfacial and bulk properties of high-performance LMFP cathodes. Full article

(This article belongs to the Special Issue Multiscale Co-Design of Electrode Architectures and Electrolytes)

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Graphical abstract

35 pages, 5962 KB

Open AccessArticle

Battery State of Charge Estimation in Electric Vehicles Using Machine Learning with Feature Engineering and Seasonal Analysis Under On-Road Conditions

by Feristah Dalkilic, Kadriye Filiz Balbal, Kokten Ulas Birant, Elife Ozturk Kiyak, Yunus Dogan, Semih Utku and Derya Birant

Batteries 2026, 12(5), 159; https://doi.org/10.3390/batteries12050159 - 29 Apr 2026

Abstract

Estimating the state of charge (SoC) is a critical task for effective management of electric vehicle batteries. Simple machine learning methods (LR, KNN, etc.) often suffer from limited prediction accuracy, while deep learning approaches (LSTM, CNN, etc.) generally require high computational resources and [...] Read more.

Estimating the state of charge (SoC) is a critical task for effective management of electric vehicle batteries. Simple machine learning methods (LR, KNN, etc.) often suffer from limited prediction accuracy, while deep learning approaches (LSTM, CNN, etc.) generally require high computational resources and behave as black-box models with limited explainability. To overcome these limitations, the present work proposes a SoC estimation approach based on the Light Gradient Boosting Machine (LightGBM). The proposed model provides a balanced trade-off between prediction accuracy and computational efficiency. Furthermore, feature engineering is performed to derive additional informative features, improving the model’s ability to learn driving conditions and battery dynamics. In addition, the study incorporates a seasonal analysis by evaluating the model under both summer and winter conditions, allowing the impact of environmental variations on SoC estimation performance to be investigated. Moreover, Explainable Artificial Intelligence (XAI) techniques are employed to interpret the model predictions. Evaluation on real-world on-road data demonstrated that the proposed model achieved substantial improvements in estimation performance compared to recent studies. Full article

(This article belongs to the Special Issue Battery Degradation: Behavior, Mechanisms, Modeling, Estimation, and Optimization Strategies)

19 pages, 4503 KB

Open AccessArticle

Stepwise Carbonization of Bagasse into Defect-Ordered Hard Carbons with Enriched Ion Channels for High-Plateau Sodium-Ion Storage

by Kang Hong, Chong Zhang, Yanlei Zhang, Guirong Bao and Liqun Jiang

Batteries 2026, 12(5), 158; https://doi.org/10.3390/batteries12050158 - 29 Apr 2026

Abstract

Bagasse, owing to its low cost and high carbon yield, is a promising precursor for hard-carbon anodes in sodium-ion batteries (SIB). Regulating the microcrystalline state and pore architecture during pyrolysis is key to boosting Na⁺ storage behavior. Here, the pyrolysis kinetics is [...] Read more.

Bagasse, owing to its low cost and high carbon yield, is a promising precursor for hard-carbon anodes in sodium-ion batteries (SIB). Regulating the microcrystalline state and pore architecture during pyrolysis is key to boosting Na⁺ storage behavior. Here, the pyrolysis kinetics is controlled via stepwise carbonization to construct a defect-ordered island structure within the cellulose-derived carbon skeleton. Retaining sp³-hybridized carbon at low temperatures creates the Na⁺ channel, while acid cleaning selectively dissolves residual metal oxides, removing the electrochemical inert phase and promoting improved ion diffusion. This process also enriches active sites and interlayer spacing in the hard carbon, boosting capacity in the plateau region. In addition, the ash-catalyzed formation of local sp² graphite microcrystals provides electron transport nodes, optimizing Na⁺ diffusion and electronic conductivity. Accordingly, the assembled SIB achieves a high reversible capacity of 378 mAh g⁻¹ at 0.1C and an initial coulombic efficiency of 97%, with the plateau capacity accounting for 59.1% of the total reversible capacity. This work presents a universal thermochemical approach for engineering high-performance carbon anodes with high closed porosity from low-cost biomass precursors, advancing the development of sustainable and efficient SIBs. Full article

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30 pages, 2472 KB

Open AccessArticle

From Renewable Variability to Hybrid Stability: Analytical and Experimental Insights into a Transient Buffering Battery–Supercapacitor Framework in a Lab-Scale PV–Wind Microgrid

by Arash Asrari, Ajit Pandey, Carter E. LaMarche and Ryan P. Kowalski

Batteries 2026, 12(5), 157; https://doi.org/10.3390/batteries12050157 - 29 Apr 2026

Abstract

The growing use of electrochemical batteries in renewable energy systems has intensified the need for storage architectures that can sustain power delivery while limiting transient electrical stress and voltage instability challenges. This study addresses the research gap in experimentally establishing a physically interpretable [...] Read more.

