Browse Topic: Thermal runaway

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Model-Based Sensitivity Study of Thermal Runaway Onset and Severity in Lithium-Ion Cells: Effects of Geometry and Thermal Boundary Conditions2026-01-07557/1/2026
Thermal runaway assessment in automotive battery development is still largely driven by isolated abuse tests, while design decisions require quantitative insight into how cell geometry, material thresholds, and thermal boundary conditions influence thermal runaway onset and severity. This paper presents a systematic sensitivity study using a coupled electrochemical and thermal model augmented with Arrhenius-based decomposition reactions to represent the dominant exothermic pathways. Thermal runaway onset is defined using a temperature rise-rate criterion to distinguish gradual heating from runaway acceleration. Two trigger modes are considered: an internal short circuit initiated by nail penetration and an external heating trigger. Four parameter groups are investigated: cell length scaling, separator decomposition temperature, external heating power, and the convective heat transfer coefficient to the environment. For the nail-triggered internal short circuit, larger cells exhibit lower peak temperatures but longer times to reach the maximum, indicating a geometry-driven shift from rapid escalation to a slower, more moderated evolution. In the external heating case, increasing cell size significantly delays onset, while peak temperature shows a nonlinear trend and approaches saturation rather than scaling inversely with size. Increasing the separator decomposition temperature also shows a saturation effect because alternative reactions can dominate the triggering sequence. External heating power exhibits a threshold: below a critical level, convective losses balance the input and prevent runaway. Even when external heating is stopped at an intermediate temperature, higher preheating power can still lead to higher peak temperatures due to a larger remaining reactive inventory when the internal short circuit occurs. Improved heat rejection consistently delays onset, reduces peak temperature, and accelerates cool-down. Overall, the study extends prior trigger-specific analyses by providing a unified reduced-order sensitivity view across two abuse pathways and by identifying threshold and saturation behaviors that translate directly into design-relevant robustness levers.
Ceylan, Deniz, Kulzer, André Casal, Winterholler, Nina, Giek, Michael, Weinmann, Johannes
A Comprehensive CFD-Driven Approach to Battery Thermal Modeling and Cooling System Optimization2026-01-07527/1/2026
With the continued expansion of electric mobility, liquid-cooled thermal management systems have become indispensable for ensuring the performance, durability, and safety of automotive battery packs. This work presents a novel cooling-plate design that integrates offset strip-fin turbulators to enhance convective heat transfer between lithium-ion cells and the circulating coolant. A comprehensive multi-region CFD model of the full battery pack is developed, incorporating an implicit lumped-parameter representation of cell heat generation. The numerical predictions are validated against dedicated experimental measurements available in the literature. Subsequently, a parametric study is conducted in which the number of hydraulic sub-modules and the inlet/outlet configurations are systematically varied to generate all feasible design permutations. The resulting configurations are compared to assess thermal performance and to quantify the benefits—as well as the potential penalties—introduced by the turbulators relative to the experimentally validated baseline.
Montenegro, Gianluca, Onorati, Angelo, Della Torre, Augusto, Tariq, Muhammad Hasnain, Bonetti, Elisa
A Simplified Layered Model for Large-Scale Impact Simulations of Li-Ion Prismatic Cells14-15-02-00126/16/2026
This article presents a novel finite element modeling approach to predict the mechanical response of jellyrolls in large-scale explicit crash simulations up to the experimental occurrence of internal short-circuit. The proposed simplified layered model embeds membrane elements within a solid element mesh to improve the prediction in load cases dominated by the buckling and sliding of the jellyroll’s layered structure. The model was validated against experimental results from in-plane, out-of-plane, and bending tests on jellyroll samples extracted from prismatic lithium-ion cells. The experimental results confirmed the jellyroll’s high compressibility under out-of-plane loads and its behavior as a collection of unconnected layers under in-plane and bending loading. Compared to the widely used crushable foam model, the simplified layered model offered additional flexibility, especially for in-plane and bending load cases. Additionally, it meets critical time increment requirements for explicit analysis and requires a limited number of calibration tests. These results highlight the model’s potential to improve the prediction of the jellyroll’s mechanical behavior in large-scale simulations.
Cioni, Daniele, Morin, David, Strating, Arjan, Kizio, Stephan, Costas, Miguel
Thermal and Emission Characterization of Cylindrical Lithium-Ion Batteries under Thermal Abuse Using a Climate Chamber2026-37-00236/9/2026
Thermal safety in lithium-ion batteries is a critical aspect due to their increasing use in energy storage systems and electric vehicles. To investigate thermal abuse conditions, numerous studies employ specialized equipment to accurately measure physical variables during thermal runaway events. However, such tests typically require robust equipment which limit their availability in conventional laboratory environments. In this context, the present study proposes and evaluates an experimental methodology based on the use of a climate chamber combined with an instrumented reduced-volume container, to reproduce severe external heating conditions. The thermal behavior and gas emissions associated with thermal runaway events were characterized in six cylindrical lithium-ion batteries of two different chemistries. Six cylindrical cells with NMC and NCA cathode chemistries were subjected to thermal abuse tests. In addition, gaseous emissions and mass loss were quantified after the event. Based on these tests, the results showed that NMC cells reached a higher average maximum surface temperature of 1086°C, whereas NCA cells exhibited the highest pressure values, with an average of 24.64 bar. Gas emissions presented high concentrations of CO and CO₂, reaching values of up to 381,555 ppm. Furthermore, both chemistries experienced a mass loss exceeding 50% after the test. Overall, the results indicate that both cell types exhibit similar behavior in terms of gas emissions, while NMC cells show greater thermal severity during the exothermic event. This work demonstrates that a system based on a climate chamber combined with an instrumented container is capable of reproducing severe thermal runaway conditions comparable to those achieved in specialized abuse-testing facilities, enabling the simultaneous characterization of temperature, pressure, and gas emissions in lithium-ion cells.
