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Batteries
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Advances in Lithium-Ion Battery Safety and Fire: 2nd Edition
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Advances in Lithium-Ion Battery Safety and Fire: 2nd Edition

A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Energy Storage System Aging, Diagnosis and Safety".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 9330

Special Issue Editors

Dr. Zhi Wang

E-Mail Website
Guest Editor
School of Safety Engineering, China University of Mining and Technology, Xuzhou 221100, China
Interests: battery safety; battery recycling; energy storage safety; process safety; thermal runaway; new energy explosions; hydrogen leakage and dispersion; fire; explosion; monitoring and early warning; fire suppression; absorption of toxic gases; thermal analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Tao Zhu

E-Mail Website
Guest Editor
School of Electrical Engineering, Beijing Jiaotong University, Beijing 100091, China
Interests: battery parameterization and modeling; thermal management; hybrid energy storage system; electric vehicle modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Qi Zhang

E-Mail Website
Guest Editor
College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Interests: industrial explosion and prevention; thermal runaway safety; early warning and prevention for lithium batteries; fire and explosion prevention in energy storage power stations
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Lithium-ion batteries have become one of the most competitive energy storage media for electric vehicles, energy storage power stations, novel energy storage systems, and so on. The safety issues associated with batteries, including thermal runaway, thermal runaway propagation, ageing degradation, fire and explosion, have caused widespread concern. These issues have not been satisfactorily unveiled and resolved. To this end, this Special Issue focuses on advances in the fundamental science and key technologies for thermal safety and management with regard to the related fire and explosion of batteries, including mechanisms, modelling, characteristics, monitoring, control, standard, etc.

Potential topics include, but are not limited to, the following:

  • Intrinsic design for battery safety (flame retardant electrolyte, self-closing separator, high stability electrode, etc.);
  • Insights into thermal runaway/propagation mechanisms and numerical modelling analysis;
  • Advanced thermal management strategies;
  • Multi-scale battery fire tests (cell, module, vehicle, energy storage station, etc.);
  • Process safety and emergency disposal of batteries during transportation;
  • Ageing mechanisms, diagnostic method and regulation measures under different paths;
  • Characteristics and evaluation of battery fire and explosion;
  • Detection, monitoring and early warning of battery thermal runaway and fire;
  • Explosion suppression and fire extinguishing involving battery fire;
  • Safety standards for battery production, storage, transportation, and usage processes;
  • Fire safety theory, technology, and equipment for battery storage, transportation, and recycling processes;
  • Fire risk assessment of full-size lithium-ion battery (sodium ion battery) energy storage system.

We are delighted to invite you to publish an original research paper or a review paper in this Special Issue. Share your results to enhance the safety of batteries.

Dr. Zhi Wang
Dr. Tao Zhu
Dr. Qi Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Batteries is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • safety battery
  • thermal runaway
  • fire and explosion
  • thermal propagation
  • thermal management
  • battery ageing
  • multi-scale test and modelling
  • monitoring and early waring
  • fire detection
  • fire extinguishing
  • explosion suppression
  • accident investigation
  • safety standards and guidelines

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (6 papers)