The growing use of electrochemical batteries in renewable energy systems has intensified the need for storage architectures that can sustain power delivery while limiting transient electrical stress and voltage instability challenges. This study addresses the research gap in experimentally establishing a physically interpretable framework that links battery-centered hybrid storage behavior at the DC bus to AC-side inverter performance under load and source disturbances. A laboratory-scale renewable microgrid integrating photovoltaic and wind generation, programmable load variation, inverter-based AC delivery, and hybrid battery–supercapacitor storage is experimentally implemented and evaluated against a battery-only baseline, supported by a unified analytical framework that quantifies how transient buffering improvements propagate through the power conversion chain. The results show that the hybrid configuration reduces DC-bus voltage droop from about 1.1 V to 0.6 V under heavy-load transitions, and from approximately 0.85 V to 0.44 V during source-side variability (e.g., photovoltaic and wind turbine variations). The hybrid system also improves AC-side behavior, yielding unified stabilization indices of 103.03% for the root-mean-square voltage and 79.51% for the peak-to-peak voltage. These findings demonstrate that the experimentally implemented lab-scale renewable microgrid with hybrid battery–supercapacitor storage provides an effective pathway for improving battery-supported microgrid stability, waveform quality, and transient resilience. Full article

(This article belongs to the Section Supercapacitors)

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22 pages, 3642 KB

Open AccessArticle

Adaptive Hyperparameter-Tuned Transformer–LSTM for Lithium-Ion Battery State-of-Health Prediction

by Xujing Chu, Siyu Deng, Nitin Roy and Bin Zhang

Batteries 2026, 12(5), 156; https://doi.org/10.3390/batteries12050156 - 28 Apr 2026

Abstract

Accurate prediction of lithium-ion battery state of health (SOH) is crucial for improving the safety, reliability, and operational efficiency of battery management systems (BMSs). However, many data-driven methods still struggle to maintain robust forecasting performance when degradation trajectories differ across cells, especially in [...] Read more.

Accurate prediction of lithium-ion battery state of health (SOH) is crucial for improving the safety, reliability, and operational efficiency of battery management systems (BMSs). However, many data-driven methods still struggle to maintain robust forecasting performance when degradation trajectories differ across cells, especially in later-stage aging. To address this issue, this study developed a robustness-oriented SOH prediction framework, termed Ada-TL, by integrating a Transformer encoder, an LSTM regressor, and adaptive hyperparameter tuning. Cycle-level health indicators were extracted from the publicly available CALCE dataset and transformed into a compact representation for supervised learning. The Transformer module captures non-local dependencies within each input window, whereas the LSTM summarizes sequential degradation dynamics. The number of attention heads, the initial learning rate, and the L2 regularization coefficient are adaptively optimized to reduce manual trial-and-error in model configuration. Experimental results on four CS2 cells show that Ada-TL consistently outperformed BP, CNN–LSTM, and the fixed-hyperparameter baseline in overall SOH prediction accuracy, achieving RMSE values of 0.0210–0.0310, MAE values of 0.0163–0.0262, and MAPE values of 4.17–9.30%. Additional late-stage and cumulative-drift analyses further indicate that Ada-TL provided more stable post-knee tracking and better control of long-horizon bias accumulation, with late-stage RMSE reduced to 0.0169–0.0217 across the four cells. An ablation study also showed that the KPCA-based three-dimensional representation improved the overall test-set accuracy on most cells while reducing input dimensionality. These results suggest that the main value of Ada-TL lies in robustness-oriented SOH forecasting under cell-to-cell variability. Full article

