Heat sinks are essential cooling components in the battery thermal management systems (BTMS). Porous fin microchannel heat sinks can achieve high heat transfer rates in confined spaces, offering significant potential for practical applications. In this study, a modified-porous fin microchannel heat sink for BTMS is numerically simulated to examine its fluid dynamics and thermal exchange properties. By partially and uniformly filling metal foam in solid fins, the temperature is reduced, the Nusselt number is increased, and the comprehensive performance is enhanced. Compared with solid fins, the modified design is shown to yield a maximum Nusselt number improvement of 153.6%, accompanied by a peak performance evaluation coefficient reaching 1.92. Thermal analysis is conducted by considering both structural optimization and coolant flow behavior. Effects of metal foam filling width and height are investigated. The fluid dynamics and thermal exchange properties of the modified structure

Zhang, Liyuan, Lai, Huanxin

Fault Diagnosis Method of Lithium-Ion Batteries Based on OCSVM and RLMQD Algorithms

2026-01-7020

2/27/2026

To address the challenges of recognizing abnormal states, detecting subtle early warning signs, and quantifying fault severity in scenarios involving simultaneous multiple faults in lithium-ion batteries, this study proposes a dual-layer fault diagnosis framework that integrates One-Class Support Vector Machine (OCSVM) and Robust Local Mahalanobis Distance Quantile (RLMQD) algorithm. First, a three-dimensional multi-scale feature space, incorporating voltage, kurtosis, and voltage change rate, is constructed to detect abnormal battery states via OCSVM and dynamically filter abnormal time periods with improved adaptability. Second, a computationally efficient RLMQD-based quantization algorithm is developed, which employs a small-scale sliding window and adaptively selects healthy cells to construct reference distributions. By incorporating low-quantile thresholds, the algorithm enhances early abnormality detection and significantly reduces false positives. Subsequently, fault severity

Wei, Fuxing, Yang, Libing, Wang, Zonglei, Xia, Xuelei, Shen, Jiangwei, Chen, Zheng

Data-Driven Capacity Estimation Method for Retired Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy

2026-01-7015

2/27/2026

With the rapid expansion of global electric vehicles (EVs) deployment, the echelon utilization of retired lithium-ion batteries (LIBs) has emerged as a critical issue. Although these batteries typically retain over 70% of their initial capacity and remain suitable for stationary energy storage systems, the substantial variability in aging states poses safety risks. Conventional capacity estimation methods are often time-intensive and costly, while data-driven approaches face challenges from complex degradation mechanisms and limited historical usage data. This study uses the electrochemical impedance spectroscopy (EIS) method to create a model that estimates the capacity of retired batteries. EIS offers fast measurement, requires no historical cycling data, and provides rich state-of-health (SOH) information. An EIS dataset was acquired from 18650-type LFP and NCM cells aged under multiple cycling conditions. The real part and magnitude of the impedance spectra were extracted as input

Hou, Zhengyu, Luan, Weiling, Sun, Changzheng, Chen, Ying

Design and Performance Study of Highly Reliable Marine Battery Based on Solid Polymer Electrolyte

2026-01-7035

2/27/2026

Currently, electric propulsion is playing an increasingly important role in marine propulsion systems.Lithium metal batteries are new-generation high-performance energy storage system with development prospect. Traditional flammable and volatile organic liquid electrolytes pose a risk of thermal runaway, while solid-state lithium metal batteries using solid electrolytes have significant advantages in energy density and safety, and are considered the most promising mobile power sources. Among numerous solid electrolyte systems, polymer solid electrolytes have excellent flexibility, good interface compatibility, and good processing characteristics, which have attracted the attention of researchers. Polyurethane (PU) is a common polymer with high mechanical strength and a flexible and adjustable molecular structure, making it one of the best choices for polymer electrolyte matrices. Based on the structural design of polyurethane polymers, this paper explores polycaprolactone type

Yuan, Meng, Tang, Qing, Yu, Gongye

Study on the Evolution of Precise Internal Short Circuit Damage in Li-Ion Batteries

2026-01-7025

2/27/2026

One primary cause of NEV fires is thermal runaway initiated by internal short circuit in power batteries, leading to subsequent thermal diffusion throughout the battery system. Severe internal short circuit damage can precipitate thermal runaway phenomena in lithium-ion batteries, potentially culminating in fire incidents involving electric vehicles. Although mild internal short circuit may not immediately induce thermal runaway, continuous charge and discharge cycling can exacerbate such conditions, progressively elevating risks associated with thermal runaway and other pertinent safety hazards. Conventional safety testing methodologies, employing techniques such as crushing and nail penetration to simulate internal short circuit, often amplify the extent of these shorts and fail to accurately replicate less severe, deeper internal short circuit. Additionally, methods incorporating foreign objects like nickel pieces for simulating internal short circuit necessitate battery disassembly

