Pub Date : 2025-10-17DOI: 10.1016/j.etran.2025.100504
Jing Hu , Caiping Zhang , Haoteng Guo , Jing Xu , Linjing Zhang , Tao Zhu , Yanru Zhang
Electrolyte leakage poses serious safety risks by shortening service life and elevating the risk of thermal runaway. A comprehensive understanding of the failure mechanisms is essential for effective safety management. However, such studies are hindered by the lack of reliable fault imitation methods, poor reproducibility of experimental data, and the complexity of side reactions. To address these challenges, this paper proposes a systematic, analytical framework that integrates reproducible fault imitation, cell regeneration, and systematic in-situ and ex-situ analyses to investigate external behaviors and reveal the failure mechanisms. Failure scenarios are imitated by drilling holes into the annular indentation of the aluminum safety valve. In-situ analyses reveal nonlinear degradation behavior and severe kinetic deterioration, primarily attributed to the degradation of the solid electrolyte interphase (SEI). Ex-situ techniques, including cell regeneration and comprehensive material characterization, are employed to distinguish between the impacts of electrolyte depletion and electrode damage. Electrolyte depletion is identified as the primary failure mechanism, which drives severe kinetic degradation and ultimately causing battery performance deterioration or even failure. In contrast, the electrode structure remains largely intact. Moreover, regeneration experiments have confirmed that partial performance recovery can be achieved through electrolyte replenishment. These methods and findings are expected to offer valuable insights for battery fault detection and recycling strategies.
{"title":"Insights into the failure mechanisms of leaky lithium-ion batteries for electric vehicles by a systematic multiscale analytical framework","authors":"Jing Hu , Caiping Zhang , Haoteng Guo , Jing Xu , Linjing Zhang , Tao Zhu , Yanru Zhang","doi":"10.1016/j.etran.2025.100504","DOIUrl":"10.1016/j.etran.2025.100504","url":null,"abstract":"<div><div>Electrolyte leakage poses serious safety risks by shortening service life and elevating the risk of thermal runaway. A comprehensive understanding of the failure mechanisms is essential for effective safety management. However, such studies are hindered by the lack of reliable fault imitation methods, poor reproducibility of experimental data, and the complexity of side reactions. To address these challenges, this paper proposes a systematic, analytical framework that integrates reproducible fault imitation, cell regeneration, and systematic in-situ and ex-situ analyses to investigate external behaviors and reveal the failure mechanisms. Failure scenarios are imitated by drilling holes into the annular indentation of the aluminum safety valve. In-situ analyses reveal nonlinear degradation behavior and severe kinetic deterioration, primarily attributed to the degradation of the solid electrolyte interphase (SEI). Ex-situ techniques, including cell regeneration and comprehensive material characterization, are employed to distinguish between the impacts of electrolyte depletion and electrode damage. Electrolyte depletion is identified as the primary failure mechanism, which drives severe kinetic degradation and ultimately causing battery performance deterioration or even failure. In contrast, the electrode structure remains largely intact. Moreover, regeneration experiments have confirmed that partial performance recovery can be achieved through electrolyte replenishment. These methods and findings are expected to offer valuable insights for battery fault detection and recycling strategies.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100504"},"PeriodicalIF":17.0,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-13DOI: 10.1016/j.etran.2025.100501
Boryann Liaw , Weihan Li , Luc Raijmakers , Lisen Yan , Haider Adel Ali Ali , Anna Windmüller , Chih-Long Tsai , Dirk Uwe Sauer , Rüdiger-A. Eichel
Data-driven techniques leveraging artificial intelligence (AI) and machine learning (ML) are growing as favorable approaches to overcome challenges in predicting complicated behaviors of battery systems. Yet the data-driven approaches continue to face stiff challenges, including the difficulties in acquiring exhausting resources for data acquisition, managing escalating data quality issues to build robust data-driven capability, and sharing multimodal data from a variety of sources using wide ranges of test and operating conditions, and the lack of a reliable framework to verify and validate data consistency so the accuracy of the heuristic data reductions could be assessed. These challenges undermine the reach of a cost-effective and robust approach to predict battery performance and life with high fidelity for battery management. Here, we look into the root of these challenges and provide exemplified guidance to shed light on future directions, aiming for addressing these issues effectively.