Penagos Vásquez, Diego Alejandro, Marco-Gimeno, Javier, Monsalve-Serrano, Javier, Garcia, Antonio, Perez Balastegui, Jose
Battery Cooling System Airborne Noise Correlation Using Statistical Based Tool14-15-02-00084/16/2026
In a traditional electric vehicle, managing its battery thermal performance is of prime importance. A well-designed battery thermal management system helps in extending its life and avoids safety-related issues like thermal runaways. A critical part of this thermal management is the battery cooling system (BCS), which can be air- or liquid-cooled. Based on the vehicle battery pack size, location, and its design complexity, the original equipment manufacturer can opt for either of the previous two methods. An air-cooled type of BCS system usually involves an active ventilation fan to dissipate the battery heat in the surroundings, which brings symbiotic noise into the picture. In an air-cooled BCS system, the primary source of noise is the cooling airflow over the heat exchanger caused by the fan. The airflow and noise performance characteristics of this fan are typically measured by the supplier in a standalone condition. These performance parameters deviate greatly when the fan is introduced inside a battery cooling module. In the current work, flow-induced noise simulation of a fan placed inside a confined BCS is performed. The simulation has made use of a statistically based tool due to its inherent low dissipative and dispersion properties. The simulation model included all complex interior parts of the BCS, including the mating gaps higher than 1 mm. The simulation results were correlated with the test, and further iterations were performed in simulations to understand the sensitivity of the condenser core location with respect to the fan. Additionally, the changes in noise performance behavior while moving from a standalone fan toward a fan integrated with the BCS system are also studied. The overall noise correlation between the simulation and test is achieved within a 0.4 dBA level. Further, the presence of flow-induced resonance inside the BCS at a lower frequency than the BPF was identified in simulation.
Nomani, Mustafa, Dupatti, Darshan, Nikam, Krishna, Sasikumar, R., Kajagar, Suresh, Panchare, Dattaji, Agalawe, Kiran
Towards Plating-Free Charging: Lightweight Prediction of Lithium Plating Current Limits2026-01-03874/7/2026
Lithium plating is a critical barrier to fast charging in electric and hybrid-electric vehicles, occurring at high state of charge (SOC) or low temperatures when Li+ deposits as metallic lithium on the anode surface instead of intercalating into graphite. At low temperatures, plated lithium may form dendrites that pierce the separator and trigger thermal runaway, while at high SOC, irreversible plating accelerates capacity fade by depleting cyclable lithium. Despite extensive study, lithium plating remains difficult to incorporate into battery management systems (BMS) due to computational complexity and the challenge of real-time detection, leading to reliance on conservative lookup maps. This work presents a lightweight empirical model for predicting plating-free charging limits in lithium nickel manganese cobalt (NMC) cells. A high-fidelity pseudo-2D electrochemical model was exercised across a wide range of charge rates and temperatures to capture the coupled effects of SOC, temperature, and current on plating potential. From these results, an empirical separable closed-form function was derived that is continuous, differentiable, and computationally efficient, enabling onboard real-time implementation. Validation against the high-fidelity model demonstrated strong agreement, with adjusted R2 > 0.99 and RMSE on the order of 1–3 A across the domain. Co-simulation confirmed that the model enforces plating-free charging across cold to hot conditions, while pulse-current tests showed that the continuous limits remain conservative under transient operation. In addition, charge-time analysis revealed an exponential dependence on temperature, leading to a compact correlation for estimating charge durations under varying thermal environments. Unlike detailed electrochemical models, this framework provides a practical, validated function for defining plating-free charging envelopes, directly suited for integration into BMS and supervisory charging strategies.
Sundar, Anirudh, Ghate, Atharva, Zhu, Qilun, Prucka, Robert, Barron, Morgan, Figueroa-Santos, Miriam
Simplified Thermal Runaway Propagation Modeling to Design a Mass Optimized Battery Pack2026-01-04044/7/2026
This paper presents a simplified approach to model thermal runaway propagation in a multi-cell battery pack, with the goal of designing a safe and lightweight pack for mass-sensitive applications. The key parameters which characterize single-cell thermal runaway, including heat release profile, apparent cell emissivity and mass loss, were extracted from empirical nail penetration tests. This characterization was used to drive a three-dimensional thermal model of a 19-cell hexagonal sub-pack with a center trigger cell. To enable rapid design exploration, a symmetry-based computationally simplified domain was used for a full-factorial Design of Experiments (DOE) varying cell spacing, epoxy thickness, heat spreader thickness, and cup geometry. The DOE results were used to identify dominant heat-transfer mechanisms, capture main and interaction effects, and determine mass-efficient design levers governing peak-neighbor cell temperature during propagation. Insights from the DOE study informed the design of a physical prototype and the placement of thermocouples for model validation. Measured temperature data showed good agreement with model predictions across multiple initiator locations, with 4–7 °C error in peak temperature and 3–5 s error in time to reach peak temperature. However, accurate reproduction of the observed trends required increasing epoxy thermal conductivity on the initiator cell to represent epoxy carbonization observed during post-test teardown. This simplified modeling approach, paired with targeted testing, can provide practical design guidance, reduce overall testing cost, and enable fast development of mass-optimized, propagation-resistant battery packs.
Kalyankar, Apoorv, Owen, Elliot, Strohmaier, Kyle, Mardall, Joseph
Guidelines for Interior Material Selection to Enhance Thermal Safety in Electric Vehicles2026-01-02494/7/2026
The advancement of electric vehicles necessitates a rigorous focus on passenger cabin safety, particularly concerning the severe thermal hazard of a lithium-ion battery thermal runaway. Unlike internal combustion engine vehicles, electric vehicles require interior materials that provide superior thermal resistance to slow heat propagation, delay autoignition, and minimize smoke and toxic gas emissions, thereby securing a survivable evacuation window. This paper examines the application of the lumped-capacitance thermal model and the derived thermal time constant (τ) as a foundational framework for evaluating and selecting cabin materials. This approach enables a quantitative, physics-based ranking of materials—including seat composites, sound-deadening layers, electrical insulation, and carpet assemblies—based on their intrinsic ability to delay their own temperature rise under transient heat flux. By integrating materials with a high τ and elevated critical failure temperatures, this study proposes a performance-based material selection strategy. This strategy is critical for extending the safe egress period mandated by standards such as UN ECE R.100 and GB 38031-2020 and is increasingly vital as safety benchmarks evolve toward longer durations. The synthesis of high-inertia materials with traditional fire-resistant insulation provides a multi-layered defense, enhancing passenger protection by functionally delaying the progression of heat, fire, and hazardous emissions into the occupied cabin space.
