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

Estimation of State-of-Health for Lithium-Ion Batteries Using Relaxation Voltage against Variable Operating Conditions

2026-01-7009

2/27/2026

The health assessment of lithium-ion batteries is crucial for the efficient operation of electric vehicles and energy storage systems. However, in the complex and ever-changing operating environment, accurately assessing the health state of lithium-ion batteries remains a challenge that urgently needs to be overcome. This paper proposes a SOH estimation method based on the relaxation phase curves after battery charging. It extracts health features from the relaxation phase and combines them with Gaussian process regression. The method initially extracts six statistical features from the relaxation voltage and selects those with high correlation as inputs for the model. Next, it constructs new combined kernel functions by randomly pairing 5 commonly used kernel functions. It employs cross-validation to adaptively select the optimal combined kernel function. The proposed method is validated with 55 batteries. Results show that compared with traditional single-kernel models, the mean

Lin, Yupeng, Yang, Da, Wan, Fu, Mu, Jingyi, Liu, Ruiqi, Yin, Wenwei, Luo, Bing, Zhong, Zheng, Chen, Weigen

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

SOC Estimation of Lithium Iron Phosphate Batteries Based on Pressure Feature and VMD-LSTM

2026-01-7039

2/27/2026

The State of Charge (SOC) is a key parameter for measuring the remaining capacity of new energy vehicle batteries. It not only directly reflects the driving range of the vehicle but also plays an indispensable role in ensuring operational safety and extending battery lifespan. Accurate estimation of SOC provides strong support for the safe and reliable operation of electric vehicles. During the charging and discharging process of lithium iron phosphate batteries, the intercalation and deintercalation of lithium ions cause deformation of the electrode's lattice structure, leading to the expansion and contraction of the electrode volume. This, in turn, exerts stress on the limited internal space of the battery, which is mainly manifested as changes in battery pressure monitored by sensors. To address the issues of insufficient information and low estimation accuracy associated with the use of electrical signals in traditional data-driven methods, this study introduces pressure

Tian, Jie, Du, Jinqiao, Rao, Bo, Lai, Tiande, Dong, Boyi, Jiang, Jiuchun

An Equivalent Circuit Model of Lithium-Ion Batteries for Commercial Charging Strategy: Application to Step Charge

2026-01-7042

2/27/2026

Lithium-ion batteries represent a complex and nonlinear voltage behaviour on various time scales. Battery models are needed to analyze and estimate the battery behaviour and determine their suitability for practical applications. Battery model simulations in previous studies were mainly based on pulse charge and discharge cases. The current amplitude used in the test cases was limited, and the temperature factor of the battery model was neglected. The simulation conditions above were significantly different from those in practical applications. In this paper, an equivalent circuit model considering the temperature factor is developed to simulate the practical applications of lithium-ion batteries. Experimental tests for parameterization are applied to the commercially available 189 Ah lithium iron phosphate battery cells under a wide range of experimental conditions. The parameters are obtained through experimental tests and are used to build the equivalent circuit model of the battery

Chang, An, Wang, Shengwei, Zhou, Kai

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

Multi-Modal Feature Fusion Network for State-of-Health Estimation of Lithium-Ion Batteries

2026-01-7010

2/27/2026

For the safe and reliable deployment of lithium-ion batteries, accurate state of health (SOH) estimation is paramount. However, most existing data-driven methodologies depend exclusively on single-modal data, such as voltage-capacity or incremental capacity (IC) curves. Such limited data frequently fails to offer a holistic understanding of the complex battery degradation process. To address this limitation, this paper proposes a novel multi-modal feature fusion network. This network can effectively combine three different but complementary data modalities: historical point features, voltage-capacity and IC sequence features, as well as degraded image features. To this end, the framework incorporates a one-dimensional convolutional neural network (1D-CNN) for analyzing point features, leverages a Transformer encoder to process sequence features, and employs ResNet for identifying spatio-temporal patterns in degraded images. These heterogeneous features are then collaboratively

Li, Xiaobin, He, Ning, Yang, Fangfang

Deep Learning-Based Screening of Retired Lithium-ion Batteries for Sustainable Lifecycle Management

2026-01-7007

2/27/2026

Ensuring safety and consistent quality in lithium-ion battery manufacturing is essential for the reliable operation of electric vehicles and energy storage systems. Strict quality control measures during production not only enhance product safety but also reduce the number of defective units entering post-market recycling streams. However, variations in battery quality remain inevitable, making efficient downstream sorting an important complement to upstream manufacturing control. Efficient sorting of retired lithium-ion batteries is critical for battery second-life utilization and circular economy development. Based on 750 commercially recycled retired batteries, this study proposes a 1D CNN-Transformer hybrid deep learning framework for automatic screening of retired batteries. The framework first employs a 1D convolutional neural network to extract local features from time–voltage sequences and compress sequence length, followed by a Transformer encoder to capture global