Sun, Zhipeng, Ma, Tianyi, Han, Ce, Wang, Fang, Ren, Gaohui

Research on State of Charge Estimation Method for Lithium Iron Phosphate Batteries Based on Expansion Force

2026-01-7040

2/27/2026

To enhance the accuracy and robustness of State of Charge (SOC) estimation for lithium iron phosphate (LiFePO₄) batteries and to overcome the limitations of traditional electrical signal-based methods—such as cumulative errors in Coulomb counting and the need for rest periods in open-circuit voltage (OCV) methods—this study proposes a novel SOC fusion estimation algorithm based on mechanical expansion force signals. Addressing the challenge of feature extraction, a model framework integrating the Sparrow Search Algorithm (SSA), Least Squares Support Vector Machine (LSSVM), and Adaptive Extended Kalman Filter (AEKF) is developed. The state equation is constructed via Coulomb counting, while SSA optimizes the LSSVM to establish an observation model centered on expansion force as the input. The AEKF is employed to achieve real-time, precise SOC prediction. Experimental validation under varying temperatures (25°C, 35°C) and dynamic driving cycles (FUDS, UDDS) demonstrate that this fusion

Du, Jinqiao, Rao, Bo, Tian, Jie, Wu, Yizeng, Xu, Haoming, Jiang, Jiuchun

Lithium Plating-Induced Surface Pressure Evolution in Lithium-Ion Pouch Cells under Rigid Constraints

2026-01-7013

2/27/2026

In practical applications, power cells face a mix of external influences such as temperature variations and structural limits (rigid constraints) that trigger intricate electrochemical and mechanical reactions. This study systematically explores the temporal evolution of surface pressure in lithium-ion pouch cells subjected to rigid mechanical constraints under varying thermal conditions, with a specific focus on the interplay among mechanical stress, lithium intercalation, and lithium plating. To investigate the battery’s electrochemical and mechanical responses, this work integrates experimental measurements with an electrochemical–mechanical coupling model. The analysis is performed under initial loads of 0.3, 0.5, and 1.0 MPa at 25 °C (ambient temperature) and 0 °C (representative low-temperature condition). At 25 °C, surface pressure followed a two-stage pattern: first, stress relaxation occurred, followed by a shift into quasi-steady cycling (cycle-to-cycle variations are minimal

Du, Yingyue, Chen, Ying, Luan, Weiling, Chen, Haofeng

Transient Modeling and PID-Controlled Nanofluid Cooling for Enhanced Thermal Management in Proton Exchange Membrane Fuel Cells

2026-01-7030

2/27/2026

Appropriate thermal management system is important for the lifespan and safety of proton exchange membrane fuel cells (PEMFCs). A comprehensive thermal management system for PEMFC was proposed through finite element model (FEM), control optimization and nanofluid cooling. An 0D-3D coupled thermal model for energy balance and local temperature field analysis was established. By coupling internal heat transfer dynamics with Proportional-Integral-Derivative (PID) control logic, the optimal parameter combination was determined as Kp=-1 m/(s⋅K), Ki=-0.1 m/(s2⋅K) and Kd=0 (m/K). Additionally, the nanofluid coolant revealed a concentration-dependent trade-off between enhanced thermal performance and decreased flow performance. In the range of 0-15% of the nanofluid concentration, the Reynolds number and pressure drop increase with the increase of the concentration of the nanofluid, while in the range of 16-20%, the Reynolds number decreases with the increase of the concentration of the

Zhang, Xiaoliang, Deng, Yikang, Zhao, Yanli, Wang, Qi, Luo, Shengfeng

Electro-Thermo-Mechanical Simulation and Machine Learning for Temperature Prediction of Lithium-Ion Batteries under Mechanical Abuse

2026-01-7041

2/27/2026

Lithium-ion battery safety under mechanical abuse has become a critical challenge with the widespread adoption of electric vehicles. This study proposes a predictive framework combining multi-physics finite element simulation and machine learning to estimate the temperature rise of lithium-ion cells under impact conditions. An Electro-Thermo-Mechanical (ETM) coupled model was established in LS-DYNA to simulate the effects of impactor radius, velocity, and ambient temperature on internal heat generation. Using a full factorial sampling design, 125 simulation scenarios were generated to extract maximum temperature data. These data were used to train and compare several regression models, including Support Vector Machines (SVM), Decision Trees (DT), Back Propagation Neural Networks (BPNN), and Random Forests (RF). A Stacking ensemble model integrating these base learners achieved the highest prediction accuracy, with an R2 of 0.996 and RMSE below 0.5. Performance remained robust even