{"title":"Demystifying data-driven approaches for battery electric transportation: Challenges and future directions","authors":"Boryann Liaw , Weihan Li , Luc Raijmakers , Lisen Yan , Haider Adel Ali Ali , Anna Windmüller , Chih-Long Tsai , Dirk Uwe Sauer , Rüdiger-A. Eichel","doi":"10.1016/j.etran.2025.100501","DOIUrl":"10.1016/j.etran.2025.100501","url":null,"abstract":"<div><div>Data-driven techniques leveraging artificial intelligence (AI) and machine learning (ML) are growing as favorable approaches to overcome challenges in predicting complicated behaviors of battery systems. Yet the data-driven approaches continue to face stiff challenges, including the difficulties in acquiring exhausting resources for data acquisition, managing escalating data quality issues to build robust data-driven capability, and sharing multimodal data from a variety of sources using wide ranges of test and operating conditions, and the lack of a reliable framework to verify and validate data consistency so the accuracy of the heuristic data reductions could be assessed. These challenges undermine the reach of a cost-effective and robust approach to predict battery performance and life with high fidelity for battery management. Here, we look into the root of these challenges and provide exemplified guidance to shed light on future directions, aiming for addressing these issues effectively.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100501"},"PeriodicalIF":17.0,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-13DOI: 10.1016/j.etran.2025.100502
Yuhang Song , Xin Jiang , Nawei Lyu , Hongfei Lu , Di Zhang , Hong Li , Yang jin
Lithium-ion batteries (LIBs) are widely used in electric vehicles and battery energy storage systems, but the risks of thermal runaway (TR) pose significant safety challenges. Gas sensing technology offers a promising solution for early TR warning by detecting hazardous gases. This paper provides a comprehensive review of gas generation mechanisms across the lifecycle and systematically analyzes gas generation characteristics under varying conditions (material chemistries, state of charge (SOC), abuse conditions, state of health, environment, battery package and capacity). Based on 247 reported TR cases, principal component analysis with SOC-balanced resampling was applied to quantitatively assess the effects of material chemistries, SOC, and abuse conditions on gas composition. These insights enable more targeted selection of warning gases. Mainstream gas sensors, including metal oxide semiconductor, electrochemical and optical types, are evaluated for their suitability in LIBs safety applications. Furthermore, optimal gas sensor selection strategies are proposed for batteries with different material chemistries, enhancing the precision of early warning systems. Finally, the challenges of gas sensing technologies in TR early warning are analyzed, and an outlook on future development directions is provided, paving the way for more reliable and effective safety strategies.
{"title":"Early warning of lithium-ion battery thermal runaway based on gas sensors","authors":"Yuhang Song , Xin Jiang , Nawei Lyu , Hongfei Lu , Di Zhang , Hong Li , Yang jin","doi":"10.1016/j.etran.2025.100502","DOIUrl":"10.1016/j.etran.2025.100502","url":null,"abstract":"<div><div>Lithium-ion batteries (LIBs) are widely used in electric vehicles and battery energy storage systems, but the risks of thermal runaway (TR) pose significant safety challenges. Gas sensing technology offers a promising solution for early TR warning by detecting hazardous gases. This paper provides a comprehensive review of gas generation mechanisms across the lifecycle and systematically analyzes gas generation characteristics under varying conditions (material chemistries, state of charge (SOC), abuse conditions, state of health, environment, battery package and capacity). Based on 247 reported TR cases, principal component analysis with SOC-balanced resampling was applied to quantitatively assess the effects of material chemistries, SOC, and abuse conditions on gas composition. These insights enable more targeted selection of warning gases. Mainstream gas sensors, including metal oxide semiconductor, electrochemical and optical types, are evaluated for their suitability in LIBs safety applications. Furthermore, optimal gas sensor selection strategies are proposed for batteries with different material chemistries, enhancing the precision of early warning systems. Finally, the challenges of gas sensing technologies in TR early warning are analyzed, and an outlook on future development directions is provided, paving the way for more reliable and effective safety strategies.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100502"},"PeriodicalIF":17.0,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Corrosion and high interfacial contact resistance (ICR) in metallic bipolar plates (BPPs) remain critical challenges limiting the durability of proton exchange membrane fuel cells (PEMFCs). This study employs a dual experimental-machine learning (ML) approach to optimize NbTa alloy coatings deposited on SS316L BPPs via DC-balanced magnetron sputtering. Electrochemical testing and surface characterization were conducted under simulated and accelerated PEMFC conditions, while an artificial neural network (ANN) model was developed to predict performance trends across coating thicknesses. A 2.5 μm coating exhibited the best overall performance, reducing corrosion current density to below 0.2 μA cm-2 and ICR to 0.9 mΩ cm2. Notably, the 1.7 μm coating also met U.S. DOE targets, representing a practical balance between cost and durability. The ANN model achieved high predictive accuracy (R2 = 0.992), validating its use in guiding experimental optimization. A preliminary techno-economic assessment indicated that NbTa alloy coatings could achieve favorable payback periods of only a few years under plausible manufacturing scenarios, reinforcing their potential for large-scale PEMFC deployment. This integrated experimental-ML framework offers a powerful strategy for accelerating the development of corrosion-resistant, conductive coatings tailored for advanced PEMFC applications.