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Research

21 pages, 4626 KB  
Article
Thermally Aware Design of Large-Format Batteries Driven by an Equivalent Circuit Network-Based Electro-Thermal Model
by Junlong Niu, Hua Tang, Hongwei Li, Caiping Zhang, Linjing Zhang, Bingxiang Sun, Kai Gao, Tong Li and Tao Zhu
Batteries 2026, 12(2), 47; https://doi.org/10.3390/batteries12020047 - 30 Jan 2026
Viewed by 647
Abstract
Large-format pouch cells enable higher pack-level energy density and simplified system architecture, yet they pose significant thermal challenges due to long internal conduction paths, pronounced spatial gradients, and limited access to core temperature. This work develops a high-fidelity electro-thermal model for large-format cells [...] Read more.
Large-format pouch cells enable higher pack-level energy density and simplified system architecture, yet they pose significant thermal challenges due to long internal conduction paths, pronounced spatial gradients, and limited access to core temperature. This work develops a high-fidelity electro-thermal model for large-format cells based on an equivalent circuit network that mirrors the physical assembly of tabs, welds, and electrode stacks. The model couples three-dimensional ohmic conduction in tabs, welds, and current collectors with node-level equivalent circuit models in the stack, and uses measurement-anchored parameters. The model is used to study thermally critical design factors for a 44 Ah pouch cell, including thermal management configurations, tab width, tab thickness, and tab welding. Simulation results indicate that among four active cooling options, two-sided stack surface cooling achieves the lowest temperatures and the best uniformity, lowering the average temperature by about 11 °C relative to natural convection and reducing the temperature standard deviation to 1.43 °C. It also decreases the core maximum temperature by more than 9 °C, whereas other configurations provide only 4 to 5 °C core reductions. Changes to tab geometry and welding have minor effects except under one-sided tab cooling. Full article
(This article belongs to the Special Issue Advances in Lithium-Ion Battery Safety and Fire: 2nd Edition)
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19 pages, 3222 KB  
Article
State of Health Estimation for Energy Storage Batteries Based on Multi-Condition Feature Extraction
by Wentao Tang, Xun Liu, Xiaohang Li, Jiangxue Shen, Zhiyuan Liao and Minming Gong
Batteries 2026, 12(1), 34; https://doi.org/10.3390/batteries12010034 - 21 Jan 2026
Viewed by 734
Abstract
In the field of energy transformation, the application of batteries is widening. To address the challenge of health state estimation of energy storage batteries with multiple operating conditions, this study analyzes the aging cycle operation data of lithium-ion batteries and develops a scheme [...] Read more.
In the field of energy transformation, the application of batteries is widening. To address the challenge of health state estimation of energy storage batteries with multiple operating conditions, this study analyzes the aging cycle operation data of lithium-ion batteries and develops a scheme to extract a number of raw features and their corresponding health status labels. Multidimensional candidate feature sets that capture aging information under different conditions are constructed. Subsequently, a three-stage feature selection strategy, including Pearson and Spearman correlation analysis, hierarchical redundancy elimination, and minimum redundancy maximum relevance, was applied to screen the candidate feature set of each condition, resulting in customized feature sets with condition adaptability. By analyzing the occurrence frequency and mean absolute correlation coefficient of each feature within the custom feature set, a comprehensive feature set with multi-condition adaptability was screened and determined. On this basis, by integrating temporal sequence information and operating condition information, a dual-path fusion estimation model with attention mechanism and condition modulation was established. The validation results of the lithium-ion battery multi-condition cycling aging dataset demonstrate that the model achieves accurate health state estimation, with mean absolute error and root mean square error of 0.8281% and 0.9835%, respectively. Finally, comparisons with other methods were conducted in terms of feature selection strategies and model estimation performance. The results demonstrate that the proposed approach achieves superior estimation accuracy and enhanced interpretability. Full article
(This article belongs to the Special Issue Advances in Lithium-Ion Battery Safety and Fire: 2nd Edition)
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19 pages, 3106 KB  
Article
A Multi-Physics Coupled Model for Elucidating Expansion in Si–C Composite Anode Lithium-Ion Batteries
by Hao-Teng Li, Xue Li, Xiao-Ying Ma, Kai Yang, Jintao Shi, Xingcun Fan, Zifeng Cong, Xiaolong Feng, Keliang Wang and Xiao-Guang Yang
Batteries 2025, 11(11), 423; https://doi.org/10.3390/batteries11110423 - 17 Nov 2025
Cited by 1 | Viewed by 2335
Abstract
Silicon–carbon (Si–C) composite anodes are a promising pathway to enhance the energy density of lithium-ion batteries (LIBs), yet the substantial volume changes of silicon during (de)lithiation cause mechanical degradation, capacity fading, and safety risks that hinder practical use. To address these challenges, we [...] Read more.