Wan, Cheng, Zhan, Zhenfei, Chen, Qiuren

Thermal Runaway Characteristics of LMFP Hybrid Batteries at Different State of Charge

2026-01-7019

2/27/2026

Lithium-ion batteries (LIBs) have drawn substantial scientific interest because of their impressive energy storage capabilities and long-term operational stability. In recent years, new battery material systems have emerged, among which LMFP (LiMnxFe1−xPO4) is regarded as a promising candidate for future battery development, combining high energy density with enhanced safety. However, research on the thermal runaway (TR) behavior of LMFP-based batteries remains scarce, leaving their cell-level safety unverified. This study modifies the conventional state of charge (SOC) classification method by measuring the oxidation state of cathode materials at specific voltages. By testing the thermal runaway (TR) temperature and gas release characteristics of LMFP hybrid batteries under different voltage states, it reveals the influence of cathode oxidation state on TR behavior. The results demonstrate that when the NCM (LiNi₀.₅Co₀.₂Mn₀.₃O₂) component remains unoxidized, the battery does not

Guo, Zhenquan, Wu, Senming, Luan, Weiling, Chen, Ying, Chen, Haofeng

Joint Estimation of State of Charge and Capacity throughout the Full Service Life Cycle for Li-Ion Batteries

2026-01-7021

2/27/2026

Accurate SOC and capacity estimation is essential for the safe operation of lithium-ion batteries. However, model parameters drift due to temperature variations and aging. This study proposes a migration-model-based method for joint estimation of SOC and capacity over a wide range of temperatures and degradation levels. The WSPF algorithm identifies migration factors in real time and applies them to estimate SOC and capacity under nonlinear, non-Gaussian conditions. Validation under various test conditions demonstrates clear advantages. Compared to EKF, the migration-model-based algorithm reduces the maximum RMSE of SOC estimation to 0.55%. For capacity estimation, it achieves a maximum RMSE of 1.15%. The estimation accuracy remains high throughout temperature changes and aging, highlighting the robustness and applicability of the proposed method for real-world battery management systems.

Liu, Weiqiang, Chen, Zheng, Wei, Fuxing, Shen, Jiangwei

Risk of Safety Abuse Testing and Safety Environment Control Strategies for Lithium-Ion Batteries

2026-01-7008

2/27/2026

With the vigorous development and technological iteration of the new energy vehicle industry, the strategic position of inspection, certification, R&D and testing in the industrial chain has become increasingly prominent. As the core energy storage component of new energy vehicles, the potential safety risks and environmental hazards in the testing process of power batteries are particularly worthy of vigilance. Based on more than ten years of operational practice in battery laboratories, this paper summarizes experience and lessons in depth, focusing on problems such as smoke, fire, explosion and release of toxic and harmful substances caused by thermal runaway of batteries in lithium-ion battery safety abuse tests. From the dimensions of risk characteristics of safety abuse tests, laboratory security design, and laboratory environmental protection facilities, it systematically expounds the risk prevention and control strategies and environmental protection measures for lithium-ion

Ren, Gaohui, Liu, Lei, Jiang, Chenglong, Sun, Zhipeng, Chen, Liduo

A Pulsed-Charging-Current Heating Method for Lithium-Ion Batteries at Low Temperature

2026-01-7004

2/27/2026

Lithium-ion batteries suffer from capacity degradation, lifespan attenuation, and power decline at low temperatures. Alternating-pulsed-current (APC) heating method is an effective solution for improving the low-temperature performance of batteries, but it still faces challenges in terms of low heating efficiency and energy consumption. This work proposes a pulsed-charging-current (PCC) heating method to address these issues. The effect of the PCC under various conditions, including frequency and amplitude, is investigated through experiments. According to the experimental results, the battery can be heated from -20 °C to above 7.5 °C within 15 minutes using the proposed PCC method, with a heating rate of 1.83 °C/min. Compared with the traditional APC heating method, the heating rate of the PCC method increases by 7.9%. During the 15-minute heating process, the battery capacity increased by 131.9 mAh on average, and the charging efficiency can be achieved 95% above. The proposed method

Xiao, Yuechan, Huang, Xinrong, Wu, Ze, Zhang, Yipu, Meng, Jinhao

Rapid Battery RUL Prediction under Different Fast-Charging Protocols via Domain-Adaptive Deep Learning and Model Compression

