Etransportation最新文献_第8页

Advancing SOC estimation in LiFePO4 batteries: Enhanced dQ/dV curve and short-pulse methods LiFePO4电池SOC预估：改进的dQ/dV曲线和短脉冲方法

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-11 DOI: 10.1016/j.etran.2025.100466

Yizhao Gao, Simona Onori

Accurate state-of-charge (SOC) estimation for lithium iron phosphate (

{LiFePO}_{4}

) batteries remains challenging due to their inherently flat open-circuit voltage (OCV)–SOC characteristics, which impair observability for conventional voltage-based and equivalent circuit model (ECM) methods. To address this limitation, we propose a DQV-based SOC estimation framework that uses short-duration current pulses to extract informative voltage features. Complete DQV–SOC reference curves are constructed offline across multiple C-rates (

\pm

1/30C,

\pm

0.2C,

\pm

0.5C,

\pm

1C, and

\pm

2C). During operation, voltage responses from brief current pulses are processed via exponential fitting to generate smooth, noise-resilient DQV segments. These segments are fused with the reference data within an Unscented Kalman Filter (UKF), enabling closed-loop SOC estimation with low computational overhead. Experimental results highlight the significant influence of C-rates on the DQV-based SOC estimator. We observe that pulse currents significantly enhance SOC estimation convergence across the full SOC range [0, 1]. However, employing a single C-rate pulse may not ensure robustness across diverse SOC ranges, emphasizing the importance of carefully selecting C-rates to achieve SOC estimation convergence throughout the entire SOC range of [0, 1]. This research contributes to advancing reliable management practices for

{LiFePO}_{4}

batteries in electric vehicles.

由于磷酸铁锂（LiFePO4）电池固有的平坦开路电压(OCV) -SOC特性，影响了传统基于电压和等效电路模型（ECM）方法的可观察性，因此对其进行准确的荷电状态（SOC）估计仍然具有挑战性。为了解决这一限制，我们提出了一种基于dqv的SOC估计框架，该框架使用短持续时间电流脉冲提取信息电压特征。完整的DQV-SOC参考曲线在多种c -rate（±1/30C，±0.2C，±0.5C，±1C和±2C）下离线构建。在运行过程中，通过指数拟合处理短电流脉冲的电压响应，生成平滑、抗噪声的DQV段。这些片段与Unscented卡尔曼滤波器（UKF）中的参考数据融合，以低计算开销实现闭环SOC估计。实验结果表明，c率对基于dqv的SOC估计器有显著影响。我们观察到脉冲电流显著增强了整个SOC范围内SOC估计的收敛性[0,1]。然而，采用单一c -速率脉冲可能无法确保在不同SOC范围内的鲁棒性，这强调了在整个SOC范围内仔细选择c -速率以实现SOC估计收敛的重要性[0,1]。该研究有助于推进电动汽车磷酸铁锂电池的可靠管理实践。

{"title":"Advancing SOC estimation in LiFePO4 batteries: Enhanced dQ/dV curve and short-pulse methods","authors":"Yizhao Gao, Simona Onori","doi":"10.1016/j.etran.2025.100466","DOIUrl":"10.1016/j.etran.2025.100466","url":null,"abstract":"<div><div>Accurate state-of-charge (SOC) estimation for lithium iron phosphate (<span><math><msub><mrow><mi>LiFePO</mi></mrow><mrow><mn>4</mn></mrow></msub></math></span>) batteries remains challenging due to their inherently flat open-circuit voltage (OCV)–SOC characteristics, which impair observability for conventional voltage-based and equivalent circuit model (ECM) methods. To address this limitation, we propose a DQV-based SOC estimation framework that uses short-duration current pulses to extract informative voltage features. Complete DQV–SOC reference curves are constructed offline across multiple C-rates (<span><math><mo>±</mo></math></span> 1/30C, <span><math><mo>±</mo></math></span> 0.2C, <span><math><mo>±</mo></math></span> 0.5C, <span><math><mo>±</mo></math></span> 1C, and <span><math><mo>±</mo></math></span> 2C). During operation, voltage responses from brief current pulses are processed via exponential fitting to generate smooth, noise-resilient DQV segments. These segments are fused with the reference data within an Unscented Kalman Filter (UKF), enabling closed-loop SOC estimation with low computational overhead. Experimental results highlight the significant influence of C-rates on the DQV-based SOC estimator. We observe that pulse currents significantly enhance SOC estimation convergence across the full SOC range [0, 1]. However, employing a single C-rate pulse may not ensure robustness across diverse SOC ranges, emphasizing the importance of carefully selecting C-rates to achieve SOC estimation convergence throughout the entire SOC range of [0, 1]. This research contributes to advancing reliable management practices for <span><math><msub><mrow><mi>LiFePO</mi></mrow><mrow><mn>4</mn></mrow></msub></math></span> batteries in electric vehicles.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100466"},"PeriodicalIF":17.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096464","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}