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13 pages, 3026 KB

Open AccessArticle

Investigation of NMC-811 Surface Degradation in Pure CO₂ and Humid Air

by Nicolò Latini, Eugenio Gibertini, Marco Bianchi, Eleonora Natale, Gianluca Mondini, Vanni Lughi and Luca Magagnin

Batteries 2026, 12(5), 155; https://doi.org/10.3390/batteries12050155 - 27 Apr 2026

Abstract

Nickel-rich NMC-811 is a benchmark cathode material for high-energy density lithium-ion batteries due to its high specific capacity (>200 mAh g⁻¹) and operating voltage (~3.8 V). However, its strong surface reactivity toward atmospheric species, particularly moisture and CO₂, poses [...] Read more.

Nickel-rich NMC-811 is a benchmark cathode material for high-energy density lithium-ion batteries due to its high specific capacity (>200 mAh g⁻¹) and operating voltage (~3.8 V). However, its strong surface reactivity toward atmospheric species, particularly moisture and CO₂, poses significant challenges during storage and processing, leading to the formation of LiOH- and Li₂CO₃-rich surface layers. Although the effects of humid air have been widely investigated, a direct comparison between high relative humidity and pure CO₂ exposure remains limited. Here, we systematically examine the morphological, structural, chemical, and electrochemical evolution of commercial NMC-811 electrodes after 5 h exposure to 80% relative humidity or CO₂-saturated atmosphere. Moisture treatment induces substantial surface reconstruction, lattice shrinkage, and increased cation disorder, accompanied by extensive hydroxide and carbonate formation. In contrast, CO₂ exposure mainly modifies the outermost surface layer without significant bulk structural changes. Electrochemical testing reveals that CO₂-treated electrodes display higher initial polarization but quickly recover near-pristine performance, whereas humidity-treated electrodes exhibit persistent kinetic limitations, accelerated capacity fading, and earlier end-of-life. Overall, degradation severity follows the trend: pristine < CO₂ < RH 80%, highlighting the dominant role of moisture in irreversible structural deterioration. Full article

(This article belongs to the Special Issue 10th Anniversary of Batteries: Interface Science in Batteries)

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34 pages, 3599 KB

Open AccessReview

Challenges and Issues in Using Coated and Uncoated Graphitic Anodes in Lithium-Ion Batteries

by Keerthan Nagendra, Koorosh Nikgoftar, Anil Kumar Madikere Raghunatha Reddy, Jitendrasingh Rajpurohit, Jeremy I. G. Dawkins, Thiago M. Guimaraes Selva and Karim Zaghib

Batteries 2026, 12(5), 154; https://doi.org/10.3390/batteries12050154 - 25 Apr 2026

Abstract

Graphite remains the predominant negative electrode material in commercial lithium-ion batteries (LIBs); however, its practical performance is increasingly limited by interface-driven degradation rather than bulk intercalation. This review examines the interconnected electrochemical, mechanical, and safety challenges associated with uncoated and coated graphite, with [...] Read more.

Graphite remains the predominant negative electrode material in commercial lithium-ion batteries (LIBs); however, its practical performance is increasingly limited by interface-driven degradation rather than bulk intercalation. This review examines the interconnected electrochemical, mechanical, and safety challenges associated with uncoated and coated graphite, with particular focus on how solid electrolyte interphase (SEI) formation and evolution deplete cyclable lithium, increase interfacial resistance, and induce polarization that leads to lithium plating and dendritic growth during rapid charging and low-temperature operation. Electrolyte and solvation engineering are highlighted as coating-free strategies to mitigate these issues by reducing Li⁺ desolvation barriers and directing interphase chemistry toward thinner, more ion-conductive, fluorinated SEI films that inhibit plating while maintaining high-rate capability. Coated graphite approaches are compared, including carbon, inorganic, and polymer coatings that function as artificial SEI layers to minimize direct electrolyte contact, stabilize interphase composition, and enhance mechanical durability. Key trade-offs are discussed, including decreased first-cycle coulombic efficiency (FCCE) due to increased surface area, transport limitations arising from excessively thick coatings, nonuniform coverage leading to local current hotspots, and side reactions induced by the coatings. The discussion is further extended to sodium and potassium systems, explaining how larger ion sizes, unfavorable thermodynamics, and significant lattice expansion hinder their insertion into graphite, and summarizing strategies such as interlayer expansion and alternative carbon architectures that improve reversibility for larger ions. This review concludes that achieving durable, safe, and fast-charging graphite electrodes requires an integrated interfacial design that combines optimized graphite morphology, electrode architecture, and electrolyte chemistry. Full article