2026-01-7003

2/27/2026

Accurate and rapid remaining useful life (RUL) prediction of batteries under various extreme conditions is crucial for battery management systems. However, existing methods often face challenges such as limited datasets under extreme conditions, high model complexity, and weak interpretability. Therefore, this paper proposes a hybrid framework based on pruning domain-adaptive convolutional neural networks (CNN) and long short-term memory (LSTM) to study RUL prediction under different fast-charging conditions using the MIT dataset. First, four voltage-related feature matrices are extracted. Using maximum mean discrepancy (MMD) constraints, the CNN-LSTM is trained with source domain and limited target domain data to align distributions. Neuron pruning is then applied to the fully connected layer to compress the model. Results demonstrate that under sparse target domain data, the domain adaptation approach achieves significantly lower prediction errors than fine-tuning. The pruned model

Huang, Mingyue, Chen, Hongxu, Luan, Weiling

A Hierarchical Fault Detection Method for Lithium-Ion Batteries in Real-World Vehicle Applications

2026-01-7006

2/27/2026

With the rapid expansion of the electric vehicle market, the safety of lithium-ion batteries, which serve as the main power source, has become a critical concern. Current mainstream methods for battery fault detection generally face a technical bottleneck of struggling to balance high accuracy with a low false alarm rate. Furthermore, constrained by algorithmic complexity and data processing efficiency, detection speeds often fail to meet the practical demands of real-time monitoring. As a result, developing more efficient and accurate fault detection technologies has emerged as a key challenge urgently needing to be addressed in the industry. This paper proposes a hierarchical fault detection framework for lithium-ion batteries that integrates voltage change characteristics with a Local Outlier Factor (LOF) scoring mechanism. The framework aims to achieve early identification and accurate diagnosis of abnormal battery states through multi-dimensional feature extraction and algorithmic

Gao, Zhengpeng, Gao, Pingping, Chang, Penghui, Liu, Gang, Wu, Ji

Real-Time Fault Diagnosis and Reliability Analysis in Lithium-Ion BESS via Bayesian Fault Propagation Networks

2026-01-7023

2/27/2026

The rapid integration of intermittent renewable energy sources (RES) poses significant operational challenges for modern power systems. Lithium-ion battery (LIB)–based battery energy storage systems (BESS) have become vital for grid stability and energy management. However, large-scale deployment of BESS has led to increasing incidents such as fires and explosions, raising serious concerns regarding their safety and reliability. To overcome the limitations of traditional reliability assessment methods—such as reliability block diagrams (RBD), fault tree analysis (FTA), and Markov models—this study proposes an integrated fault detection and reliability analysis framework that combines FTA, failure mode and effects analysis (FMEA), and a Bayesian Fault Propagation Network (BFPN). The framework systematically models fault propagation across component, subsystem, and system levels, dynamically updating the prior probabilities of basic failure events using a Gaussian Mixture Model (GMM) and

Yang, Zhan, Chen, Xiaobo, Zheng, Ruixiang, Li, Mian

Steel-Based Laminates for Lithium-ion Pouch Cell of Electric Vehicles

2026-26-0186

1/16/2026

Aluminum foils have gained traction with EV battery manufacturers for their pouch cell format. Over the years, it has evolved as a material of choice, but it is still plagued by the issues of stress concentration and swelling due to lower strength and lower stiffness of base aluminum layer. Preliminary investigation revealed that laminates using steel foil material (thickness < 0.1mm) could be a potential candidate for EV pouch cell casing. Thus, steel-based laminate was developed meeting key functional requirements (e.g., barrier performance, insulation resistance, peel strength, electrolyte resistance, formable without cracking at edges, and heat sealing compliant). This innovative patented steel-based laminate [1] was further used to manufacture pouch cell prototypes (up to a maximum capacity of 2.8Ah) for key performance evaluation (e.g., cell cycling and nail penetration). The study paves the way for a low cost, sustainable and flexible yet strong steel-based laminate packaging

Singh, Pundan Kumar, Raj, Abhishek, Kumar, Ankit, Chatterjee, Sourabh, Verma, Rahul Kumar, Samantaray, Bikash, Gautam, Vikas, Pandey, Ashwani

Enhancing EV Battery Safety: Progressive Crushing and Energy Absorption Technique

2026-26-0189

1/16/2026

A crash energy absorption technique and method improve the safety and structural integrity of electric vehicle battery packs during collisions, complying with global regulations. This analysis details an assembly featuring a battery housing for mounting battery cells, a crash member connected to the battery housing's periphery, and flexural members linked to the crash member. The flexural members are designed to absorb impact forces by deforming and storing potential energy during sudden impacts. This approach ensures energy is stored within the flexural elements and then transferred to the battery cells through progressive crushing. The design effectively delays intrusion, enhances battery safety, and minimizes cell-level damage. This solution improves occupant safety and prevents thermal runaway incidents while maintaining the battery's overall performance and reliability in EVs.