El-Sharkawy, Alaa, Taha, Nahla, Asar, Mona, Sheta, Mai
Advancing Safe Battery Design through Experimental Cell Characterization of Thermal, Electrical and Mechanical Properties2026-01-04084/7/2026
Lithium-ion batteries are critical to Electric Vehicles (EV) and grid-scale energy storage. Safe design of battery systems relies on accurate simulation of thermal runaway under electrical, thermal, and mechanical abuse. A predictive battery simulation requires characterization of electrical, thermal, and mechanical properties at the full cell and cell-component levels. In this study, a commercial cell from an EV was disassembled, and tested to support both homogenized and detailed computational models. At the cell level, electrical properties were characterized using Hybrid Pulse Power Characterization (HPPC) testing to assess the cell’s power capability. Full cell compression tests were conducted to characterize mechanical behavior under deformation and used to develop a multi-physics homogenized cell model. On the other hand, detailed cell modeling that includes different component layers could help users understand localized cell integrity under mechanical deformation. At the component level, cathode and anode electrodes, separator, and cell pouch laminate were tested for their thermal properties, including heat capacity, thermal conductivity, and melting points. This data is essential to modeling heat generation and dissipation in the detailed battery cell model. Mechanical behavior of these component materials was tested to understand structural integrity and failure modes. Electrical conductivity of cell component materials was also characterized. These experimentally measured properties and derived parameters may be integrated into a representative multi-physics battery cell model. By providing detailed characterization of a commercial lithium-ion EV cell, this research provides an experimental framework for developing both macro and detailed cell computational models needed for safety design assessments of EV battery systems.
Challa, Vidyu, Rostami-Angas, Masoud, kong, Kevin, Wang, Leyu, Reichert, Rudolf, Kan, Cing-Dao
Thermal Insulation Materials Used in Battery Pack Assembly: A Review of Materials, Test Methods, and Key Considerations2026-01-04064/7/2026
Electric vehicle (EV) battery packs have undergone substantial advancements in recent years, driven by engineering design improvements, material innovations, and increasingly stringent regulatory enforcement. These developments have enabled battery packs to become more energy-dense, which is essential for extending driving range and improving overall vehicle performance. However, with increased energy density comes a higher severity of thermal events, such as thermal runaway, which continues to raise concerns regarding vehicle safety, reliability, and long-term durability. This review highlights the critical role that thermal insulation materials play in mitigating the impact of such thermal events within EV battery systems. It presents an overview of commonly used thermal insulation materials, emphasizing their chemical composition, thermal resistance, and mechanical integrity under extreme conditions such as high temperatures and physical stress. The ability of these materials to maintain performance during thermal abuse is essential for protecting both the battery and surrounding vehicle components. In addition to material properties, the review compares the methodology and performance metrics of common methods for evaluating flammability, including flame retardance test such as UL 94 and torch and grit flammability tests such as one described in UL 2596. These comparisons are crucial for identifying insulation materials that can withstand severe thermal conditions without compromising safety. Beyond flammability, high temperature, smoke, and other important considerations such as thermal properties, environmental durability, corrosion resistance, and dielectric strength will be discussed with examples. These factors contribute to the overall effectiveness and reliability of insulation materials in EV applications. By understanding and optimizing these properties, engineers can better design battery packs that are not only high-performing but also safe and durable under demanding operating conditions.
Ng, Sze-Sze, Dhyani, Abhishek, Gorin, Craig, Jeon, Junho, Nuguri, Sravya, Repollet Pedrosa, Milton, Rylski, Adrian, Shete, Abhishek, Steinbrecher, Jacob, Thomas, Ryan
Modeling Non-Uniform Temperature Distribution in Lithium-Ion Batteries2026-01-01204/7/2026
Non-uniform temperature distribution within lithium-ion battery cells is a critical challenge that accelerates degradation, compromises safety, and reduces pack-level performance in electric vehicles (EVs). This work focuses on modeling and minimizing these thermal gradients through the structured optimization of a liquid-based Battery Thermal Management System (BTMS). A one-dimensional transient thermal model is developed to capture the axial temperature differentials (ΔT) in a cylindrical cell under dynamic drive-cycle loading, incorporating detailed heat transfer from the cell interior through thermal interface materials (TIM) and an aluminum cooling plate to the coolant. Using a Design for Six Sigma (DFSS) approach with an L18 orthogonal array, key control factors—including coolant flow rate, inlet temperature, TIM properties, and plate geometry—are systematically analyzed to identify configurations that optimally balance low average temperature with minimal internal temperature variation. The results provide a data-driven framework for designing robust cooling systems that mitigate the risks of localized hotspots and thermal runaway, thereby enhancing the durability and safety of EV battery packs.
El-Sharkawy, Alaa, Asar, Mona, Serpento, Stan, Sheta, Mai
Standalone Battery Pack Thermal Behavior Simulation and Experimental Correlation in a Controlled Environment2026-01-01254/7/2026
Battery thermal management is crucial for ensuring the safety, efficiency, and longevity of lithium-ion battery packs, particularly in electric vehicles (EVs). The primary purpose of a lithium-ion battery in an electric vehicle is to store and provide electrical energy for vehicle propulsion while maintaining safety under different operating conditions. This work proposes a thermal correlation between 1D CFD simulation and experimental test data under passive environmental heat exchange conditions without active coolant flow of a battery pack comprising four modules. An environmental exchange test was conducted using a 50% state of charge (SOC) battery pack, which is stabilized at 25°C to assess passive heat dissipation, thermal soak behavior, temperature distribution, and potential thermal runaway risks. The simulation predictions correlate well within a 1.5°C range compared to test results using ambient temperature and flow inputs, which confirms the reliability of the modeling approach. The simulation work was carried out using the GT-SUITE software. This study improves battery thermal management strategies by enhancing predictive accuracy and optimizing simulation frameworks for real-world applications. It minimizes overheating risks in practical scenarios, such as prolonged exposure to high ambient temperatures.
Nayaka, Sateesh Kumar, Dixit, Manish, Gudiyella, Soumya
Predictive Modeling and Sensitivity Analysis of Thermal Runaway in 800V Lithium-Ion Battery Module Using DFSS Methodology2026-01-01244/7/2026
Thermal runaway in high-voltage lithium-ion battery modules should focus on critical safety and design challenges in electric vehicle applications, which need predictive methods that enhance passenger safety and support regulatory compliance. The primary purpose of a lithium-ion battery in an electric vehicle is to provide reliable energy storage while maintaining safe operation under different operating conditions. This study proposes a Design for Six Sigma (DFSS) methodology to virtually predict and correlate thermal runaway and its propagation in an 800V high-power lithium-ion battery pack module. Conventional propagation analysis relies heavily on physical testing, whereas the DFSS-based virtual framework enables cost-effective evaluation at early design stages. Input factors included are heat transfer pathways, which are sensitive to the temperature changes, as well as thermal propagation time. Control factors are the design or process parameters that engineers use to establish the functional performance of a system. The noise factors capture material variability and manufacturing tolerances affecting thermal properties. Output responses included the maximum cell temperature Versus time, thermal propagation time to adjacent cells, and total propagation duration across the module, measured in minutes. The validated 1D GT-SUITE model shows strong correlation with experimental data, confirming its reliability to predict thermal propagation time and supporting safer, thermally optimized battery pack designs. The validated model can be integrated into system (battery pack) level 1D thermal simulations, offering a calibrated model for future pack level propagation studies and supporting the development of safer, thermally optimized battery architectures.