(This article belongs to the Section Battery Materials and Interfaces: Anode, Cathode, Separators and Electrolytes or Others)

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18 pages, 3915 KB

Open AccessArticle

State of Health Estimation for Lithium-Ion Batteries Based on Alternating Electrical Signals Within a Specific Frequency Range

by Bo Rao, Jinqiao Du, Jie Tian, Weige Zhang, Xinyuan Fan and Tianrun Yu

Batteries 2026, 12(5), 153; https://doi.org/10.3390/batteries12050153 - 24 Apr 2026

Abstract

State of Health (SOH) estimation of lithium-ion batteries is a critical and challenging requirement in advanced battery management technologies. As an important parameter, battery impedance contains significant electrochemical information that can reflect the state of health of batteries. In this study, a SOH [...] Read more.

State of Health (SOH) estimation of lithium-ion batteries is a critical and challenging requirement in advanced battery management technologies. As an important parameter, battery impedance contains significant electrochemical information that can reflect the state of health of batteries. In this study, a SOH estimation method is proposed based on alternating electrical signals. First, an aging test was carried out using commercial 18650-type batteries. Considering the current uncertainty in practical applications, tests under different discharge conditions were conducted to obtain the capacity and wide frequency band impedance data of each battery throughout its life cycle. Then, important features at specific frequencies were extracted from the impedance data, and an interpretable analysis of the features was performed using the distribution of relaxation times (DRTs). Finally, the impedance features were combined with the Gaussian process regression algorithm in machine learning to estimate and validate the SOH. The results show that using fixed-frequency impedance features can achieve accurate estimation. The average value of the maximum absolute error of each battery under different working conditions can be controlled within 1.59%. With the development of embedded chips and online measurement technology, battery management systems can obtain important impedance features by applying alternating electrical signals within a certain frequency range, thus achieving online estimation of SOH. Full article

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

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46 pages, 4530 KB

Open AccessReview

Progress in Flexible and Wearable Power Sources

by Mervat Ibrahim and Hani Nasser Abdelhamid

Batteries 2026, 12(5), 152; https://doi.org/10.3390/batteries12050152 - 24 Apr 2026

Abstract

The demand for flexible and wearable electronics has intensified the need for conformable, high-performance, and self-sustaining power sources. Flexible supercapacitors (FSCs) and flexible batteries (e.g., lithium-ion and lithium–sulfur) are promising owing to their high-power density, long cycle life, and mechanical flexibility. A transformative [...] Read more.

The demand for flexible and wearable electronics has intensified the need for conformable, high-performance, and self-sustaining power sources. Flexible supercapacitors (FSCs) and flexible batteries (e.g., lithium-ion and lithium–sulfur) are promising owing to their high-power density, long cycle life, and mechanical flexibility. A transformative solution lies in integrating these storage devices with mechanical energy harvesters, particularly triboelectric nanogenerators (TENGs), to create autonomous self-charging power systems (SCPSs). TENGs exhibit high output, versatile operational modes, material flexibility, and efficient energy harvesting from body movements. This review provides an overview of the recent advances in flexible energy storage technologies, encompassing carbon-based materials, MXenes, polymers, metal oxides, metal–organic frameworks (MOFs), and their hybrid architectures. It discusses the synergistic integration of these storage devices with TENGs to realize multifunctional SCPSs. It also highlights the fundamental design principles of flexible devices, the critical interplay of materials and architecture, and the journey towards monolithic system integration. The review also underscores the importance of managing harvesters’ pulsed output for efficient storage. Finally, a critical analysis of the challenges, including the energy density–flexibility compromise, environmental stability, and safety, is presented, alongside a forward-looking perspective on commercialization pathways for these technologies to power the next generation of autonomous wearable and sustainable electronic systems. Full article