Amberkar S, Sunil, Lakshman singh, Meenakumari, Bodaindala, Anil Kumar

Comprehensive Insights on Techniques for Managing and Preventing Thermal Runaway in EV Lithium-Ion Batteries

2026-26-0171

1/16/2026

This paper presents a comprehensive investigation into the mechanisms, risks, and mitigation strategies associated with thermal runaway in lithium-ion batteries used in electric vehicles (EVs). It begins by emphasizing the urgency of the issue, identifying key vulnerabilities within EV battery systems that contribute to runaway events. A multiscale, stage-wise breakdown of thermal runaway progression is provided, illustrating how physical, chemical, and thermal interactions compound during failure scenarios. The study analyzes global incident data from 2000 to 2025, revealing trends in human health impacts, vehicle damage, and public safety concerns. Particular attention is given to how battery aging, manufacturing defects, and external abuse conditions elevate the likelihood and severity of thermal runaway. Current emergency response protocols and state-of-the-art mitigation technologies are critically evaluated to identify best practices and existing gaps in safety management. A

Jain, Gaurav, Premlal, P, Pathak, Rahul, Gore, Pandurang

Conceptual Framework for Real-Time Battery Health Estimation in Automotive Digital Twins Using Reinforcement Learning

2026-26-0658

1/16/2026

The explosive growth of electric vehicles (EVs) calls forth the need for smart battery management systems that can perform health monitoring and predictive diagnostics in real-time. The conventional battery modelling methods mostly do not cover the complicated, dynamic behaviors coming from different usage patterns. The study outlines a structure that would use Reinforcement Learning (RL)-based AI agent as a part of the Battery Electrical Analogy (BEA) simulation platform. With the help of the AI agent, different health parameters such as State of Health (SOH), State of Charge (SOC), and the signs of early thermal runaway can be predicted in real-time. The suggested design takes advantage of the simulation-based approach to have the agent learn and utilizes a decentralized cloud architecture suitable for scaling and reducing the response time. The RL agent performs an essential role in the process by tagging along with the continuous learning and the adjustment of the battery

Pardeshi, Rutuja Rahul, Kondhare, Manish, Sasi Kiran, Talabhaktula

Preventing Thermal Run-Away using Shape Memory Alloy

2026-26-0009

1/16/2026

The present disclosure is about combating Thermal runaway in Electric, Plug-in Hybrids and mild hybrid vehicles. This paper comprises of high-Voltage Battery pack containing Battery cells electrically coupled with Shape Memory Alloy along with Busbars. These connectors (Shape Memory Alloy) are programmed to operate in two states: First to electrically connect the cells with the busbars, second to disconnect the individual cells from electric connection beyond the threshold temperature. This mechanism enables the Battery cells to rapidly prevent the Battery from the Thermal runaway event which is caused from the cell level ensuring the Battery safety mechanically. Additionally, the Battery pack includes the cell monitoring system and Battery Monitoring System to enhance the above invention with regards to the safety of the vehicle. This configuration is implementable and retrofittable into existing battery systems, offering a robust solution to the challenges posed by prolonged vehicle

Reginald, Rini, Rout, Saswat, VENKATESH, Muthukrishnan, Chauhan, Ashish Jitendra, Selvaraj, Elayanila

Optimization and Evaluation of Thermal Interface Material Thickness and Distribution Patterns for Improved Heat Transfer in Liquid-Cooled Battery Pack

2026-26-0432

1/16/2026

Battery Thermal Management Systems (BTMS) play a critical role in ensuring the longevity, safety, and efficient operation of lithium-ion battery packs. These systems are designed to better dissipate the heat generated by the cells during vehicle operation, thereby maintaining a uniform temperature distribution across the battery modules, preventing overheating and mitigating the chances of thermal runaway. However, one of the primary challenges in BTMS design lies in achieving effective thermal contact between the battery cells and the cooling plate. Non-uniform or excessive application of Thermal Interface Materials (TIMs) without ensuring robustness and uniformity can increase interfacial thermal resistance, leading to significant temperature variations across the battery modules, which may trigger power limitations via the Battery Management System (BMS) and these thermal changes can cause inefficient cooling, ultimately affecting battery performance and lifespan. In this paper, a

K, Mathankumar, Jahagirdar, Manasi, Kumbhar, Makarand Shivaji

State of Health Estimation in Lithium-Ion Batteries: Methods, Comparisons, and Future Directions