Dixit, Manish, Raja, Vinayak, Gudiyella, Soumya
Application of DFSS for Optimizing Thermal Protection in Electric Vehicles during Battery Thermal Runaway Events2026-01-01424/7/2026
Battery thermal runaway is a major safety concern in electric vehicles because of the extreme heat and hazardous gases released during cell failure. These venting events can quickly raise the temperature of the battery enclosure and cabin floor, threatening occupant safety. To address this challenge, this study employs the Design for Six Sigma (DFSS) methodology to design and optimize a thermal protection system that delays and limits heat transfer to the cabin. A physics-based transient heat-transfer model was combined with DFSS principles to systematically evaluate insulation materials, shield layouts, surface emissivity, and layer geometry. An L-18 orthogonal array was used to identify key parameters and quantify their influence on thermal robustness. The optimized architecture reduced cabin-floor temperature rise under severe runaway conditions (600–900 °C vent gas), meeting occupant-egress safety requirements. Findings confirm DFSS as an effective framework for developing high-robustness EV thermal protection systems under uncertainty and extreme boundary conditions.
El-Sharkawy, Alaa, Asar, Mona, Taha, Nahla, Sheta, Mai
Effects of Obstacle Height and Vehicle Roll Behavior on Electric Vehicle Side Impact Safety2026-01-04054/7/2026
Electric vehicles (EVs) face unique safety challenges under pole side impact conditions, largely due to the presence of floor-mounted battery packs. Existing regulatory test procedures, such as FMVSS 214, primarily address occupant injury using full-height cylindrical obstacles. These procedures were originally developed for internal combustion vehicles (ICVs). However, real-world roadside crashes frequently involve obstacles of varying heights, such as guardrails, curbs, and median bases. While these obstacles pose limited risk to the passenger compartment, they can intrude into the battery pack and trigger thermal runaway. This study investigates the influence of obstacle height on EV pole side impacts. Finite element simulations of a commercially available sedan were conducted against rigid obstacles of different heights. Results reveal a non-monotonic trend of battery intrusion governed by the interplay between rollover dynamics and structural stiffness. Theoretical analyses were conducted to clarify the underlying mechanisms. When the obstacle height falls below the window frame level, rollover effects become more significant. The longer roll moment arm allows part of the impact energy to be dissipated through vehicle roll motion, leading to a reduction in battery intrusion. However, as the obstacle height is further reduced into the threshold beam and battery side beam region, the supporting structural members are bypassed. The equivalent contact stiffness drops sharply, resulting in significant battery intrusion. The findings demonstrate that obstacle height governs EV battery safety through a competition between rollover energy dissipation and reduced contact stiffness. This work provides new insights for extending existing side impact tests to low-height obstacles and offers guidance for vehicle safety design.
Ma, Chenghao, Xing, Bobin, Zhou, Qing, Xia, Yong
A CFD Based Multi-Physics Modeling Approach to Predict Vent Gas Flow Paths Due to Can Melting during Prismatic Cell Thermal Runaway2026-01-04074/7/2026
As the automotive industry increasingly adopts high-energy-density batteries, ensuring vehicle safety against catastrophic thermal runaway (TR) has become paramount. Predicting the complex failure sequence of prismatic cells, requires high-fidelity simulation tools that can capture tightly coupled physical phenomena. This paper presents a comprehensive, three-dimensional multi-physics Computational Fluid Dynamics (CFD) framework designed to simulate the entire TR event. The simulation originates with a multi-step Arrhenius chemical kinetics model to calculate the heat and gas generated by the primary exothermic reactions. This process drives a rapid increase in internal temperature and pressure, which is resolved by the model’s fluid dynamics solver. The initial vent opening is triggered when this internal pressure exceeds a predefined mechanical burst threshold, simulating a realistic seal rupture. Concurrently, a Conjugate Heat Transfer (CHT) analysis calculates the temperature distribution throughout the solid cell components. These predicted high temperatures are then utilized by a solidification/melting phase-change model to account for the subsequent melting of the aluminum can material. This melting creates additional, evolving pathways for the venting of internally generated gas. By integrating these distinct but interconnected failure mechanisms, the framework provides a high-fidelity analysis of the complete TR sequence, serving as a critical engineering tool for the development of safer battery systems.
Mukherjee, Swarnava, Schlautman, Jeff, Srinivasan, Chiranth
A Pulsed-Charging-Current Heating Method for Lithium-Ion Batteries at Low Temperature2026-01-70042/27/2026
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 provides an effective solution for the low-temperature, low state of charge (SOC) application scenario in electric vehicles.
Xiao, Yuechan, Huang, Xinrong, Wu, Ze, Zhang, Yipu, Meng, Jinhao
Research on State of Charge Estimation Method for Lithium Iron Phosphate Batteries Based on Expansion Force2026-01-70402/27/2026
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 algorithm significantly outperforms traditional electrical signal-based methods, with cumulative SOC estimation errors not exceeding 2.2%. The approach exhibits higher accuracy, improved environmental adaptability, and enhanced robustness. This research confirms the feasibility and effectiveness of using expansion force as a non-electrical quantity for SOC estimation, providing a new perspective for high-precision battery state assessment.
Du, Jinqiao, Rao, Bo, Tian, Jie, Wu, Yizeng, Xu, Haoming, Jiang, Jiuchun
Risk of Safety Abuse Testing and Safety Environment Control Strategies for Lithium-Ion Batteries2026-01-70082/27/2026
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 battery safety abuse laboratories, aiming to provide useful references for the healthy and orderly development of the new energy industry and the practice of social responsibility from a practical perspective.