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24 pages, 5012 KB

Open AccessArticle

Operando Mechanochemical Evolution of Cylindrical 18650 NMC Lithium-Ion Cell Under Progressive High-Rate and Deep-Discharge Conditions Using Fiber Bragg Grating Sensing

by Aung Ko Ko, Zungsun Choi and Jaeyoung Lee

Batteries 2026, 12(5), 151; https://doi.org/10.3390/batteries12050151 - 24 Apr 2026

Abstract

Operando mechanical behavior of lithium-ion batteries under aggressive conditions remains insufficiently quantified, especially under combined high-rate and deep-discharge operation. This study investigated strain evolution in a commercial 18650 NMC lithium-ion cell using surface-mounted fiber Bragg grating sensors across 20 sequential conditions combining five [...] Read more.

Operando mechanical behavior of lithium-ion batteries under aggressive conditions remains insufficiently quantified, especially under combined high-rate and deep-discharge operation. This study investigated strain evolution in a commercial 18650 NMC lithium-ion cell using surface-mounted fiber Bragg grating sensors across 20 sequential conditions combining five discharge rates (1–4.5 C) and four cutoff voltages (2.5–1.0 V). All tests were performed on a single cell using identical 0.5 C constant-current constant-voltage charging, followed by a 2 h rest period and controlled discharge, to systematically evaluate mechanochemical evolution with increasing electrochemical severity. Maximum tensile strain during charging ranged from 45 to 59 µε and showed limited sensitivity to discharge severity. In contrast, discharge behavior exhibited clear rate- and cutoff-dependent transitions from tensile to compressive deformation; the most severe condition (4.5 C, 1.0 V cutoff) produced a peak compressive strain of about −27 µε and the most negative residual strain after relaxation. Although temperature increased monotonically with C-rate, strain evolution was nonlinear and non-monotonic, indicating that electrochemically induced stress dominated over thermal expansion alone. These findings reveal progressive amplification of irreversible deformation under severe discharge and demonstrate the value of fiber Bragg grating sensing for operando assessment of electrochemical–mechanical coupling in cylindrical lithium-ion cells. Full article

(This article belongs to the Special Issue Toward Next-Generation Rechargeable Lithium-Ion Batteries: Current Status and Future Prospects—2nd Edition)

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25 pages, 2026 KB

Open AccessArticle

Fractional-Order Degradation Modeling for Lithium-Ion Batteries with Robust Identification and Calibrated Uncertainty Under Cross-Cell Transfer

by Julio Guerra, Jairo Revelo, Cristian Farinango, Luis González and Gerardo Collaguazo

Batteries 2026, 12(5), 150; https://doi.org/10.3390/batteries12050150 - 23 Apr 2026

Abstract

Accurate and trustworthy prediction of lithium-ion battery aging remains challenging due to multi-mechanistic degradation, cell-to-cell variability, and distribution shift between laboratory calibration and deployment. Fractional-order models have been proposed to capture long-memory effects in electrochemical systems; however, it remains unclear when such memory [...] Read more.

Accurate and trustworthy prediction of lithium-ion battery aging remains challenging due to multi-mechanistic degradation, cell-to-cell variability, and distribution shift between laboratory calibration and deployment. Fractional-order models have been proposed to capture long-memory effects in electrochemical systems; however, it remains unclear when such memory is empirically identifiable and beneficial within the common prognostics abstraction of state-of-health (SOH) versus cycle index. This work develops a fully reproducible computational pipeline for mechanistic battery aging based on a Caputo fractional differential equation (FDE) and evaluates its cross-cell generalization on open NASA cycling data. Parameters are identified using bounded robust nonlinear least squares and validated under a strict transfer protocol: calibration on cells B0005/B0006 and evaluation on held-out cells B0007/B0018 without refitting. The fractional model is benchmarked against a classical ODE surrogate, an ECM-inspired resistance-proxy baseline, and one-step-ahead machine-learning predictors. Uncertainty quantification is performed via parameter bootstrap and subsequently calibrated using conformal correction to target nominal coverage under transfer. Results show that the fractional order tends to collapse toward the integer-order limit (α → 1) in this dataset, indicating limited evidence of additional long-memory at the SOH-versus-cycle level under the considered protocol, while robust identification remains essential for stability. Calibrated prediction intervals achieve near-nominal coverage on held-out cells, highlighting the importance of UQ calibration under cell-to-cell shift. The proposed scripts and environment specifications enable direct replication and facilitate future extensions to stress-aware fractional models and hybrid physics–ML approaches. Full article