2026-26-0208

1/16/2026

As the world is moving towards electric vehicles, we are observing a wide use of Lithium-Ion batteries in modern transportation. Lithium-Ion Batteries offer several advantages over conventional battery systems, including higher energy density that is energy stored per unit mass, longer Cycle Life, faster Charging rates, low Self-Discharge, lighter weight, and ease of maintenance as the memory effect present in other batteries is absent. However, despite these advantages, the system faces significant technical challenges arising from inaccurate battery State of Health (SOH) estimation techniques. These inaccuracies can lead to unexpected vehicle failures and a degraded end-user experience, especially due to incorrect “distance to empty” predictions. In this paper, different SOH estimation techniques are reviewed and compared in detail. The SOH estimation approaches are broadly classified into three main categories: Model based estimation techniques, data driven estimation techniques

Patel, Parvez, Bhagat, Ayush

Kinetic Modeling and Adaptive Time Stepping for Battery Thermal Runaway Propagation

14-15-01-0003

1/12/2026

0D, quasi-3D, and 3D chemistry solvers with varying degrees of complexity are developed to predict the thermal runaway propagation in battery cells. The 0D solver assumes the system as homogeneous and closed. The quasi-3D solver assumes the system as homogeneous on the selection level and the 3D solver accounts all spatial inhomogeneities in the temperature and composition. Both the quasi-3D and 3D solvers are fully integrated into a computational fluid dynamic (CFD) solver and capable of predicting thermal runaway in multiple battery cells with cell-specific kinetic reaction model. As the modeling complexity increases with each solver, respectively, the accuracy and the simulation time increases. With the large amount of heat and rapid transitions from the onset of thermal runaway, the CFD solvers usually encounter difficulties in predicting the solution accurately and in extreme heat release cases the solver may diverge. A chemical time scale based adaptive time stepping is developed

Chittipotula, Thirumalesha

Constraint Enforcement of a Lithium-Ion Battery Pack Safety-Critical System for Electric Hybrid Vehicles

2025-36-0160

12/18/2025

System robustness and performance are essential considerations in controller design to ensure reference tracking, disturbance rejection, and resilience to modeling uncertainties. However, guaranteeing that the system operates within safe bounds becomes a priority in safety-critical applications, even if performance must be compromised temporarily. One prominent example is the thermal management of lithium-ion battery packs, where temperature must be strictly controlled to prevent degradation and avoid hazardous thermal runaway events. In these systems, temperature constraints must consistently be enforced, regardless of external disturbances or control errors. Traditional strategies, such as Model Predictive Control (MPC), can explicitly handle such constraints but often require solving high-dimensional optimization problems, making real-time implementation computationally demanding. To overcome these limitations, this study investigates the use of a Constraint Enforcement strategy to

Ebner, Eric Rossini, Fernandes, Lucas Pasqual, Leal, Gustavo Nobre, Neto, Cyro Albuquerque, Leonardi, Fabrizio

Research on Thermal Runaway Triggering Methods for Non-Rechargeable Lithium Battery in Civil Aircraft

2025-99-0405

12/10/2025

In aviation industry, compared to traditional batteries (lead-acid and nickel-cadmium batteries), non-rechargeable lithium batteries are usually the primary choice as independent backup power sources for emergency equipment (such as Emergency Locator Transmitter and Underwater Locator Beacon) due to excellent performance, weight/volume advantages and relatively long inspection/maintenance intervals. However, considering higher energy density and more active chemical characteristics, lithium batteries unique failure modes require special consideration in safety analysis. Among these failure modes, thermal runaway is one of the most severe failure modes of non-rechargeable lithium batteries, potentially leading to serious impact such as flame, explosion, and release of toxic and harmful gases/liquid. Therefore, it is necessary to demonstrate the containment of thermal runaway of non-rechargeable lithium batteries through equipment-level testing, and do aircraft-level safety analysis to

Zhang, Xiaoyu, Zheng, Jian, Yang, Dianliang, Sheng, Jiaqian

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99-07-06-0TOC

12/2/2025

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Tobolski, Sue

Experts agree on need for large-scale fire testing of battery energy storage systems

25AUTP12_16

12/1/2025

A panel of four battery testing experts from different fields agreed that large scale fire testing, as called for in a proposed update to testing standard UL 9540A, could help address confusion among consumers, battery companies and insurers. Moderated by LaTanya Schwalb, principal engineer for energy and industrial automation at UL Solutions, the panel discussion held at the Battery Show North America underscored the need for a current standard and for standards to adapt more quickly to new battery chemistries and technologies.