引用次数: 0

A reconfigurable battery system for a Tesla Model Y: Package and efficiency analysis 特斯拉Y型可重构电池系统：封装与效率分析

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-08 DOI: 10.1016/j.etran.2025.100464

Andreas Wiedenmann , Julian Estaller , Johannes Buberger , Wolfgang Grupp , Manuel Kuder , Antje Neve , Thomas Weyh

This study investigates the integration of a modular multilevel inverter-based reconfigurable battery system into an existing electric vehicle. The aim is to evaluate how such systems can replace conventional traction inverters, battery management systems, and on-board chargers. To this end, a classification of the different topology levels and possible forms of integration of power electronics, control logic, and driver electronics is performed. A Tesla Model Y’s traction battery is redesigned, retaining its structural properties and the 4680 cell format. A package analysis shows that the multilevel system occupies a volume comparable to the conventional battery pack, while the volume previously reserved for dedicated power electronics becomes available. Efficiency simulations demonstrate that the multilevel inverter can increase the overall vehicle efficiency, especially in situations with low driving speeds and high torque requirements. As a result, WLTP energy consumption is reduced from 14.9 kWh/100km to 14.5 kWh/100km. However, the battery efficiency is reduced at higher speeds due to higher cell currents. In addition, the system enables bidirectional charging at full system power, including supply to external loads or the grid, and a more integrated vehicle architecture.

本研究探讨了一种基于多电平逆变器的模块化可重构电池系统与现有电动汽车的集成。目的是评估这种系统如何取代传统的牵引逆变器、电池管理系统和车载充电器。为此，对电力电子器件、控制逻辑和驱动电子器件的不同拓扑级别和可能的集成形式进行了分类。特斯拉Y型的牵引电池进行了重新设计，保留了其结构特性和4680电池格式。封装分析表明，多电平系统占用的体积与传统电池组相当，而以前为专用电力电子设备保留的体积变得可用。效率仿真结果表明，多电平逆变器可以提高整车效率，特别是在低速和高转矩要求的情况下。因此，WLTP能耗从14.9千瓦时/100公里降低到14.5千瓦时/100公里。然而，由于更高的电池电流，电池效率在更高的速度下会降低。此外，该系统还可以在全系统功率下进行双向充电，包括向外部负载或电网供电，以及更集成的车辆架构。

{"title":"A reconfigurable battery system for a Tesla Model Y: Package and efficiency analysis","authors":"Andreas Wiedenmann , Julian Estaller , Johannes Buberger , Wolfgang Grupp , Manuel Kuder , Antje Neve , Thomas Weyh","doi":"10.1016/j.etran.2025.100464","DOIUrl":"10.1016/j.etran.2025.100464","url":null,"abstract":"<div><div>This study investigates the integration of a modular multilevel inverter-based reconfigurable battery system into an existing electric vehicle. The aim is to evaluate how such systems can replace conventional traction inverters, battery management systems, and on-board chargers. To this end, a classification of the different topology levels and possible forms of integration of power electronics, control logic, and driver electronics is performed. A Tesla Model Y’s traction battery is redesigned, retaining its structural properties and the 4680 cell format. A package analysis shows that the multilevel system occupies a volume comparable to the conventional battery pack, while the volume previously reserved for dedicated power electronics becomes available. Efficiency simulations demonstrate that the multilevel inverter can increase the overall vehicle efficiency, especially in situations with low driving speeds and high torque requirements. As a result, WLTP energy consumption is reduced from 14.9 kWh/100km to 14.5 kWh/100km. However, the battery efficiency is reduced at higher speeds due to higher cell currents. In addition, the system enables bidirectional charging at full system power, including supply to external loads or the grid, and a more integrated vehicle architecture.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100464"},"PeriodicalIF":17.0,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096467","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}

引用次数: 0

Enhancing mechanical reliability and safety performance of a battery pack system for electric vehicles: A review 提高电动汽车电池组系统的机械可靠性和安全性能：综述

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-08 DOI: 10.1016/j.etran.2025.100469