Ren, Gaohui, Liu, Lei, Jiang, Chenglong, Sun, Zhipeng, Chen, Liduo
Research on the Repeatability of Thermal Runaway of Lithium-Ion Batteries Triggered by Heating Wires2026-01-70272/27/2026
Due to limitations in available battery samples and testing costs, lithium-ion battery thermal runaway experiments are not practical to repeat multiple times, and the reliability of experimental results is frequently questioned. To systematically evaluate the repeatability of the heating wire-triggered method in thermal runaway tests, this study investigates two types of commercial 18650 cylindrical batteries with NCM/graphite chemistry under different heating power levels and health conditions. The results indicate that under the same heating power, batteries of the same type exhibit good repeatability in thermal runaway onset time and onset temperature, with the consistency of onset time outperforming that of onset temperature. As the heating power increases, the onset time of thermal runaway decreases significantly, while the variation in onset temperature remains relatively small. Compared to fresh batteries, aged batteries show reduced variability in thermal runaway characteristics, with standard deviations in onset time generally below 7 s, the range is less than 15 s, indicating improved repeatability. The heating wire-triggered method demonstrates stable and reliable repeatability under different power levels and aging states. This study provides critical data and technical references for the standardization of lithium-ion battery thermal runaway testing, offering valuable engineering guidance for battery safety assessment.
Wang, Jia, Yan, Hongtao, Zhang, Yuemeng, Lin, Chunjing, Lao, Li
Real-Time Fault Diagnosis and Reliability Analysis in Lithium-Ion BESS via Bayesian Fault Propagation Networks2026-01-70232/27/2026
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 Expectation–Maximization (EM) algorithm. Conditional Probability Tables (CPTs) are recalculated through Maximum Likelihood Estimation (MLE) with logical relationships to achieve accurate and adaptive fault probability estimation. A multi-feature fusion indicator, the State Severity Indicator (SSI), is further introduced to evaluate system health in real time. A qualitative comparison with representative fault modeling and detection approaches—including Bayesian Network, FTA-DBN, and various machine learning methods—shows that the proposed BFPN offers a well-balanced trade-off between interpretability and real-time performance. Simulation experiments under both single- and multiple-fault scenarios demonstrate that the proposed framework accurately detects typical fault events and provides early warnings before fault escalation. Under complex coupled fault conditions, it effectively captures fault interactions and predicts cascading failures across subsystems and the overall BESS, showing strong robustness and diagnostic capability for real-time reliability assessment in modern energy storage systems.
Yang, Zhan, Chen, Xiaobo, Zheng, Ruixiang, Li, Mian
Electro-Thermal Co-Optimization of Battery Modules Based on 3D Thermal Model Simulation2026-01-70292/27/2026
As an important energy storage device and the power source for key equipment such as automobiles and drones at present, lithium-ion batteries generate a substantial amount of heat during their operation. Without an effective cooling system, the temperature of the battery module can rise, significantly impacting the battery's service life and safety performance. Therefore, automotive battery modules require an efficient battery thermal management system to regulate heat dissipation and extend battery life. We note that many existing vehicle battery thermal management systems focus solely on the surface temperature of the battery. However, uneven heat distribution within the battery can also lead to issues such as unbalanced aging and thermal runaway safety hazards. Thus, we specifically emphasize the internal temperature distribution of the battery, focusing on internal temperature optimization design and simulation. Taking the battery module equipped with the third-generation NCM 9-series high-nickel CVD silicon-carbon anode semi-solid battery cells as an example, this paper designs an integrated electro-thermal simulation and optimization scheme for the interior of electric vehicles, as well as an external heat exchange device capable of efficiently exchanging heat with the interior. By establishing a 3D thermal model of the battery, conducting a series of simulations, and comparing the results with the corresponding experimental data, this study not only obtains a relatively comprehensive 3D thermal model and thermal simulation process, but also develops an optimized thermal management solution for the battery module.
Wu, Jiayi, Zheng, Bowen, Kang, Mengran, Zhan, Wenwei, Qi, Ji, Yi, Yong
Electro-Thermo-Mechanical Simulation and Machine Learning for Temperature Prediction of Lithium-Ion Batteries under Mechanical Abuse2026-01-70412/27/2026
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 outside the original design domain, with prediction errors under 5% in 93.1% of test cases. The results demonstrate the effectiveness of integrating machine learning with physics-based modeling for reliable, data-efficient prediction of battery behavior under abusive conditions, offering new insights into battery safety design and real-time risk assessment.
Wan, Cheng, Zhan, Zhenfei, Chen, Qiuren
Joint Estimation of State of Charge and Capacity throughout the Full Service Life Cycle for Li-Ion Batteries2026-01-70212/27/2026
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
Fault Diagnosis Method of Lithium-Ion Batteries Based on OCSVM and RLMQD Algorithms2026-01-70202/27/2026
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 is quantified through scale-weighted fusion and normalization, enabling accurate evaluation across diverse abnormal modes. Finally, The diagnostic performance of the proposed method is comprehensively validated through three sets of simulation experiments and real-vehicle data collected under realistic operating conditions. The results demonstrate that the proposed method accurately identifies both single-point and clustered anomalies, corresponds closely with actual fault conditions and exhibiting strong generalization capability. In real vehicle validation, the method achieves 95.79% accuracy, 100% recall, and a 93.3% F1 score in abnormal detection tasks. Furthermore, It demonstrates robustness and interpretability, enabling multi-type abnormal detection and fault severity evaluation without reliance on extensive fault datasets, thereby offering high suitablility for online monitoring and early warning in actual Battery Management Systems.
Wei, Fuxing, Yang, Libing, Wang, Zonglei, Xia, Xuelei, Shen, Jiangwei, Chen, Zheng
Lithium Plating-Induced Surface Pressure Evolution in Lithium-Ion Pouch Cells under Rigid Constraints2026-01-70132/27/2026
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). This pattern is largely driven by the reversible volume changes in the electrodes as lithium ions are alternately inserted (intercalation) and removed (deintercalation) during electrochemical cycling of the cells. At 0 °C, slower ion transport and reaction kinetics promoted lithium plating, causing irreversible anode expansion and a continuous rise in surface pressure. Concurrently, the depletion of active lithium diminished the electrode’s maximum achievable state of charge (SOC). This limitation curtailed the degree of electrode expansion and contraction throughout charge–discharge cycles, resulting in a decrease in the amplitude of pressure fluctuations on the battery surface during cycling. Numerical simulations confirmed that lithium plating and SOC degradation collectively shaped the mechanical response at low temperatures. The proposed model accurately replicates experimental pressure evolution and distinguishes between reversible and irreversible contributions to volume changes. This work reveals how temperature and mechanical loading jointly regulate surface pressure and capacity retention, offering insights relevant to battery pack design and the optimization of low-temperature performance.