Clonts, Chris

Electrode-Level Modeling of Thermal Decomposition Reactions as a Foundation for Multiscale Thermal Runaway Analysis in Lithium-Ion Batteries

2025-01-0523

11/25/2025

Thermal runaway in lithium-ion batteries represents a critical safety challenge, particularly in high-voltage battery systems used in electric vehicles and stationary energy storage. A comprehensive understanding of the multi-scale processes that initiate and propagate thermal runaway is essential for the development of effective safety measures and design strategies. This study provides a structured theoretical overview of the thermal runaway phenomenon across four hierarchical levels: electrode, single cell, module, and high-voltage battery system. At the electrode level, thermal runaway initiation is linked to electrochemical and chemical degradation mechanisms such as solid electrolyte interphase decomposition, separator breakdown, and internal short circuits. These processes lead to highly exothermic reactions that, at the cell scale, can result in rapid temperature increases, gas generation, and overpressure. On the module and system levels, thermal runaway can propagate through

Ceylan, Deniz, Kulzer, André Casal, Winterholler, Nina, Weinmann, Johannes, Schiek, Werner

Current Research Progress and Future Perspectives of Early Fire Prediction Modeling for Electric Vehicles

14-15-01-0001

11/21/2025

With the wide application of electric vehicles (EVs) around the world, the increase in battery pack energy density and the growing complexity of electrical systems have gradually heightened the risk of vehicle fires. Therefore, achieving efficient and timely fire risk prediction is essential to minimize the probability of fires in EVs. However, the development of EV prediction models requires multidisciplinary integration to address complex safety challenges. This article provides a detailed discussion on the mechanisms and combustion characteristics of EV fires, followed by an investigation into the high-risk factors that trigger such fires. Based on the above content, this article conducts an in-depth analysis of the characteristics of different models for high-risk factors such as batteries, electrical systems, and collision damage, offering insights to bridge the gap between different disciplines. Finally, it explores the future development direction of predictive models for EVs

Shao, Yuyang, Cong, Beihua, Jianghong, Liu

Investigation on Flow Pattern Architecture Design on Thermal Plate for Battery Electric Vehicles

2025-28-0406

10/30/2025

Liquid cooling systems are a widely used method for cooling lithium-ion batteries in modern electric vehicles. Battery thermal plate (BTP) is a key component of the liquid-cooled thermal management system, which regulates battery temperature to prevent thermal runaway and fire accidents. Designing an energy efficient flow pattern with uniform velocity and temperature distribution is a major challenge for the BTP. In this paper, the effect of flow patterns in cooling performance of the BTP is examined. Battery temperature can be efficiently controlled by varying direction, number of flow channels and structure of the BTP. Complex flow pattern networks are modeled and compared based on the computational fluid dynamics results. The channel flow resistance, pressure drop, and temperature distribution are key parameters which are evaluated for varying mass flow rate conditions. From this study, the flow pattern which satisfies the temperature requirement and has 10% less pumping power

K, Muthukrishnan, S, Saikrishna, K, Keshavbalaje, Gutte, Ashish

Thermal Management in Electrified Transportation: A Comparative Study of Different Powertrain Technologies

2025-28-0412

10/30/2025

The transition towards sustainable transportation necessitates the development of advanced thermal management systems (TMS) for electric vehicles (EVs), hybrid electric vehicles (HEVs), hydrogen fuel cell vehicles (FCVs), and hydrogen internal combustion engine vehicles (HICEVs). Effective thermal control is crucial for passenger comfort and the performance, longevity, and safety of critical vehicle components. This paper presents a rigorous and comparative analysis of TMS strategies across these diverse powertrain technologies. It systematically examines the unique thermal challenges associated with each subsystem, including cabin HVAC, battery packs, fuel cell stacks, traction motors, and power electronics. For cabin HVAC, the paper explores methods for minimizing energy consumption while maintaining thermal comfort, considering factors such as ambient temperature, humidity, and occupant load. The critical importance of battery thermal management is emphasized, with a focus on

K, Neelima, K, Anisha, Ch, Kavya, C, Somasundar, Satyam, Satyam, P, Geetha

Automotive Battery Thermal Management by Federated Learning Methodology - Real Time Thermal Runaway Detection

2025-28-0408

10/30/2025

Modern battery management systems, as part of Battery Digital Twin, include cloud-based predictive analytics algorithms. These algorithms predicts critical parameters like Thermal runaway events, state of health (SOH), state of charge (SOC), remaining useful life (RUL), etc. However, relying only on cloud-based computations adds significant latency to time-sensitive procedures such as thermal runaway monitoring. This is a very critical and safety function and delay is not acceptable, but automobiles operate in various areas throughout the intended path of travel, internet connectivity varies, resulting in a delay in data delivery to the cloud and similarly delay in return of the detected warning to the driver back in the vehicle. As a result, the inherent lag in data transfer between the cloud and vehicles challenges the present deployment of cloud-based real-time monitoring solutions. This study proposes application of Federated Learning and applying to a thermal runaway model in low