{"title":"Machine learning-assisted optimization of NbTa alloy coating thickness via DC magnetron sputtering for SS316L bipolar plates in PEMFCs","authors":"Yasin Mehdizadeh Chellehbari , Pramoth Varsan Madhavan , Mohammadhossein Johar , Leila Moradizadeh , Abhay Gupta , Xianguo Li , Samaneh Shahgaldi","doi":"10.1016/j.etran.2025.100500","DOIUrl":"10.1016/j.etran.2025.100500","url":null,"abstract":"<div><div>Corrosion and high interfacial contact resistance (ICR) in metallic bipolar plates (BPPs) remain critical challenges limiting the durability of proton exchange membrane fuel cells (PEMFCs). This study employs a dual experimental-machine learning (ML) approach to optimize NbTa alloy coatings deposited on SS316L BPPs via DC-balanced magnetron sputtering. Electrochemical testing and surface characterization were conducted under simulated and accelerated PEMFC conditions, while an artificial neural network (ANN) model was developed to predict performance trends across coating thicknesses. A 2.5 μm coating exhibited the best overall performance, reducing corrosion current density to below 0.2 μA cm<sup>-2</sup> and ICR to 0.9 mΩ cm<sup>2</sup>. Notably, the 1.7 μm coating also met U.S. DOE targets, representing a practical balance between cost and durability. The ANN model achieved high predictive accuracy (R<sup>2</sup> = 0.992), validating its use in guiding experimental optimization. A preliminary techno-economic assessment indicated that NbTa alloy coatings could achieve favorable payback periods of only a few years under plausible manufacturing scenarios, reinforcing their potential for large-scale PEMFC deployment. This integrated experimental-ML framework offers a powerful strategy for accelerating the development of corrosion-resistant, conductive coatings tailored for advanced PEMFC applications.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100500"},"PeriodicalIF":17.0,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-13DOI: 10.1016/j.etran.2025.100494
Kyunghyun Kim , Kyeongeun Cho , Kwangho Lee , Junyoung Yoon , Jung-Il Choi
The traditional paradigm of battery research, primarily rooted in controlled laboratory experiments, is being fundamentally reshaped by the influx of real-world field data. Although laboratory tests remain indispensable for isolating specific electrochemical mechanisms, they fall short of capturing the complex phenomena that arise under practical operating conditions. Field data offers essential insights into this complexity by revealing the intricate interplay among dynamic loads, thermal transients, and path-dependent degradation—interactions often obscured in simplified test protocols. This discrepancy underscores a significant gap in understanding, highlighting that field data is not merely a validation tool, but a vital source for uncovering new physics governing battery performance and aging in realistic environments. Harnessing this potential requires addressing critical challenges—from data quality and privacy to the integration of emerging methodologies in feature engineering, fleet analytics, and physics-informed machine learning. This review surveys large-scale fleet datasets alongside high-resolution vehicle- and cell-level measurements, and examines methodologies spanning state estimation, fault detection, and energy optimization. These developments collectively point to a paradigm shift in battery research—from passive diagnostics toward proactive lifecycle management. Ultimately, this trajectory leads to generalized battery foundation models: continuously evolving digital twins that actively shape, rather than merely predict, a battery’s entire lifecycle.