Du, Yingyue, Chen, Ying, Luan, Weiling, Chen, Haofeng
A Hierarchical Fault Detection Method for Lithium-Ion Batteries in Real-World Vehicle Applications2026-01-70062/27/2026
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 fusion. In the first layer, decentralized voltage data are standardized using the 3σ rule to identify potentially anomalous batteries. In the secondary analysis phase, a sliding time-window mechanism is adopted to dynamically capture voltage sequences. Within each window, voltage variations are calculated along both vertical and horizontal directions, and statistical metrics, including mean, standard deviation, range, and increment are derived. Principal component analysis is then applied to extract key features, and battery anomalies are evaluated and confirmed based on the maximum LOF score. Experimental validation using datasets from vehicles that experienced thermal runaway events, along with data from 1,000 normal vehicles, demonstrates that the proposed method significantly improves the accuracy of battery fault detection. It also provides early warnings up to 17 days prior to the occurrence of thermal runaway.
Gao, Zhengpeng, Gao, Pingping, Chang, Penghui, Liu, Gang, Wu, Ji
Thermal Runaway Characteristics of LMFP Hybrid Batteries at Different State of Charge2026-01-70192/27/2026
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 undergo complete TR. The self-heating temperature (T₁) increases as the voltage decreases. However, comparative analysis of 3.9 V and 4.2 V batteries indicates that the oxidation state of NCM has the most significant impact on peak TR temperature. Furthermore, the severity of TR weakens with decreasing voltage, whereas the explosion hazard from vented gases intensifies, peaking at 3.5 V. This work fills a critical research gap in understanding the cell-level TR behavior of LMFP-based batteries, providing a theoretical foundation for their further optimization.
Guo, Zhenquan, Wu, Senming, Luan, Weiling, Chen, Ying, Chen, Haofeng
Effects of Salt Spray Corrosion on Thermal Runaway of Lithium-ion Batteries2026-01-70312/27/2026
The increasing electrification of marine equipment underscores the need to ensure lithium-ion battery (LIB) safety in corrosive environments. Unlike land applications, shipboard batteries are continuously exposed to salt spray, which accelerates material degradation and raises the risk of thermal hazards. Thus, this study investigates the effects of salt spray corrosion on the electrochemical performance and Thermal runaway (TR) behavior of commercial 18650-type ternary LIBs. Through charge-discharge calorimetry and cone calorimeter tests, variations in voltage response, capacity fade, mass loss, and heat release rate were analyzed under different states of charge (SOC), states of health (SOH), and exposure durations. The results show that corrosion significantly accelerates electrode deterioration, leading to faster capacity decline and voltage plateau shifts. At higher SOH, casing rupture induced earlier TR with violent combustion, whereas at lower SOH, corrosion-induced energy depletion delayed onset and reduced flame intensity. Mass loss increased with decreasing SOH, while the effective heat of combustion remained relatively stable. Thus, salt spray intrusion can cause damage to the internal structure of the battery, inhibit electrochemical reactions, and significantly reduce the operational function, thereby bringing potential safety risks.
Tao, Liyanyu, Shi, Xinyuan, Yang, Qinyuan, Liu, Jiahao
Rapid Battery RUL Prediction under Different Fast-Charging Protocols via Domain-Adaptive Deep Learning and Model Compression2026-01-70032/27/2026
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 maintains low prediction errors while reducing parameters by 42.32%. Further, an explainable algorithm quantifies regional data contributions to identify critical voltage intervals. Ultimately, precise predictions are achieved using only key data from the 2.9–3.2V range, fully demonstrating the method's efficiency. This study provides a lightweight and interpretable solution for cross-domain battery RUL prediction under fast-charging conditions.
Huang, Mingyue, Chen, Hongxu, Luan, Weiling
Study on the Evolution of Precise Internal Short Circuit Damage in Li-Ion Batteries2026-01-70252/27/2026
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, thereby compromising structural integrity and impeding effective characterization. This study introduces an innovative approach utilizing semi-insulated nails to precisely trigger internal short circuit in lithium-ion batteries. This method affords accurate control over the location of internal short circuit within the battery, mitigating the exaggerated spread effect inherent to traditional nail penetration techniques and enhancing the fidelity of internal short circuit simulation. Despite the immediate risk of thermal runaway being relatively low following precisely triggered internal short circuit, and external parameters such as voltage and temperature showing no significant deviations from normal batteries, undetected internal short circuit poses substantial latent risks to the safety and performance of electric vehicles. These inconsistencies become particularly pronounced under conditions of abuse, such as short circuits or overcharging, wherein the safety performance diverges significantly from that of unaffected batteries. Comparative analysis through overcharging and short-circuit safety tests following traditional internal short circuit events facilitates a more thorough investigation into safety reliability and failure evolution, thereby elucidating the underlying discrepancies and associated safety implications.
Sun, Zhipeng, Ma, Tianyi, Han, Ce, Wang, Fang, Ren, Gaohui
Transient Modeling and PID-Controlled Nanofluid Cooling for Enhanced Thermal Management in Proton Exchange Membrane Fuel Cells2026-01-70302/27/2026
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 nanofluid, except when the Newton concentration is 0%. This shows that there is a nonlinear relationship between nanoparticle load and hydrodynamic behaviour, and optimisation must be carefully considered when designing a cooling system in real life.
Zhang, Xiaoliang, Deng, Yikang, Zhao, Yanli, Wang, Qi, Luo, Shengfeng
Design and Performance Study of Highly Reliable Marine Battery Based on Solid Polymer Electrolyte2026-01-70352/27/2026
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 polyurethane electrolyte and the effect of high dielectric constant polycaprolactone on the dissolution and dissociation of lithium salts was studied. We found that polycaprolactone, as the soft segment, exhibits greater electronegativity and provides more oxygen atoms for coordination with lithium ions, which is crucial for enhancing lithium ion transport and improving ionic conductivity.The prepared PU-based solid polymer electrolyte has a conductivity of up to 2.4 × 10-4S/cm, lithium ion migration number 0.78, electrochemical stability window 4.74V. The tensile strengths of PU-based solid polymer electrolyte can reach 2.36 MPa, that balance ionic conductivity and mechanical strength.Besides, it also possesses excellent thermal stability.The symmetrical battery assembled based on the prepared PU-based solid polymer electrolyte exhibits excellent cycling stability (400 hours). The assembled solid-state lithium metal battery based on LiFePO4 can stably cycle for 200 cycles at a current density of 1C, with a Coulombic efficiency of over 99% and a capacity retention rate of up to 99.4%, demonstrating exceptional reliability.