Sarkar, Prasanta

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99-07-05-0TOC

10/23/2025

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Tobolski, Sue

ML-Based System Level Optimization of EV Cooling Circuit

2025-01-0376

10/7/2025

Efficient thermal management is vital for electric vehicles (EVs) to maintain optimal operating temperatures and enhance energy efficiency. Traditional simulation-based design approaches, while accurate, are often computationally expensive and limited in their ability to explore large design spaces. This study introduces a machine learning (ML)-based optimization framework for the design of an EV cooling circuit, targeting a 5°C reduction in the maximum electric motor temperature. A one-dimensional computational fluid dynamics (1D-CFD) model is utilized to generate a Design of Experiments (DOE) matrix, incorporating key parameters such as coolant flow rate and heat exchanger dimensions. A Radial Basis Function (RBF) neural network is trained on the simulation data to serve as a surrogate model, enabling rapid performance prediction. Optimization is performed using the Non-Dominated Sorting Genetic Algorithm II (NSGA2), yielding three distinct design solutions that meet the thermal

Paul, Kavin, Ganesan, Arul, Mansour, Youssef

Role of Charging Rate on Internal Short-Circuit Characteristics of Lithium-Ion Battery

14-14-03-0020

10/7/2025

Lithium-ion batteries used in electric vehicles (EVs) are facing issues owing to internal short-circuit (ISC), leading to thermal runaway. In this study, a pseudo-two-dimensional (P2D) model is employed to numerically investigate the effects of charging rate (C-rate) and separator electrical conductivity on the ISC behavior of a lithium-ion cell. The results reveal that as C-rate increases, both the voltage and capacity decrease more rapidly marked by higher solid potential gradient indicating increased internal resistance. These effects further intensified at higher separator conductivity, which facilitates greater ISC current and accelerates cell degradation. Also, the variations in current density and solid-phase lithium concentration become more pronounced at higher C-rates, particularly near the anode–separator interface, indicating increased non-uniformity during ISC conditions. Furthermore, the electrolyte voltage drop intensifies with rising C-rate, contributing to additional

Ch, Narendra Babu, Paramane, Ashish, Randive, Pitambar

Lithium and Lithium-Ion Cell and Battery Containment Performance Recommended Practice

J3303_202510 (Current)

10/2/2025

Prescribe test conditions to quantify the effectiveness of containment devices for containing thermal runaway hazards of lithium/lithium-ion cells, batteries, and equipment during storage resulting from the failure of a cell within the container. Due to the many storage locations (indoors, outdoors, etc.), the hazards shall be classified individually to allow for varying performance based on a given storage location.

Battery Transportation and Storage Committee

Safety by design: a next-gen blueprint for EV batteries

25AUTP10_04

10/1/2025

The EV industry can - and should - stop pushing decades-old battery architecture well past its limits. For more than three decades, the fundamental design of lithium-ion batteries has remained largely unchanged, tracing back to Sony's original design in 1991 for small portable electronics. While impressive advancements have been made in materials, chemistry, and manufacturing efficiencies, the core design has remained the same. This legacy design, initially developed for small, low-voltage, low-energy-density applications, has now become a significant bottleneck for the automotive industry and the future of electric vehicles. Imagine only updating the apps on your phone without ever updating the operating system. No matter how modern the apps become, the system cannot keep up with new demands. At some point, performance stalls and the system hits a wall. This is the situation the EV industry faces today. We're pushing decades-old battery architecture well past its limits.

Ota, Naoki

Revolutionizing EV battery safety: advanced coatings and composite solutions

25AUTP10_10

10/1/2025

Thermal runaway in electric vehicle (EV) batteries is rare, but it can happen, producing smoke, fire, and explosions. This uncontrollable, self-heating state can transfer intense heat to adjacent cells and cause pressure buildups that exceed the mechanical limits of cell casings. Since the gases that can form inside a battery cell are flammable, a spark or other ignition source could propagate fire or lead to an explosion and cause the violent venting of shrapnel or particulates, putting vehicle occupants and emergency responders at risk. To support EV safety, silicone thermal management materials are placed between battery cells and between battery modules. For battery pack enclosures, however, mica sheets traditionally have been used as protective barriers. Mica provides thermal and electrical insulation, but sheets made of this mineral are limited in terms of thermal performance, mechanical durability, processability, and sustainable sourcing. To address these challenges, advanced

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