{"title":"Battery field data and why it matters: Foundations for real-world electric vehicles","authors":"Kyunghyun Kim , Kyeongeun Cho , Kwangho Lee , Junyoung Yoon , Jung-Il Choi","doi":"10.1016/j.etran.2025.100494","DOIUrl":"10.1016/j.etran.2025.100494","url":null,"abstract":"<div><div>The traditional paradigm of battery research, primarily rooted in controlled laboratory experiments, is being fundamentally reshaped by the influx of real-world field data. Although laboratory tests remain indispensable for isolating specific electrochemical mechanisms, they fall short of capturing the complex phenomena that arise under practical operating conditions. Field data offers essential insights into this complexity by revealing the intricate interplay among dynamic loads, thermal transients, and path-dependent degradation—interactions often obscured in simplified test protocols. This discrepancy underscores a significant gap in understanding, highlighting that field data is not merely a validation tool, but a vital source for uncovering new physics governing battery performance and aging in realistic environments. Harnessing this potential requires addressing critical challenges—from data quality and privacy to the integration of emerging methodologies in feature engineering, fleet analytics, and physics-informed machine learning. This review surveys large-scale fleet datasets alongside high-resolution vehicle- and cell-level measurements, and examines methodologies spanning state estimation, fault detection, and energy optimization. These developments collectively point to a paradigm shift in battery research—from passive diagnostics toward proactive lifecycle management. Ultimately, this trajectory leads to generalized battery foundation models: continuously evolving digital twins that actively shape, rather than merely predict, a battery’s entire lifecycle.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100494"},"PeriodicalIF":17.0,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-10DOI: 10.1016/j.etran.2025.100498
Bingbing Hu , Qi Zhang , Dafang Wang , Ziwei Hao , Xuan Liang , Kai Xiong , Dianbo Ren
Reliable early warning of lithium-ion batteries (LIBs) thermal runaway (TR) remains a pivotal yet unresolved challenge in battery safety research. Given the escalating risks of LIB fire hazards, developing timely and reliable early-stage TR detection methods holds significant practical importance. In this study, we conducted TR experiments triggered by overheating on pouch cells at varying states of charge (SOC). A rapid impedance testing platform was established to monitor real-time impedance at five characteristic frequency points during TR progression. Concurrently, parameters including temperature, voltage, and impedance were analyzed throughout the process. The TR event was divided into four distinct phases based on the evolution of impedance: heat conduction-dominated phase, gas generation-dominated phase, partial internal short circuit-dominated phase, and thermal runaway phase. Based on impedance characteristics at specified frequencies and their corresponding TR mechanisms, a two-level early warning strategy was developed. This method successfully achieved TR warning and demonstrated a 93.1 % alert time ahead of significant voltage drop or intense temperature rise in validation experiments using an NCA cell. These findings provide critical insights for enhancing the monitoring capabilities of battery management systems (BMS) and improving LIB safety.