Yuan, Meng, Tang, Qing, Yu, Gongye
Revealing the Thermal-Gas Stability of Lithium Ion Batteries under Low-Temperature Cyclic Aging2026-01-70282/27/2026
With high energy density and long cycle life, lithium-ion batteries (LIBs) are currently the most promising electrochemical devices for electric vehicles and energy storage. However, the safety and reliability of LIBs can be significantly compromised in low-temperature cyclic due to anode lithium plating and other factors which are still unclear. Therefore, it is essential to reveal the thermal-gas stability of LIBs under low-temperature cyclic. This study investigates the thermal runaway (TR) characteristics and gas production characteristics after TR of 18650-type NCA LIBs across four states of health (SOH), from 100% to 70%. Using Glove box, Electrochemical impedance spectroscopy, Scanning electron microscope, X-ray photoelectron spectroscopy, Accelerating rate calorimetry, and Gas chromatography, the research identifies critical trends in temperature rate, gas composition and explosion risk. After around 150 cycles, there is a significant and rapid decline in capacity. The internal resistance of batteries continues to increase, lithium is precipitated on the anode, and the cathode experiences particle fragmentation. Comparing the 70% SOH batteries with the 100% SOH, it is observed that more Li2O, Li2CO3 and LiF appeared on the anode. The triggering time of TR was 41.38% earlier, and the maximum temperature during TR decreased by 7.89%. The mass loss of the 70% SOH batteries were 11.85% higher than that of the 100% SOH. The gas production volume of the 80% SOH is the lowest, while that of the 70% SOH is the highest. Compared with the 100% SOH batteries, the upper limit (UEL) of gas production explosion for 70% SOH decreases by 3.67%, while the lower limit (LEL) increases by 24.46%. This indicates that the gas production of fresh batteries has a wider range of explosion limits. These research findings provide crucial insights for enhancing the safety and reliability of LIBs during operation, storage, and recycling processes.
Wang, Hailong, Wu, Senming, Luan, Weiling, Chen, Haofeng
A Thin-Film Flexible Temperature-Strain Multimodal Sensor for Lithium-Ion Battery Monitoring Based of Laser Direct Writing2026-01-70262/27/2026
Multimodal sensors, capable of simultaneously acquiring multiple physical or chemical signals, have shown broad application potential in fields such as health monitoring, soft robotics, and energy systems. However, current multimodal sensors often suffer from complex fabrication processes and signal decoupling challenges, which limit their practical deployment. To address these issues, this work presents a thin-film temperature–strain multimodal sensor (FTSMS) fabricated via laser processing. The temperature-sensing unit, based on the Seebeck effect, achieves a sensitivity of 9.08 μV/°C, while the strain-sensing unit, utilizing BaTiO₃/AlN@PDMS as the sensitive layer, exhibits a gauge factor (GF) of 43.2. By integrating distinct sensing mechanisms (thermovoltage for temperature and capacitance change for strain), the FTSMS enables self-decoupled measurements over 20–90 °C. Applied in LIB monitoring, it successfully captures real-time temperature and strain variations during charge-discharge cycles and provides multidimensional information throughout thermal runaway (TR) processes, enabling early TR warning and supporting safety-oriented battery design.
Wang, Ziwei, Li, Zhenglin, Gao, Yang, Xuan, Fuzhen
Data-Driven Capacity Estimation Method for Retired Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy2026-01-70152/27/2026
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 features for model training. A hybrid deep learning framework integrating the sparrow search algorithm (SSA), convolutional neural networks (CNN), gated recurrent units (GRU), and an attention mechanism was developed. SSA automatically optimize model hyperparameters, mitigating the overfitting risks, while the attention mechanism highlighted informative frequency-domain features, reducing manual feature engineering and enhancing prediction accuracy. Results show excellent performance: for LFP cells, the root-mean-square error (RMSE) and mean absolute error (MAE) are 0.24% and 0.19%, respectively, with a coefficient of determination (R2) of 98.96%; for NCM cells, the RMSE and MAE are 0.99% and 0.88%, with R2 of 97.97%. On the mixed-material dataset, the RMSE, MAE, and R2 reach 0.79%, 0.67%, and 97.84%. These results confirm that the proposed method maintains high accuracy across different cathode chemistries, while significantly reducing testing and modeling costs. The approach shows strong potential for large-scale, automated screening and classification of retired LIBs in practical second-life applications.
Hou, Zhengyu, Luan, Weiling, Sun, Changzheng, Chen, Ying
Analysis of Heat Transfer in Microchannel Heat Sink with Porous Fins for Battery Cooling Module2026-01-70172/27/2026
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, as influenced by the Reynolds number, are studied. The interfacial area between metal foam and coolant flow is the main factor affecting the heat sink performance. Thermal enhancement is observed with both the decreased metal foam filling width and the increased filling height. As the Reynolds number increases, heat transfer improves. The growth ratio of the Nusselt number is decreased in higher Reynolds number regimes, thus yielding better comprehensive performance in lower Reynolds number regimes. The reduced thermal resistance defined by the entransy dissipation indicates that the modified heat sink can achieve a stronger convective heat transfer effect. This heat transfer enhancement is also evidenced by the decreased synergy angle.
Zhang, Liyuan, Lai, Huanxin
State of Health Estimation in Lithium-Ion Batteries: Methods, Comparisons, and Future Directions2026-26-02081/16/2026
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, and fusion technology typically involving the combination of multiple estimation methods). This review highlights the strengths and limitations of each technique, offering a comparative analysis that enables researchers and engineers to select the most suitable approach based on system requirements and application constraints. Additionally, this paper emphasizes the importance of reliable SOH estimation in enhancing the safety, longevity, and overall performance of battery-powered systems, and discusses potential future directions for developing more accurate, adaptive, and real-time SOH estimation frameworks. A robust SOH framework can reduce warranty costs for manufacturers, prevent thermal runaways by timely identifying degradation patterns, and improve user trust in E-vehicle technology.