Ibna Kawsar, Honggang Li, Binghe Liu, Yongzhi Zhang, Yongjun Pan

In electric vehicles (EVs), battery packs (BPs) are susceptible to mechanical and functional failures, where various environmental factors are influenced. Although standard testing procedures contribute to improved safety and overall performance, current research primarily examines individual factors, neglecting a comprehensive assessment of battery pack (BP) design solutions. This review comprehensively analyzes safety standards, empirical research, and advances in patent design to provide a broad perspective on the safety of battery pack systems (BPS). Specifically, it examines the responses of BPs to severe environmental conditions, including vibrations, mechanical shock, and collisions. The paper presents comprehensive design solutions, providing valuable knowledge on reducing the likelihood of failure and addressing safety concerns. The review emphasizes the importance of a complete optimization strategy for BPS, explicitly focusing on analyzing mechanical reactions, particularly concerning the reliability and efficacy of safety alerts. The conclusion highlights the imperative to meet operational requirements and safety standards in the design of BP, emphasizing the importance of adopting a robust structural design approach. The study suggested adopting harmonized standards for testing in realistic scenarios. Furthermore, this study makes an innovative contribution by exploring advanced technologies, such as FEA-DNN, reinforcement learning, and various intelligent optimization algorithms, to mitigate mechanical stresses, vibrations, shock impacts, and collision-induced damage in different work environments, providing engineering guidance to enhance the safety performance of BPS.

在电动汽车（ev）中，电池组（bp）容易受到机械和功能故障的影响，其中各种环境因素都受到影响。虽然标准测试程序有助于提高安全性和整体性能，但目前的研究主要是检查单个因素，而忽略了对电池组（BP）设计解决方案的全面评估。本文综合分析了安全标准、实证研究和专利设计的进展，为电池组系统（BPS）的安全性提供了一个广阔的视角。具体来说，它检查了bp对恶劣环境条件的响应，包括振动、机械冲击和碰撞。本文提出了全面的设计解决方案，提供了减少故障可能性和解决安全问题的宝贵知识。该综述强调了BPS完整优化策略的重要性，明确侧重于分析机械反应，特别是安全警报的可靠性和有效性。结论强调了在BP设计中满足运行要求和安全标准的必要性，强调了采用稳健的结构设计方法的重要性。该研究建议在现实情况下采用统一的测试标准。此外，本研究还探索了先进的技术，如有限元深度神经网络、强化学习和各种智能优化算法，以减轻不同工作环境下的机械应力、振动、冲击冲击和碰撞损伤，为提高BPS的安全性能提供了工程指导。

{"title":"Enhancing mechanical reliability and safety performance of a battery pack system for electric vehicles: A review","authors":"Ibna Kawsar, Honggang Li, Binghe Liu, Yongzhi Zhang, Yongjun Pan","doi":"10.1016/j.etran.2025.100469","DOIUrl":"10.1016/j.etran.2025.100469","url":null,"abstract":"<div><div>In electric vehicles (EVs), battery packs (BPs) are susceptible to mechanical and functional failures, where various environmental factors are influenced. Although standard testing procedures contribute to improved safety and overall performance, current research primarily examines individual factors, neglecting a comprehensive assessment of battery pack (BP) design solutions. This review comprehensively analyzes safety standards, empirical research, and advances in patent design to provide a broad perspective on the safety of battery pack systems (BPS). Specifically, it examines the responses of BPs to severe environmental conditions, including vibrations, mechanical shock, and collisions. The paper presents comprehensive design solutions, providing valuable knowledge on reducing the likelihood of failure and addressing safety concerns. The review emphasizes the importance of a complete optimization strategy for BPS, explicitly focusing on analyzing mechanical reactions, particularly concerning the reliability and efficacy of safety alerts. The conclusion highlights the imperative to meet operational requirements and safety standards in the design of BP, emphasizing the importance of adopting a robust structural design approach. The study suggested adopting harmonized standards for testing in realistic scenarios. Furthermore, this study makes an innovative contribution by exploring advanced technologies, such as FEA-DNN, reinforcement learning, and various intelligent optimization algorithms, to mitigate mechanical stresses, vibrations, shock impacts, and collision-induced damage in different work environments, providing engineering guidance to enhance the safety performance of BPS.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100469"},"PeriodicalIF":17.0,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060521","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}

引用次数: 0

Atmosphere-regulated thermal runaway characteristics and multidimensional safety assessment of sodium-ion and lithium-ion batteries 钠离子和锂离子电池大气调节热失控特性及多维安全评价