Xiao, Hualong, Luo, Gang, Wang, Li, Lin, Mingqiang, Wu, Ji

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

Simulation-Based Evaluation of Lithium-Ion Batteries Manufacturing Performance

2026-01-7005

2/27/2026

Lithium-ion batteries (LIBs) have become indispensable components in diverse energy applications driven by their high energy density, long cycle life, and low self-discharge. These excellent characteristics are directly influenced by their manufacturing processes, where variations in battery design and processing parameters will lead to significant differences in performance. Therefore, reliable and efficient evaluation of battery performance across manufacturing processes is essential for quality assurance and process improvement. Traditional methods rely on formation cycling and associated electrochemical tests, which are time and cost intensive. Different from them, a simulation-based approach for manufacturing performance evaluation is proposed in this study. The method employs the pseudo two dimensions (P2D) electrochemical model within the PyBaMM framework, where model parameters such as electrode type, electrode size, and particle size are derived from manufacturing data and

Yan, Yifei, Meng, Jinhao, Song, Zhengxiang, Zhang, Shirui, Pan, Yuhao, Yang, Peihao, Peng, Jichang

Prognostics of Battery State of Health Using Hybrid Modeling of Electric Vehicle Charging Data

2026-01-7002

2/27/2026

Accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for ensuring the safety, reliability, and performance optimization of electric vehicles. In practical operating environments, however, data quality is often compromised by noise interference, frequent fluctuations in load conditions, and the inherently non-stationary nature of battery degradation features. These challenges reduce the effectiveness of conventional modeling approaches, which often struggle to maintain both high prediction accuracy and strong generalization capability. To address these issues, this study develops a comprehensive SOH estimation approach encompassing data quality enhancement, degradation feature extraction, and hybrid deep learning-based modeling. In the first stage, multi-stage anomaly detection techniques are applied to remove noisy or inconsistent measurements. A week-based indexing strategy is introduced to generate temporally coherent labels, ensuring that time

Wang, Sijing, Jiao, Meiyuan, Huang, Weixuan, Lin, Yiting, Liu, Honglai, Lian, Cheng

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

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

Failure Analysis of Li-Cu Reference Electrode in NCM811||Gr Pouch Battery

2026-01-7012

2/27/2026

Reliable monitoring of the internal state of lithium-ion batteries (LIBs) is crucial for mitigating potential safety hazards. The incorporation of a reference electrode (RE) within the battery constitutes a vital approach for achieving single-electrode monitoring and understanding changes in electrode state during cycling. Among these, the lithium-copper reference electrode (Li-Cu RE) is particularly cost-effective and straightforward to prepare, being fabricated by depositing lithium onto a copper wire. However, Li-Cu RE exhibits a relatively short effective lifespan during long-term cycling, thereby limiting its practical application. In this work, based on a self-fabricated three-electrode single-layer pouch cell, the microstructural changes before and after failure of the Li-Cu RE were characterized and analyzed, revealing its failure evolution process. Post-failure microstructures observations exhibit marked porosity in the electrode, attributed to substantial depletion of surface

Hu, Jiaxing, Luan, Weiling, Chen, Haofeng, Chen, Ying

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

Life Cycle Assessment for Sustainable EV Battery Recycling: A Technical Framework for EBRR

2026-28-0092

2/12/2026

Electric vehicle (EV) battery life cycle assessment (LCA) is emerging as a strategic necessity amid booming demand and tightening environmental regulations. This report consolidates key findings and recommendations for EBRR (Electric Battery Reuse & Recycling) to implement a comprehensive LCA program covering EV lithium-ion batteries from cradle-to-grave and cradle-to-cradle perspectives. The study confirms that global Li-ion battery demand is skyrocketing – projected to increase 14-fold by 2030[1] – amplifying the urgency for sustainable battery management (see Figure 1). It outlines the full life cycle stages of EV batteries (raw material extraction, manufacturing, use, and end-of-life) and compares linear vs. circular approaches. Using the ISO 14040/44 framework[18, 19] and industry-standard LCA tools, the report evaluates environmental impacts and identifies hotspots. Key findings show that mining and manufacturing dominate the battery’s carbon footprint, but end-of-life strategies

Asokan, Gayathri, Raju cEng, Rajkumar, Dhananjaya, Chandan, Sattigeri cEng, Sudhir V

Numerical Study of Battery Pack Cooling for Electric Vehicles

2026-28-0110

2/12/2026

This study presents a systematic CFD-based investigation of air-cooled lithium-ion battery pack thermal management using a novel U-shaped channel. The U-shaped domain was selected due to its ability to promote recirculation and uniform air distribution, which enhances cooling effectiveness compared to conventional straight and Z-type channels. A systematic parametric optimization of inlet position and airflow velocity was performed to minimize hotspot formation and improve temperature uniformity. Results reveal that shifting the inlet from 30 mm to 20 mm and increasing velocity from 2 m/s to 3 m/s reduced the maximum battery temperature by 3.46 K, from a baseline of 333 K to 329.54 K, while maintaining minimal pressure drop. These findings highlight that strategic control of inlet parameters can yield significant thermal improvements with high cost-effectiveness and geometric simplicity.