Silicon–carbon (Si–C) composite anodes are a promising pathway to enhance the energy density of lithium-ion batteries (LIBs), yet the substantial volume changes of silicon during (de)lithiation cause mechanical degradation, capacity fading, and safety risks that hinder practical use. To address these challenges, we develop an electrochemical–thermal–mechanical coupled model tailored for LIBs with Si–C anodes. Built upon the Newman pseudo-two-dimensional framework, the multi-scale model integrates particle-, electrode-, and cell-level submodels. Electrochemical–mechanical coupling is captured through intercalation-induced particle expansion and cell-level thermal expansion, while bidirectional electrochemical–thermal coupling is introduced via a lumped thermal model with temperature-dependent electrochemical kinetics. The model is validated against experimental data, accurately reproducing current–voltage profiles, temperature rise, and displacement under various operating conditions. Simulations further reveal the distinct contributions of silicon and graphite: although silicon accounts for only a small fraction of anode mass, it can contribute 30% to the capacity of the cell owing to the high specific capacity of Si. At the same time, while silicon particles undergo volume changes exceeding 300%, the overall cell expansion remains below 7.5% due to structural dilution effects from other components. These findings establish a quantitative link between silicon content, electrochemical behavior, and cell expansion, providing theoretical guidance for the rational design of high-energy-density LIBs. Full article
(This article belongs to the Special Issue Advances in Lithium-Ion Battery Safety and Fire: 2nd Edition)
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21 pages, 4246 KB  
Article
Expansion Pressure as a Probe for Mechanical Degradation in LiFePO4 Prismatic Batteries
by Shuaibang Liu, Xue Li, Jinhan Li, Jintao Shi, Xingcun Fan, Zifeng Cong, Xiaolong Feng, Haoteng Li, Wenwei Wang, Jiuchun Jiang and Xiao-Guang Yang
Batteries 2025, 11(11), 391; https://doi.org/10.3390/batteries11110391 - 23 Oct 2025
Viewed by 1901
Abstract
Battery mechanical properties degrade progressively with aging, manifesting as expansion pressure in module-constrained cells. Here, an in situ pressure operating system was developed to replicate the mechanical environment of lithium iron phosphate (LFP) prismatic batteries, enabling long-term monitoring under different loads and temperatures. [...] Read more.
Battery mechanical properties degrade progressively with aging, manifesting as expansion pressure in module-constrained cells. Here, an in situ pressure operating system was developed to replicate the mechanical environment of lithium iron phosphate (LFP) prismatic batteries, enabling long-term monitoring under different loads and temperatures. Coupled with quasi-static compression tests on internal components, stress–strain curves and elasticity moduli were obtained to link microscopic behavior with macroscopic pressure response. Results show that irreversible pressure growth is jointly governed by state of health (SOH) and load: under low-load conditions, irreversible pressure increases nonlinearly with SOH, whereas higher loads yield more linear trends. A multilevel physical model encompassing electrodes, cells, and modules was proposed to explain these behaviors. This model takes into account the influence of external pressure on the modulus of the battery, and indicates that SOH and load influence reversible pressure curves through their effect on modulus. A theoretical method was derived to calculate in-module modulus, confirming its linear correlation with the fluctuation amplitude of reversible pressure. Differential pressure-capacity analysis further demonstrated that characteristic changes in expansion pressure reflect modulus evolution, and deviations from this relationship reveal degradation pathways such as gas generation, solid electrolyte interphase (SEI) growth, or lithium plating. This study establishes pressure signals as mechanistic indicators of modulus evolution and provides a framework for diagnosing mechanical degradation in batteries. Full article
(This article belongs to the Special Issue Advances in Lithium-Ion Battery Safety and Fire: 2nd Edition)
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21 pages, 2547 KB  
Article
Remaining Available Energy Prediction for Energy Storage Batteries Based on Interpretable Generalized Additive Neural Network
by Ji Qi, Pengrui Li, Yifan Dong, Zhicheng Fu, Zhanguo Wang, Yong Yi and Jie Tian
Batteries 2025, 11(7), 276; https://doi.org/10.3390/batteries11070276 - 20 Jul 2025
Cited by 1 | Viewed by 1181
Abstract
Precise estimation of the remaining available energy in batteries is not only key to improving energy management efficiency, but also serves as a critical safeguard for ensuring the safe operation of battery systems. To address the challenges associated with energy state estimation under [...] Read more.
Precise estimation of the remaining available energy in batteries is not only key to improving energy management efficiency, but also serves as a critical safeguard for ensuring the safe operation of battery systems. To address the challenges associated with energy state estimation under dynamic operating conditions, this study proposes a method for predicting the remaining available energy of energy storage batteries based on an interpretable generalized additive neural network (IGANN). First, considering the variability in battery operating conditions, the study designs a battery working voltage threshold that accounts for safety margins and proposes an available energy state assessment metric, which enhances prediction consistency under different discharge conditions. Subsequently, 12 features are selected from both direct observation and statistical characteristics to capture the operating condition information of the battery, and a dataset is constructed using actual operational data from an energy storage station. Finally, the model is trained and validated on the feature dataset. The validation results show that the model achieves an average absolute error of 2.39%, indicating that it effectively captures the energy variation characteristics within the 0.2 C to 0.6 C dynamic current range. Furthermore, the contribution of each feature is analyzed based on the model’s interpretability, and the model is optimized by utilizing high-contribution features. This optimization improves both the accuracy and runtime efficiency of the model. Finally, a dynamic prediction is conducted for a discharge cycle, comparing the predictions of the IGANN model with those of three other machine learning methods. The IGANN model demonstrates the best performance, with the average absolute error consistently controlled within 3%, proving the model’s accuracy and robustness under complex conditions. Full article
(This article belongs to the Special Issue Advances in Lithium-Ion Battery Safety and Fire: 2nd Edition)
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Graphical abstract