Patel, Parvez, Bhagat, Ayush
Enhancing EV Battery Safety: Progressive Crushing and Energy Absorption Technique2026-26-01891/16/2026
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
Preventing Thermal Run-Away using Shape Memory Alloy2026-26-00091/16/2026
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 electrification.
Reginald, Rini, Rout, Saswat, VENKATESH, Muthukrishnan, Chauhan, Ashish Jitendra, Selvaraj, Elayanila
Conceptual Framework for Real-Time Battery Health Estimation in Automotive Digital Twins Using Reinforcement Learning2026-26-06581/16/2026
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 conditions, but beyond that, it is able to aid in deciding and prevent faults. This investigation aims to set a stage for an adaptable, data-driven battery control system in the realm of connected and self-driving EVs, by fiercely defending the concepts of modular openness, edge deploy ability, and critical safety insights.
Pardeshi, Rutuja Rahul, Kondhare, Manish, Sasi Kiran, Talabhaktula
Optimization and Evaluation of Thermal Interface Material Thickness and Distribution Patterns for Improved Heat Transfer in Liquid-Cooled Battery Pack2026-26-04321/16/2026
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 real-world testing was conducted on the battery pack with uneven TIM application and unoptimized distribution patterns, which resulted in significant temperature variations across the pack. In contrast, the application of uniformly optimized TIM thickness reduced these temperature differences by up to 70%, demonstrating the critical impact of consistent interface design on thermal performance. To validate and further understand these findings, combined conduction-convection heat transfer model was developed using ANSYS Fluent to simulate the thermal changes of the battery pack with different TIM thicknesses alongside the unoptimized distribution patterns. The results confirmed that uneven TIM distribution contributes significantly to thermal non-uniformity within the battery pack, whereas optimizing the thickness improves overall thermal performance. Additionally, the optimized application led to a significant reduction in weight of the thermal paste (TIMs) usage, resulting in cost savings and more efficient material utilization.
K, Mathankumar, Jahagirdar, Manasi, Kumbhar, Makarand Shivaji
Steel-Based Laminates for Lithium-ion Pouch Cell of Electric Vehicles2026-26-01861/16/2026
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 material solution for lithium-ion pouch cells.
Singh, Pundan Kumar, Raj, Abhishek, Kumar, Ankit, Chatterjee, Sourabh, Verma, Rahul Kumar, Samantaray, Bikash, Gautam, Vikas, Pandey, Ashwani
Comprehensive Insights on Techniques for Managing and Preventing Thermal Runaway in EV Lithium-Ion Batteries2026-26-01711/16/2026
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 hypothesis-driven investigation explores the distinct thermal runaway risks associated with compact battery formats, supported by simulated abuse and accelerated aging validation. The paper concludes by proposing advanced cooling strategies, improved battery chemistries, and safer architectural designs, aligned with evolving industry safety standards. By bridging scientific analysis with practical engineering and regulatory insight, this work offers high-impact, actionable solutions to support the development of safer, more resilient EV battery systems and to accelerate the transition toward sustainable electric mobility.
Jain, Gaurav, Premlal, P, Pathak, Rahul, Gore, Pandurang
Kinetic Modeling and Adaptive Time Stepping for Battery Thermal Runaway Propagation14-15-01-00031/12/2026
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 in this work to address the accuracy, convergence, and stability issues of the CFD solver. The proposed timescale contains in the definition the reaction rate, reaction enthalpy, and total enthalpy content of the system. As the thermal runaway progresses, the CFD solver time step is obtained dynamically from the defined timescale. The developed solvers and the adaptive time-stepping method were quite intensively tested and analyzed by using different reaction mechanisms representing different battery cells and test conditions. The analysis of the timescales and the adaptive time stepping proved quite efficient for solution accuracy, simulation time, and solver stability.
Chittipotula, Thirumalesha
Constraint Enforcement of a Lithium-Ion Battery Pack Safety-Critical System for Electric Hybrid Vehicles2025-36-016012/18/2025
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 manage the temperature of a safety-critical battery pack system. This approach reduces computational complexity using a single-step horizon, making it suitable for real-time applications. We applied Constraint Enforcement to a battery pack thermal system to assess this strategy’s effectiveness and practical implications in a thermal management context. We compared its performance to a conventional PID controller commonly used in industrial applications. Numerical simulations demonstrate that the Constraint Enforcement approach successfully maintains battery temperature within safe operational limits under varying load and environmental conditions, outperforming the PID controller in critical scenarios where constraint violations would occur. Furthermore, the results highlight the trade-offs between responsiveness and constraint satisfaction, offering valuable insights into the practical deployment of constraint-aware controllers in battery management systems. This study shows that Constraint Enforcement provides a promising alternative for safety-critical thermal control, balancing performance and safety with manageable computational demand, as well as demonstrating the ease of implementing it into an existing controlled system.
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 Aircraft2025-99-040512/10/2025
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 show that the impact of thermal runaway of non-rechargeable lithium batteries is acceptable. Equipment-level tests combined aircraft-level safety analysis finally show the non-rechargeable lithium battery compliance. This article presents recommended thermal runaway triggering test methods and setups of non-rechargeable lithium batteries.
Zhang, Xiaoyu, Zheng, Jian, Yang, Dianliang, Sheng, Jiaqian
TOC99-07-06-0TOC12/2/2025
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Tobolski, Sue
Experts agree on need for large-scale fire testing of battery energy storage systems25AUTP12_1612/1/2025
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 Batteries2025-01-052311/25/2025
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 thermal and mechanical coupling between neighboring cells, influenced by layout, cooling design, and enclosure properties. The core contribution of this study lies in a detailed modeling approach that focuses exclusively on the chemical and thermal decomposition reactions occurring at the electrode scale. These reactions form the foundational layer of a broader simulation framework to be developed in subsequent work. A semi-empirical model is proposed, capturing key phenomena such as electrolyte decomposition, solid electrolyte interphase breakdown, and active material reactions. The model integrates thermal conduction and heat generation from exothermic reactions to characterize the local temperature evolution during the early stages of thermal runaway. By isolating and accurately representing these fundamental decomposition pathways, this modeling approach provides a critical building block for future extensions toward higher-scale thermal runaway simulations. It offers valuable insights into the onset mechanisms of thermal instability, supporting battery design optimization and risk assessment from the ground up.
Ceylan, Deniz, Kulzer, André Casal, Winterholler, Nina, Weinmann, Johannes, Schiek, Werner
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