{"title":"Early warning strategy for overheating-induced thermal runaway in lithium-ion batteries based on fast impedance measurement","authors":"Bingbing Hu , Qi Zhang , Dafang Wang , Ziwei Hao , Xuan Liang , Kai Xiong , Dianbo Ren","doi":"10.1016/j.etran.2025.100498","DOIUrl":"10.1016/j.etran.2025.100498","url":null,"abstract":"<div><div>Reliable early warning of lithium-ion batteries (LIBs) thermal runaway (TR) remains a pivotal yet unresolved challenge in battery safety research. Given the escalating risks of LIB fire hazards, developing timely and reliable early-stage TR detection methods holds significant practical importance. In this study, we conducted TR experiments triggered by overheating on pouch cells at varying states of charge (SOC). A rapid impedance testing platform was established to monitor real-time impedance at five characteristic frequency points during TR progression. Concurrently, parameters including temperature, voltage, and impedance were analyzed throughout the process. The TR event was divided into four distinct phases based on the evolution of impedance: heat conduction-dominated phase, gas generation-dominated phase, partial internal short circuit-dominated phase, and thermal runaway phase. Based on impedance characteristics at specified frequencies and their corresponding TR mechanisms, a two-level early warning strategy was developed. This method successfully achieved TR warning and demonstrated a 93.1 % alert time ahead of significant voltage drop or intense temperature rise in validation experiments using an NCA cell. These findings provide critical insights for enhancing the monitoring capabilities of battery management systems (BMS) and improving LIB safety.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100498"},"PeriodicalIF":17.0,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-10DOI: 10.1016/j.etran.2025.100497
Yiwen Zhao , Zhenpo Wang , Lisen Yan , Zhenyu Sun , Peng Liu , Lei Zhang , Weihan Li
As lithium-ion batteries continue to empower the global shift toward transportation electrification and renewable energy integration, ensuring their reliable, long-term and safe operation has emerged as a topmost challenge. Despite extensive research on both degradation mechanisms and catastrophic failures such as thermal runaway, these phenomena are often investigated in isolation, hindering the development of comprehensive safety strategies. This review bridges the critical gap between battery degradation and safety by establishing a unified framework that connects gradual degradation processes, fault evolution and extreme risks. We systematically examine how electrochemical degradation influences the emergence of safety-critical events and emphasize the importance of diagnostic strategies capable of identifying performance degradation, detecting early-stage faults and predicting impending thermal hazards. Such insights not only enhance safety risk awareness but also enable proactive interventions across the battery lifecycle. Looking ahead, we provide guidance on key pathways toward lifecycle-aware battery management system development and scalable methods for large-scale deployment.
{"title":"Bridging battery degradation and safety: Challenges and opportunities","authors":"Yiwen Zhao , Zhenpo Wang , Lisen Yan , Zhenyu Sun , Peng Liu , Lei Zhang , Weihan Li","doi":"10.1016/j.etran.2025.100497","DOIUrl":"10.1016/j.etran.2025.100497","url":null,"abstract":"<div><div>As lithium-ion batteries continue to empower the global shift toward transportation electrification and renewable energy integration, ensuring their reliable, long-term and safe operation has emerged as a topmost challenge. Despite extensive research on both degradation mechanisms and catastrophic failures such as thermal runaway, these phenomena are often investigated in isolation, hindering the development of comprehensive safety strategies. This review bridges the critical gap between battery degradation and safety by establishing a unified framework that connects gradual degradation processes, fault evolution and extreme risks. We systematically examine how electrochemical degradation influences the emergence of safety-critical events and emphasize the importance of diagnostic strategies capable of identifying performance degradation, detecting early-stage faults and predicting impending thermal hazards. Such insights not only enhance safety risk awareness but also enable proactive interventions across the battery lifecycle. Looking ahead, we provide guidance on key pathways toward lifecycle-aware battery management system development and scalable methods for large-scale deployment.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100497"},"PeriodicalIF":17.0,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-10DOI: 10.1016/j.etran.2025.100499
Ratnak Sok, Jin Kusaka
Controlling battery temperature can reduce cell aging, internal resistance, and overheating, and improve pack performance. These require advanced battery thermal management systems (BTMS) for all-weather driving. A full liquid-cooled pack has hundreds to thousands of cells and coolant channels. Therefore, designing a full pack requires a thorough understanding of battery thermal, flow, and electrical responses under various driving and thermal conditions. This work presents a holistic reverse-engineering method to model and validate a production-based, liquid-cooled, 75-kWh lithium-ion battery pack, including its BTMS via multi-physics simulation. The model includes 4416 cells, 28 side-coolant lines, 784 coolant flow channels, and all plate bends. The channel geometries (width, height, bend angle, radius, and length) are optimized using a genetic algorithm. Firstly, a design-of-experiment is performed by changing the inlet coolant flow rate ( = 0–16 L/min) to measure steady-state and transient pressure drops. A sensitivity analysis of the channel geometries to the coolant flow characteristics is performed for the pack's flow model validation. A full battery-electric SUV equipped with the battery pack and dual e-motors was tested under a 60 km/h driving (winter test with ambient temperature = −10 °C) and repeated WLTC and (FTP75+HWFET) cycles in summer (25–40 °C). The pack performances were recorded under battery heating (initial < initial ) and cooling ( > ) modes. The battery model is based on the 2RC equivalent-circuit model, calibrated against an electrochemical NCA/Gr-SiOx cell model to accelerate simulation time. Using the optimized flow and cell models, the model accurately (over 90 % accuracy) predicts the pack's responses (voltage, state of charge, flow, temperature) under steady-state and dynamic conditions. The detailed approach to building a comprehensive pack model can serve as a guideline for future BTMS development.