23 pages, 5743 KB  
Article
Impact of Low-Pressure in High-Altitude Area on the Aging Characteristics of NCM523/Graphite Pouch Cells
by Xiantao Chen, Zhi Wang, Jian Wang, Yichao Lin and Jian Li
Batteries 2025, 11(7), 261; https://doi.org/10.3390/batteries11070261 - 13 Jul 2025
Cited by 1 | Viewed by 1725
Abstract
With the development and application of electric vehicles powered by lithium-ion batteries (LIBs) at high altitude, the lack of research on the aging laws and mechanisms of LIBs under a low-pressure aviation environment has become an important obstacle to their safe application. Herein, [...] Read more.
With the development and application of electric vehicles powered by lithium-ion batteries (LIBs) at high altitude, the lack of research on the aging laws and mechanisms of LIBs under a low-pressure aviation environment has become an important obstacle to their safe application. Herein, the influences and mechanisms of high-altitude and low-pressure environment (50 kPa) on the cycling performance of commercial pouch LIBs were systematically studied. The results showed that low air pressure caused a sharp decrease in battery capacity to 46.6% after 200 cycles, with a significant increase in charge transfer impedance by 70%, and the contribution rate of active lithium loss reached 74%. Low air pressure led to irreversible deformation of the battery, resulting in the expansion of the gap between the electrodes, poor electrolyte infiltration, and reduction of the effective lithium insertion area, which in turn induced multiple synergistic accelerated decay mechanisms, including obstructed lithium-ion transmission, reduced interfacial reaction efficiency, increased active lithium consumption, changes in heat generation structure, and a significant increase in heat generation. After applying external force, the deformation of the electrode was effectively suppressed, and the cycle capacity retention rate increased to 87.6%, which significantly alleviated the performance degradation of LIBs in low pressure environment. This work provides a key theoretical basis and engineering solutions for the design of power batteries in high-altitude areas. Full article
(This article belongs to the Special Issue Advances in Lithium-Ion Battery Safety and Fire: 2nd Edition)
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