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-06 DOI: 10.1016/j.etran.2025.100475

Zhixiang Cheng, Zhiyuan Li, Yuxuan Li, Yin Yu, Chaoshi Liu, Zhenwei Wu, Peiyu Duan, Huang Li, Wenxin Mei, Qingsong Wang

Understanding and quantifying the thermal runaway behavior of emerging battery chemistries is essential for ensuring safety in real-world applications. This study systematically investigates the thermal runaway characteristics of sodium-ion (SIB) and lithium-ion (LIB) batteries of comparable volumes under both air and inert gas environments. Experimental results show that under low-oxygen conditions, SIB and nickel–cobalt–manganese (NCM) cells exhibit substantial mitigation of thermal runaway severity, including over 35 % decrease in gas generation metrics, while lithium iron phosphate (LFP) cells remain largely unaffected. In gas composition analysis, NCM cells show significant decreases in CO₂/CO and O₂/N₂ ratios, whereas SIB and LFP display no notable compositional changes. Based on experimental data and literature, a multidimensional database of thermal runaway parameters is developed, incorporating metrics such as gas explosiveness, toxicity, and heat of combustion. Three classical multi-criteria evaluation methods—Technique for Order Preference by Similarity to Ideal Solution, Principal Component Analysis, and a median-based approach—are applied and compared. To address limitations arising from dimensional and variance scale differences among parameters, an expected contribution method is proposed to enable balanced and consistent scoring. Results demonstrate that this method enhances fairness and interpretability, particularly in scenarios with substantial scale disparities among variables arising from cross-battery systems. This work establishes a quantitative safety assessment framework that enables cross-platform comparisons and provides guidance for battery system design, risk zoning, and thermal mitigation strategies. The framework is broadly applicable to emerging battery chemistries and advances battery safety evaluation across diverse application environments.

理解和量化新兴电池化学物质的热失控行为对于确保实际应用中的安全性至关重要。本研究系统地研究了同等体积的钠离子（SIB）和锂离子（LIB）电池在空气和惰性气体环境下的热失控特性。实验结果表明，在低氧条件下，SIB和镍钴锰（NCM）电池的热失控严重程度得到了显著缓解，其中产气指标降低了35%以上，而磷酸铁锂（LFP）电池在很大程度上不受影响。在气体成分分析中，NCM细胞的CO2/CO和O2/N2比例显著降低，而SIB和LFP细胞的成分没有显著变化。基于实验数据和文献，开发了一个多维热失控参数数据库，包括气体爆炸性、毒性和燃烧热等指标。应用并比较了三种经典的多准则评价方法——理想解相似性排序偏好法、主成分分析法和基于中值法。为了解决参数之间的维度和方差尺度差异所带来的限制，提出了一种期望贡献方法，以实现平衡和一致的评分。结果表明，该方法增强了公平性和可解释性，特别是在跨电池系统产生的变量之间存在巨大规模差异的情况下。这项工作建立了一个定量的安全评估框架，可以进行跨平台比较，并为电池系统设计、风险分区和热缓解策略提供指导。该框架广泛适用于新兴的电池化学物质，并在不同的应用环境中推进电池安全评估。

{"title":"Atmosphere-regulated thermal runaway characteristics and multidimensional safety assessment of sodium-ion and lithium-ion batteries","authors":"Zhixiang Cheng, Zhiyuan Li, Yuxuan Li, Yin Yu, Chaoshi Liu, Zhenwei Wu, Peiyu Duan, Huang Li, Wenxin Mei, Qingsong Wang","doi":"10.1016/j.etran.2025.100475","DOIUrl":"10.1016/j.etran.2025.100475","url":null,"abstract":"<div><div>Understanding and quantifying the thermal runaway behavior of emerging battery chemistries is essential for ensuring safety in real-world applications. This study systematically investigates the thermal runaway characteristics of sodium-ion (SIB) and lithium-ion (LIB) batteries of comparable volumes under both air and inert gas environments. Experimental results show that under low-oxygen conditions, SIB and nickel–cobalt–manganese (NCM) cells exhibit substantial mitigation of thermal runaway severity, including over 35 % decrease in gas generation metrics, while lithium iron phosphate (LFP) cells remain largely unaffected. In gas composition analysis, NCM cells show significant decreases in CO<sub>2</sub>/CO and O<sub>2</sub>/N<sub>2</sub> ratios, whereas SIB and LFP display no notable compositional changes. Based on experimental data and literature, a multidimensional database of thermal runaway parameters is developed, incorporating metrics such as gas explosiveness, toxicity, and heat of combustion. Three classical multi-criteria evaluation methods—Technique for Order Preference by Similarity to Ideal Solution, Principal Component Analysis, and a median-based approach—are applied and compared. To address limitations arising from dimensional and variance scale differences among parameters, an expected contribution method is proposed to enable balanced and consistent scoring. Results demonstrate that this method enhances fairness and interpretability, particularly in scenarios with substantial scale disparities among variables arising from cross-battery systems. This work establishes a quantitative safety assessment framework that enables cross-platform comparisons and provides guidance for battery system design, risk zoning, and thermal mitigation strategies. The framework is broadly applicable to emerging battery chemistries and advances battery safety evaluation across diverse application environments.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100475"},"PeriodicalIF":17.0,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045875","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}