{"title":"A multi-physics, fully liquid-cooled battery pack model development for winter-summer driving using a holistic reverse-engineering method","authors":"Ratnak Sok, Jin Kusaka","doi":"10.1016/j.etran.2025.100499","DOIUrl":"10.1016/j.etran.2025.100499","url":null,"abstract":"<div><div>Controlling battery temperature can reduce cell aging, internal resistance, and overheating, and improve pack performance. These require advanced battery thermal management systems (BTMS) for all-weather driving. A full liquid-cooled pack has hundreds to thousands of cells and coolant channels. Therefore, designing a full pack requires a thorough understanding of battery thermal, flow, and electrical responses under various driving and thermal conditions. This work presents a holistic reverse-engineering method to model and validate a production-based, liquid-cooled, 75-kWh lithium-ion battery pack, including its BTMS via multi-physics simulation. The model includes 4416 cells, 28 side-coolant lines, 784 coolant flow channels, and all plate bends. The channel geometries (width, height, bend angle, radius, and length) are optimized using a genetic algorithm. Firstly, a design-of-experiment is performed by changing the inlet coolant flow rate (<span><math><mrow><msub><mi>V</mi><mrow><mi>c</mi><mi>o</mi><mi>o</mi><mi>l</mi></mrow></msub></mrow></math></span> = 0–16 L/min) to measure steady-state and transient pressure drops. A sensitivity analysis of the channel geometries to the coolant flow characteristics is performed for the pack's flow model validation. A full battery-electric SUV equipped with the battery pack and dual e-motors was tested under a 60 km/h driving (winter test with ambient temperature <span><math><mrow><msub><mi>T</mi><mi>a</mi></msub></mrow></math></span> = −10 °C) and repeated WLTC and (FTP75+HWFET) cycles in summer (25–40 °C). The pack performances were recorded under battery heating (initial <span><math><mrow><msub><mi>T</mi><mrow><mi>a</mi><mo>,</mo><mi>i</mi></mrow></msub></mrow></math></span> < initial <span><math><mrow><msub><mi>T</mi><mrow><mi>b</mi><mo>,</mo><mi>i</mi></mrow></msub></mrow></math></span>) and cooling (<span><math><mrow><msub><mi>T</mi><mrow><mi>a</mi><mo>,</mo><mi>i</mi></mrow></msub></mrow></math></span> > <span><math><mrow><msub><mi>T</mi><mrow><mi>b</mi><mo>,</mo><mi>i</mi></mrow></msub></mrow></math></span>) modes. The battery model is based on the 2RC equivalent-circuit model, calibrated against an electrochemical NCA/Gr-SiO<sub>x</sub> cell model to accelerate simulation time. Using the optimized flow and cell models, the model accurately (over 90 % accuracy) predicts the pack's responses (voltage, state of charge, flow, temperature) under steady-state and dynamic conditions. The detailed approach to building a comprehensive pack model can serve as a guideline for future BTMS development.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100499"},"PeriodicalIF":17.0,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-10DOI: 10.1016/j.etran.2025.100495
Zhen Liu , Mingjie Zhang , Kai Yang , Yuhong Jin , Hao Wang , Bin Wei , Jingbing Liu
Sodium-ion batteries (SIBs) have emerged as a promising complementary technology to lithium-ion batteries (LIBs), primarily due to their potential for cost-effectiveness and resource sustainability. However, the thermal safety of SIBs still needs to be evaluated, as it is crucial for their potential application in electric vehicles and energy storage fields. In this study, we systematically examine and compare the thermal runaway (TR) and gas venting behaviors of 185 Ah Cu-Fe-Mn-based sodium-ion (CFM) and 314 Ah LiFePO4 (LFP) batteries under overcharging and overheating conditions-factors. Experimental results indicate that the TR process in CFM batteries exhibits distinct characteristics when compared to LFP batteries. Under overcharging conditions, CFM batteries experience more severe temperature fluctuations than those observed during overheating-maximum TR temperatures reach 620.9 °C and 587.3 °C, respectively-significantly higher than those recorded in LFP batteries. The activation time of the safety valve is similar to the onset of TR in both scenarios. Gas analysis reveals that the primary gaseous compositions generated during TR in CFM batteries are comparable to those produced by LFP batteries, with total gas volumes measuring 397.6 L during overheating and 699.3 L during overcharging. Although CFM batteries demonstrate superior resistance to overcharging relative to LFP counterparts, their elevated TR temperatures coupled with substantial emissions of combustible gases-including hydrogen, carbon monoxide, and methane considerably heighten combustion and explosion risks. These results may contribute to safer integration of CFM batteries in future applications, such as in electric vehicles, charging station and energy storage systems.
