Batteries

23 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

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

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

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

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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37 pages, 4605 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

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

26 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

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

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

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

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 (registering DOI) - 6 Jul 2026

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

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

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

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

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

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

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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21 pages, 15634 KB

Open AccessArticle

Simulation Study on Battery Rack for Electric Vessels Under Accelerations in Different Directions

by Xuan Wang, Yixi Zhao, Rui Yin, Yunsong Zhang, Yaoqi Feng, Qing Yan, Yijie Wang and Ling Chen

Batteries 2026, 12(7), 237; https://doi.org/10.3390/batteries12070237 - 30 Jun 2026

Abstract

With increasingly stringent global requirements on emission reduction and environmental protection in the maritime industry, lithium battery-powered ships have developed rapidly due to their near-zero emissions and low noise characteristics. However, the transition to power systems also introduces new safety challenges, particularly the [...] Read more.

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

(This article belongs to the Special Issue Advanced Architecture and Intelligent Thermal Management of Battery Systems)

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18 pages, 615 KB

Open AccessArticle

Early-Cycle Lifetime Prediction of Lithium-Ion Batteries with Ultra-Short Cycle Life Using Transferable Statistical Features

by Yuxiang Kuang, Dongxu Guo and Yuejiu Zheng

Batteries 2026, 12(7), 236; https://doi.org/10.3390/batteries12070236 - 29 Jun 2026

Abstract

Early-cycle lifetime prediction of lithium-ion batteries is important for rapid cell screening, battery development, and manufacturing quality control. However, accurate prediction at the early stage remains difficult because capacity fade is usually very limited during the initial cycles, and the available degradation signals [...] Read more.

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

(This article belongs to the Special Issue Advanced Architecture and Intelligent Thermal Management of Battery Systems)

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19 pages, 11919 KB

Open AccessArticle

Study on the Thermal Runaway Mechanism of Lithium-Ion Batteries Induced by External Short Circuit Under Mechanical Stress State

by Yong Ding, Ruixin Jia, Zhongzheng Huang and Zhoujian An

Batteries 2026, 12(7), 235; https://doi.org/10.3390/batteries12070235 - 29 Jun 2026

Abstract

The pouch cells are typically assembled into modules with mechanical preload to meet voltage/capacity requirements, and the stress state is a critical factor influencing the failure behavior of lithium-ion batteries during external short circuits. This study comparatively analyzes performance differences between mechanically preloaded [...] Read more.

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

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

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