引用次数: 0

3D-printed honeycomb lithium-silicon alloy anodes for stabilized interface in sulfide all-solid-state batteries 用于硫化物全固态电池稳定界面的3d打印蜂窝锂硅合金阳极

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-05 DOI: 10.1016/j.etran.2025.100476

Lutong Wang , Ziqi Zhang , Fuqiang Xu , Jixian Luo , Chuang Yi , Hong Li , Liquan Chen , Fan Wu

Solid-state batteries have emerged as a crucial development direction for next-generation energy storage technologies, owing to their high energy density, long cycle life, and excellent safety. However, the most challenging issue of interfacial contact/degradation in solid-state batteries remains unsolved. Herein, a novel Si-C interlocking honeycomb electrode is designed/realized via 3D printing technology. Achieves 98.9 % capacity retention over 2100 cycles at 1C. The honeycomb pore walls form a mortise-tenon structure with the electrolyte to maintain good interfacial contact, while the hard carbon layer isolates the electrolyte from the lithium-silicon interface, thereby stabilizing the growth of the solid electrolyte interphase (SEI) and achieving stress-electrochemical coupling regulation. Moreover, as the honeycomb channels form an interpenetrating structure with the solid electrolyte, a three-dimensional ion transport network is established, shortening the lithium-ion diffusion path, enhancing the interfacial contact between the electrode and solid electrolyte, reducing the risk of lithium dendrite formation, and improving the rate performance of all-solid-state batteries. This approach leverages structural design to enhance material performance, for the first time enabling the compatibility of 3D-printed structured silicon-based anodes with sulfide-based all-solid-state systems, thus providing a scalable solution for next-generation high-energy-density batteries.

固态电池具有能量密度高、循环寿命长、安全性好等优点，已成为下一代储能技术的重要发展方向。然而，固态电池中最具挑战性的界面接触/降解问题仍未得到解决。本文采用3D打印技术设计/实现了一种新型硅碳联锁蜂窝电极。达到98.9%的容量保持超过2100循环在1C。蜂窝孔壁与电解质形成榫卯结构，保持良好的界面接触，而硬碳层将电解质与锂硅界面隔离，从而稳定固体电解质界面相（SEI）的生长，实现应力-电化学耦合调节。此外，由于蜂窝通道与固体电解质形成互穿结构，建立了三维离子输运网络，缩短了锂离子扩散路径，增强了电极与固体电解质的界面接触，降低了锂枝晶形成的风险，提高了全固态电池的速率性能。这种方法利用结构设计来提高材料性能，首次实现了3d打印结构硅基阳极与硫化物基全固态系统的兼容性，从而为下一代高能量密度电池提供了可扩展的解决方案。

{"title":"3D-printed honeycomb lithium-silicon alloy anodes for stabilized interface in sulfide all-solid-state batteries","authors":"Lutong Wang , Ziqi Zhang , Fuqiang Xu , Jixian Luo , Chuang Yi , Hong Li , Liquan Chen , Fan Wu","doi":"10.1016/j.etran.2025.100476","DOIUrl":"10.1016/j.etran.2025.100476","url":null,"abstract":"<div><div>Solid-state batteries have emerged as a crucial development direction for next-generation energy storage technologies, owing to their high energy density, long cycle life, and excellent safety. However, the most challenging issue of interfacial contact/degradation in solid-state batteries remains unsolved. Herein, a novel Si-C interlocking honeycomb electrode is designed/realized via 3D printing technology. Achieves 98.9 % capacity retention over 2100 cycles at 1C. The honeycomb pore walls form a mortise-tenon structure with the electrolyte to maintain good interfacial contact, while the hard carbon layer isolates the electrolyte from the lithium-silicon interface, thereby stabilizing the growth of the solid electrolyte interphase (SEI) and achieving stress-electrochemical coupling regulation. Moreover, as the honeycomb channels form an interpenetrating structure with the solid electrolyte, a three-dimensional ion transport network is established, shortening the lithium-ion diffusion path, enhancing the interfacial contact between the electrode and solid electrolyte, reducing the risk of lithium dendrite formation, and improving the rate performance of all-solid-state batteries. This approach leverages structural design to enhance material performance, for the first time enabling the compatibility of 3D-printed structured silicon-based anodes with sulfide-based all-solid-state systems, thus providing a scalable solution for next-generation high-energy-density batteries.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100476"},"PeriodicalIF":17.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004531","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}