钠离子电池（sib）已经成为锂离子电池（lib）的一种有前途的补充技术，主要是因为它们具有成本效益和资源可持续性的潜力。然而，sib的热安全性仍然需要评估，因为它对于其在电动汽车和储能领域的潜在应用至关重要。在这项研究中，我们系统地研究和比较了185 Ah cu - fe - mn基钠离子（CFM）和314 Ah LiFePO4 （LFP）电池在过充和过热条件下的热失控（TR）和排气行为。实验结果表明，与LFP电池相比，CFM电池的TR过程具有明显的特点。在过充条件下，CFM电池的温度波动比过热时更严重，最高TR温度分别达到620.9°C和587.3°C，显著高于LFP电池。在两种情况下，安全阀的激活时间与TR的开始时间相似。气体分析表明，CFM电池在TR过程中产生的主要气体成分与LFP电池相当，过热时的总气体体积为397.6 L，过充时的总气体体积为699.3 L。尽管CFM电池相对于LFP电池具有更强的抗过充能力，但其较高的TR温度加上大量可燃气体（包括氢气、一氧化碳和甲烷）的排放大大增加了燃烧和爆炸的风险。这些结果可能有助于CFM电池在未来的应用中更安全的集成，例如电动汽车、充电站和储能系统。
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Pub Date : 2025-10-01DOI: 10.1016/j.etran.2025.100492
Rongqi Peng , Ping Ping , Depeng Kong , Wei Gao , Gongquan Wang , Yihe Dong , Juntao Huo , Song Zhang , Zehao Li
The high-temperature multiphase flow vented by lithium-ion batteries (LIBs) during thermal runaway (TR) can significantly influence thermal runaway propagation (TRP) within confined battery packs. Quantitative analysis of the heating effect of unignited TR venting on neighboring cells is essential for understanding and predicting TRP behavior, particularly under semi-confined packaging conditions. In this study, we designed a modular experimental platform featuring an adjustable ceiling and peripheral baffles to emulate the semi-confined space of a battery pack. A distributed array of temperature-monitoring plates surrounding the triggered cell was used to record the transient heat flux induced by TR venting. Three critical parameters were systematically investigated in a stepwise spatial confinement framework: ceiling gap, trigger position, and state of charge (SOC). Reducing the ceiling gap from 100 mm to 15 mm markedly intensified the venting-induced heating: the maximum temperature rise of the plate adjacent to the trigger cell increased from approximately 44.1 °C–102.8 °C, while its thermal exposure integral (TEI) more than doubled. Center-triggered venting produced a more uniform but lower-intensity heat distribution over a wider area. In contrast, side-triggered venting-constrained by the enclosure-generated a localized high-heat region, where the maximum temperature rise and TEI on adjacent plates were approximately 20 % higher than in the center case, albeit over a smaller affected zone. Higher SOCs amplified the heating effect: at 100 % SOC, maximum temperature rise and TEI on adjacent plates were nearly double those observed at 50 % SOC. Based on these findings, an empirical heat-flux correlation incorporating multiphase venting effects was derived. While currently applicable to LFP cells under the tested conditions, this methodology can be extended to other battery configurations, supporting TRP modeling and informing future pack-level thermal protection strategies.
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