PC, Murugan, J, Sivasankar, W, Beno Wincy, G, Arun Prasad

Performance Evaluation of a Novel Coolant Channel Configuration for Liquid Based Battery Thermal Management in Electric Vehicles

2026-28-0042

2/12/2026

The performance and longevity of lithium-ion (Li-ion) batteries in electric vehicles (EVs) are critically dependent on effective thermal management. As internal heat generation during charge and discharge cycles can lead to uneven temperature distribution, exceeding optimal operating limits (25 - 40°C) can significantly degrade battery performance and lifespan. This study presents a performance evaluation of a novel liquid-based Battery Thermal Management System (BTMS) featuring a dual-directional coolant channel configuration designed to enhance thermal uniformity and heat dissipation. The proposed configuration combines horizontal and vertical coolant passages in an indirect cooling layout to address the limitations of conventional serpentine-type channels. A comprehensive thermal analysis was carried out under realistic loading conditions using three coolant types: water, ethylene glycol- based G48, and graphene-enhanced water nanofluids. These were evaluated for thermal

Selvan, Arul Mozhi, Periyasamy, Muthukumar, R, Thiruppathi, Prasad S, Hari, Raghav, R, Boddu, Sriram Pydi Aditya

Analysis of Second-Life Electric Vehicle Batteries Value Maximizing through Repurposing and Resale, Ways to Achieve Optimal Performance and Standardization Challenges for Testing and Certification

2026-26-0193

1/16/2026

As electric vehicle (EV) adoption accelerates globally, a growing volume of lithium-ion batteries are reaching an end-of-life in their primary automotive application—despite retaining 60 to 80% of their original capacity. This presents a significant opportunity to extend battery utility through second-life applications such as stationary energy storage, microgrid support, and commercial backup systems. This paper analyzes the strategies for maximizing the residual value of second-life EV batteries through repurposing and resale, while also addressing the challenges associated with performance optimization and standardization of testing and certification procedures. The study evaluates the techno-economic viability of second-life batteries compared to new systems, emphasizing cost savings, environmental impact, and emerging market demand. Techniques for enhancing second-life performance are examined, including advanced state-of-health (SOH) diagnostics, machine learning models for usage

Agarwal, Pranjal, Penta, Amar

Comparative Assessment of Lithium-Ion Battery Recycling Strategies: Unveiling Challenges, Optimizing Benefits, and Exploring Future Potential

2026-26-0201

1/16/2026

This research paper offers a comprehensive evaluation of lithium-ion battery recycling methods, tracing the entire journey from global demand to the practical challenges and solutions for sustainable battery recycling. It starts with the analysis of worldwide LIB demand growth alongside the exponential growth in volumes of spent batteries and recycling rates. The study focuses on the imbalance in production and recovery of critical battery components and its environmental and economic effects. The paper then systematically examines six major recycling methodologies: mechanical, pyrometallurgical, hydrometallurgical, biotechnological, direct, and ion-exchange recycling. It goes into detail about their advantages, limitations, and roles in maximizing the recovery of valuable metals such as lithium, cobalt, and nickel. Traditional techniques like hydrometallurgical and pyrometallurgical methods, and emerging approaches including bioleaching and ion-exchange, are evaluated for their

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

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

Transforming Battery Management: The Role of Artificial Intelligence and Machine Learning in Digital Twin Technologies

2026-26-0382

1/16/2026

The traditional Battery Management System (BMS) faces certain limitations in fully utilizing battery capacity and performance during the long cycle life operation of Electric Vehicles (EVs). These constraints include limited real-time data collection, low processing speed, lack of predictive maintenance, and minimal accuracy in predicting health and degradation chemistry. A Battery Digital Twin (BDT) can effectively address these limitations of the BMS. Battery Digital Twins (BDT) can be viewed as a cyber-physical system comprising four key elements: virtual representation, bidirectional connection, Simulation, and connection across the life cycle phases of an EV battery. The performance of a Li-ion battery largely depends on the cathode chemistry, component design, and operating conditions. The battery should be manufactured in a manner (such as cylindrical or prismatic cell) that prevents explosion, leakage, and gas generation inside the battery. To enhance the performance and safety

Chaturvedi, Vikash, M, Venkatesan, Lanke, Siddhi, Subramaniam, Anand, Karle, Manish, Pandit, Rugved, Gupta, Drishti, Karle, Ujjwala Shailesh