引用次数: 0

State-of-charge estimation over full battery lifespan under diverse fast-charging protocols: A lightweight base-error joint modeling framework 多种快速充电协议下全电池寿命的充电状态估计：轻量级基础误差联合建模框架

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-05 DOI: 10.1016/j.etran.2025.100474

Ganglin Cao , Shouxuan Chen , Yuanfei Geng , Shuzhi Zhang , Yao Jia , Rong Feng , Yongjun Liu

Accurate state-of-charge (SOC) online estimation during various multi-stage constant current (MCC) fast-charging protocols over battery entire lifespan holds significant importance. In this work, we develop a lightweight-training oriented data-driven base-error joint modeling framework to fill this research gap. Through deep learning-based initial-cycle data training and lightweight machine learning-based typical-cycle data training, we only extract approximately 1 % of whole battery data for data-driven base-error joint modeling. With consideration of SOC time-dependency, short-term Ampere-hour is further combined via a simple filter structure to guarantee final SOC estimation accuracy. The validation, derived from a public battery degradation dataset comprising 8 different MCC fast-charging protocols from 46 cells, demonstrates that our framework allows rapid data-driven base-error joint modeling with training time only about l min, where both average mean absolute error and average root mean square error of SOC estimation during various MCC fast-charging protocols over battery entire lifespan are roughly below 0.3 %. Our work, for the first time, reveals the possibility of joint data-driven model trained via extremely few data on accurate SOC online estimation with consideration of various MCC fast-charging protocols and battery degradation status, and also offers a pretty concise but efficient solution for multi-scenario battery aging diagnosis and voltage dynamics forecast. The code accompanying this work is available at https://github.com/szzhang96/A-light-weighted-training-oriented-data-driven-base-error-joint-modeling-method-for-SOC-estimation.

在不同的多级恒流（MCC）快速充电协议中，准确的在线估计电池的充电状态（SOC）具有重要的意义。在这项工作中，我们开发了一个面向轻量级训练的数据驱动基础误差联合建模框架来填补这一研究空白。通过基于深度学习的初始周期数据训练和基于轻量级机器学习的典型周期数据训练，我们只提取了大约1%的整个电池数据，用于数据驱动的基础误差联合建模。考虑到SOC的时间依赖性，通过简单的滤波器结构进一步组合短期安培小时，以保证最终的SOC估计精度。来自46个电池的8种不同MCC快速充电协议的公共电池退化数据集的验证表明，我们的框架允许快速数据驱动的基本误差联合建模，训练时间仅为1分钟，其中各种MCC快速充电协议下SOC估计的平均绝对误差和平均均方根误差在电池整个使用寿命期间大致低于0.3%。我们的工作首次揭示了通过极少量数据训练的联合数据驱动模型在考虑各种MCC快速充电协议和电池退化状态的情况下进行准确的SOC在线估计的可能性，并为多场景电池老化诊断和电压动态预测提供了一种非常简洁而高效的解决方案。本文附带的代码可从https://github.com/szzhang96/A-light-weighted-training-oriented-data-driven-base-error-joint-modeling-method-for-SOC-estimation获得。