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29 pages, 2247 KB

Open AccessArticle

Physics-Informed and Explainable Machine Learning for State-of-Health Estimation of Second-Life Lithium-Ion Batteries Under Sparse Cycling

by Md Sabbir Hossen, Md Tanjil Sarker, Gobbi Ramasamy and Ngu Eng Eng

Batteries 2026, 12(5), 149; https://doi.org/10.3390/batteries12050149 - 23 Apr 2026

Abstract

Reliable state-of-health (SOH) estimation is a key prerequisite for the safe and effective reuse of second-life lithium-ion batteries. However, practical assessment during early-stage screening is often constrained by extremely limited cycling data, where only a few discharge cycles are available due to time [...] Read more.

Reliable state-of-health (SOH) estimation is a key prerequisite for the safe and effective reuse of second-life lithium-ion batteries. However, practical assessment during early-stage screening is often constrained by extremely limited cycling data, where only a few discharge cycles are available due to time and cost limitations. This study investigates SOH estimation under an extreme sparse-cycling scenario in which only three discharge cycles per battery are available, reflecting realistic constraints in early-stage second-life battery screening. Under such severe data limitations, conventional data-driven models become unreliable, motivating the need for data-efficient and interpretable approaches. To address this challenge, a physics-aware and explainable machine learning framework is proposed, integrating physically interpretable feature extraction with lightweight regression models and Shapley Additive exPlanations SHAP-based interpretability analysis. Electrochemically motivated and mathematically derived features are extracted from voltage, current, and capacity measurements to ensure robustness under severe data scarcity. Multiple model classes, including linear regression, support vector regression, tree-based ensembles, and deep learning architectures, are systematically evaluated to assess their suitability in this constrained regime. Experimental results on real second-life battery datasets demonstrate that physics-aware linear models provide stable and interpretable SOH estimates under extreme data sparsity, whereas more complex nonlinear and deep learning models exhibit higher variability due to insufficient training data. These findings highlight that model suitability is strongly dependent on data availability and support the adoption of interpretable, physics-aware approaches for early-stage second-life battery screening rather than long-term degradation modeling. Full article

(This article belongs to the Special Issue Battery Degradation: Behavior, Mechanisms, Modeling, Estimation, and Optimization Strategies)

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17 pages, 2827 KB

Open AccessArticle

Ionowaxes on Porous Polymer Supports Form Cheap, Robust and Exquisitely Selective Proton-Conducting Membranes

by Ro L. Dunlop, Thomas J. Grummitt, Joel C. Schuurman and Deborah L. Crittenden

Batteries 2026, 12(4), 148; https://doi.org/10.3390/batteries12040148 - 21 Apr 2026

Abstract

Redox-flow batteries are a promising emerging technology for large-scale storage of renewable energy. However, existing ion-exchange membranes used for separating electrolytes are expensive and often ineffective at preventing crossover of redox-active species, leading to a decrease in battery capacity over time. Herein, we [...] Read more.

Redox-flow batteries are a promising emerging technology for large-scale storage of renewable energy. However, existing ion-exchange membranes used for separating electrolytes are expensive and often ineffective at preventing crossover of redox-active species, leading to a decrease in battery capacity over time. Herein, we introduce a new class of proton-conducting membranes formed by depositing highly alkylated waxy hydrophobic salts on porous polypropylene supports and demonstrate that they form self-assembled nanostructures which exclusively conduct protons via a unique mechanism of action. These new “ionowax” membranes display comparable proton conductivities to existing commercially available functionalized porous polymer membranes but are cheaper and easier to fabricate. We anticipate that these new membranes will facilitate future development of cheaper and/or longer-lasting aqueous redox-flow batteries. Full article

(This article belongs to the Section Battery Materials and Interfaces: Anode, Cathode, Separators and Electrolytes or Others)

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