Integrated Simulation of Electric Vehicle Model Using the ROM Approach

2026-26-0369

1/16/2026

The growing demand for Electric Vehicles (EVs) has highlighted the importance of efficient and accurate simulation tools for design and performance optimization. The architecture of electric vehicles is distinct from that of internal combustion engine vehicles. It consists of on-board charger, DC-DC converter, Lithium ion battery pack, Inverter, electric motor, controllers and transmission. The battery pack supplies electric current to the traction motor, which then converts this electrical energy into mechanical energy, resulting in the rotational motion needed to drive the vehicle. Wide range of Multi-physics is involved in the simulation which involves Power electronics, Electromagnetics, Fluid Mechanics, Thermal engineering. This paper presents an integrated simulation and range prediction methodology for Electric Vehicles (EVs) using the Reduced Order Model (ROM) approach. The methodology includes simulation in both 3D and 1D domain. CFD simulation is performed to understand the

Shandilya, Anand, Kumar, Vivek

State of Health Estimation Based on Dynamic Cell Behaviour in EVs

2026-26-0183

1/16/2026

In electric vehicle (EV) applications, accurate estimation of State of Health (SOH) of lithium ion battery pack is critical for ensuring its performance, reliability, operational safety and user confidence. SOH is a key parameter monitored by Battery Management System (BMS) to check the remaining usable life of the battery and to make informed decisions regarding charging, discharging, power delivery, and maintenance scheduling. In traditional SOH estimation techniques commonly rely on simplistic full-cycle charge-discharge data or single-parameter tracking (such as voltage or internal resistance) and other method like coulomb counting. Kalman filter, model based method such as equivalent circuit modelling, data driven models etc. This methods not consider variable field conditions such as partial and full state of-charge usage condition, dynamic load profiles, and non-uniform aging. As a result, these methods can produce significant deviations in SOH estimation, potentially causing

Nikam, Ashish, Tiwari, Awanish Shankar, Sodha, Nirav, Hariyani, Ganesh, Ambhore, Yogesh Gajanan

Coupled Thermo-Mechanical Simulation Framework for Structural Evaluation of Battery Pack

2026-26-0414

1/16/2026

With the rapid adoption of electric vehicles (EVs), ensuring the structural integrity and thermal safety of lithium-ion battery has become a critical priority. Battery failures resulting from mechanical abuse, thermal stress, internal pressure build up or electrical faults may lead to structural failure. To address these challenges, it is essential to understand the coupled thermal and mechanical responses of battery structure under extreme conditions. Thermo-mechanical simulation serves as a powerful tool for predictive safety assessment and design optimization, particularly in addressing thermal propagation and pressure-induced failure events. This study presents a comprehensive coupled thermo-mechanical simulation framework designed to evaluate the structural performance of EV battery enclosures under worst-case thermal and overpressure conditions. The methodology involves high-fidelity three-dimensional modeling of the battery pack enclosure, incorporating realistic material

Bhat, Sadashiv C, Sugumar, Mohanraj

Simulation Based Approach to Optimize Electric Vehicle Battery Thermal System

2026-26-0376

1/16/2026

The performance and longevity of Li-ion batteries in electric vehicles are significantly influenced by the cell temperature. Hence, efficient thermal management techniques are essential for battery packs. Simulation based optimization approaches improves the efficiency of the battery pack thermal management during the early stage of product development. In this paper, a simulation-based methodology has been introduced to increase the heat transfer from/to coolant via cooling plate as well as to reduce the heat transfer from/to the external environment. The heat transfer coefficient between cooling plate and coolant needs to be enhanced to achieve efficient heat transfer through cooling plate, without exceeding the coolant pressure drop the target limit. A one-dimensional simulation methodology described in this work analyzed numerous design of experiments for coolant layout without performing CAD iteration loops and optimized the cooling channel width, height and number of channels to

U, Reghunath, P S, Shebin

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

Lithium-Ion Battery’s State of Health Prediction: A Deep Learning Framework

2026-26-0162

1/16/2026

This paper presents a comprehensive study on predictive maintenance of lithium-ion batteries in electric vehicles (EVs) using data-driven approaches. The study involves collecting data from four individual battery cells, each subjected to various charging and discharging parameters. After preprocessing the data, we apply feature extraction techniques to extract relevant features. Subsequent data analysis guides the development of machine learning (ML) and deep learning (DL) models on the combined dataset of the four cells. A crucial aspect of this study involves addressing measurement noise inherent in cellwise data. Through innovative techniques, we mitigate the effects of measurement noise, improving the accuracy and robustness of our models. The proposed DL models demonstrate remarkable efficiency in handling noise, leading to superior predictive performance in estimating State of Health (SoH) as degraded capacity. The findings of this research offer valuable insights into

Suryawanshi, Chaitanya Balasaheb, Nangare, Kapilraj, Gaikwad, Pooja

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