{"title":"State-of-charge estimation over full battery lifespan under diverse fast-charging protocols: A lightweight base-error joint modeling framework","authors":"Ganglin Cao , Shouxuan Chen , Yuanfei Geng , Shuzhi Zhang , Yao Jia , Rong Feng , Yongjun Liu","doi":"10.1016/j.etran.2025.100474","DOIUrl":"10.1016/j.etran.2025.100474","url":null,"abstract":"<div><div>Accurate state-of-charge (SOC) online estimation during various multi-stage constant current (MCC) fast-charging protocols over battery entire lifespan holds significant importance. In this work, we develop a lightweight-training oriented data-driven base-error joint modeling framework to fill this research gap. Through deep learning-based initial-cycle data training and lightweight machine learning-based typical-cycle data training, we only extract approximately 1 % of whole battery data for data-driven base-error joint modeling. With consideration of SOC time-dependency, short-term Ampere-hour is further combined via a simple filter structure to guarantee final SOC estimation accuracy. The validation, derived from a public battery degradation dataset comprising 8 different MCC fast-charging protocols from 46 cells, demonstrates that our framework allows rapid data-driven base-error joint modeling with training time only about l min, where both average mean absolute error and average root mean square error of SOC estimation during various MCC fast-charging protocols over battery entire lifespan are roughly below 0.3 %. Our work, for the first time, reveals the possibility of joint data-driven model trained via extremely few data on accurate SOC online estimation with consideration of various MCC fast-charging protocols and battery degradation status, and also offers a pretty concise but efficient solution for multi-scenario battery aging diagnosis and voltage dynamics forecast. The code accompanying this work is available at <span><span>https://github.com/szzhang96/A-light-weighted-training-oriented-data-driven-base-error-joint-modeling-method-for-SOC-estimation</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100474"},"PeriodicalIF":17.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010679","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}

引用次数: 0

Multi-level passive-active thermal control for battery thermal runaway prevention and suppression in electric vehicles 电动汽车电池热失控预防与抑制多级被动-主动热控制

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-04 DOI: 10.1016/j.etran.2025.100467

Jiekai Xie , Junlin Li , Canbing Li , Xinyan Huang , Guoqing Zhang , Xiaoqing Yang

Resolving the contradiction between heat-dissipation during normal operation and thermal-insulation after thermal runaway (TR) is highly desirable for battery thermal safety system but remains challenges. Herein, a multi-leveled thermal control strategy, i.e., passive cooling - active cooling - passive suppression - active suppression, has been proposed for TR prevention-suppression of the battery packs. The system is primarily designed by modular composite phase change material (CPCM), liquid cooling (LC) plates and aerogel plates (APs). Firstly, the passive cooling CPCM coordinated with active LC enables a suitable working temperature, low temperature gradient and low energy consumption of the battery pack under variable environments. Secondly, the modular design of the battery pack couples with the passive thermal-insulation effect of APs, successfully preventing TR from propagating to other modules. Thirdly, APs work synergistically with dynamic LC, greatly enhancing the directional heat-dissipation, and consequently, the TR propagation can be suppressed to the lowest level. By the flexible dynamic flow rate adjustment, the TR of large-scaled battery packs with different configurations of 4S12P, 6S8P, 8S6P and 12S4P can be successfully suppressed in the initially-triggered cell.

解决电池正常工作时的散热与热失控后的隔热之间的矛盾是电池热安全系统迫切需要解决的问题，但仍是一个挑战。本文提出了一种多层热控制策略，即被动冷却-主动冷却-被动抑制-主动抑制，以防止电池组的TR抑制。该系统主要由模块化复合相变材料（CPCM）、液体冷却（LC）板和气凝胶板（APs）设计而成。首先，被动冷却CPCM与主动LC相协调，使电池组在可变环境下具有合适的工作温度、低温度梯度和低能耗。其次，电池组的模块化设计与ap的被动隔热效应耦合，成功地阻止了TR传播到其他模块。第三，ap与动态LC协同工作，大大增强了定向散热，从而将TR传播抑制到最低水平。通过灵活的动态流量调节，4S12P、6S8P、8S6P和12S4P不同配置的大型电池组的TR可以在初始触发电池中成功抑制。

{"title":"Multi-level passive-active thermal control for battery thermal runaway prevention and suppression in electric vehicles","authors":"Jiekai Xie , Junlin Li , Canbing Li , Xinyan Huang , Guoqing Zhang , Xiaoqing Yang","doi":"10.1016/j.etran.2025.100467","DOIUrl":"10.1016/j.etran.2025.100467","url":null,"abstract":"<div><div>Resolving the contradiction between heat-dissipation during normal operation and thermal-insulation after thermal runaway (TR) is highly desirable for battery thermal safety system but remains challenges. Herein, a multi-leveled thermal control strategy, <em>i.e.</em>, passive cooling - active cooling - passive suppression - active suppression, has been proposed for TR prevention-suppression of the battery packs. The system is primarily designed by modular composite phase change material (CPCM), liquid cooling (LC) plates and aerogel plates (APs). Firstly, the passive cooling CPCM coordinated with active LC enables a suitable working temperature, low temperature gradient and low energy consumption of the battery pack under variable environments. Secondly, the modular design of the battery pack couples with the passive thermal-insulation effect of APs, successfully preventing TR from propagating to other modules. Thirdly, APs work synergistically with dynamic LC, greatly enhancing the directional heat-dissipation, and consequently, the TR propagation can be suppressed to the lowest level. By the flexible dynamic flow rate adjustment, the TR of large-scaled battery packs with different configurations of 4S12P, 6S8P, 8S6P and 12S4P can be successfully suppressed in the initially-triggered cell.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100467"},"PeriodicalIF":17.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145019420","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}

引用次数: 0

Full-scene battery self-heating method based on powertrain system for electric vehicles at extremely low temperatures 基于动力总成系统的电动汽车极低温全场景电池自热方法

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-04 DOI: 10.1016/j.etran.2025.100465

Heping Ling, Lei Yan, Hua Pan, Siliang Chen, Fang Li, Shiyun Zhang

The popularity of electric vehicles (EVs) in the cold regions is seriously hindered by the degradation of lithium-ion batteries (LIBs) at low temperatures. To settle such issue, it is necessary to preheat the LIBs to moderate temperature for normal operation. As one of attractive internal preheating methods, pulse self-heating possesses high heating rate and efficiency. However, the application of pulse self-heating still faces the challenges of the pulse current power source unavailable in EVs. Herein we proposed a novel battery self-heating method which reuses the powertrain system of EVs to generate pulse excitation onboard, eliminating additional hardware. Moreover, the decoupled control of battery self-heating and motor torque was further developed to achieve the full-scene application, including charging, parking and driving. When applied in EVs, the proposed self-heating method could realize fast temperature rising of battery pack, shortening 30.7 % charging time at −20 °C compared with the conventional heat pump method. It also achieves rapid startup of EVs even at low temperature of −38 °C with high heating rate (0.73 °C min⁻¹) and low energy consumption (4.2 % SOC), as well as maintains the dynamic performance during driving at −30 °C. The proposed method provides a promising solution to preheat the battery pack for EVs application at extremely low temperatures.

锂离子电池的低温降解严重阻碍了电动汽车在寒冷地区的普及。为了解决这个问题，有必要将lib预热到正常工作的温度。脉冲自加热具有较高的加热速率和效率，是一种很有吸引力的内部预热方法。然而，脉冲自加热的应用仍然面临着电动汽车无法获得脉冲电流电源的挑战。在此，我们提出了一种新的电池自加热方法，该方法利用电动汽车的动力总成系统产生车载脉冲激励，消除了额外的硬件。进一步开发电池自热和电机转矩的解耦控制，实现充电、停车、行驶全场景应用。应用于电动汽车时，所提出的自加热方法可以实现电池组的快速升温，与传统热泵方法相比，在−20℃的充电时间缩短了30.7%。在−38℃的低温条件下，也能实现电动汽车的快速启动，加热速率高（0.73℃min−1），能耗低（4.2 % SOC），且在−30℃下仍能保持行驶过程中的动态性能。该方法为在极低温度下对电动汽车电池组进行预热提供了一种很有前景的解决方案。

{"title":"Full-scene battery self-heating method based on powertrain system for electric vehicles at extremely low temperatures","authors":"Heping Ling, Lei Yan, Hua Pan, Siliang Chen, Fang Li, Shiyun Zhang","doi":"10.1016/j.etran.2025.100465","DOIUrl":"10.1016/j.etran.2025.100465","url":null,"abstract":"<div><div>The popularity of electric vehicles (EVs) in the cold regions is seriously hindered by the degradation of lithium-ion batteries (LIBs) at low temperatures. To settle such issue, it is necessary to preheat the LIBs to moderate temperature for normal operation. As one of attractive internal preheating methods, pulse self-heating possesses high heating rate and efficiency. However, the application of pulse self-heating still faces the challenges of the pulse current power source unavailable in EVs. Herein we proposed a novel battery self-heating method which reuses the powertrain system of EVs to generate pulse excitation onboard, eliminating additional hardware. Moreover, the decoupled control of battery self-heating and motor torque was further developed to achieve the full-scene application, including charging, parking and driving. When applied in EVs, the proposed self-heating method could realize fast temperature rising of battery pack, shortening 30.7 % charging time at −20 °C compared with the conventional heat pump method. It also achieves rapid startup of EVs even at low temperature of −38 °C with high heating rate (0.73 °C min<sup>−1</sup>) and low energy consumption (4.2 % SOC), as well as maintains the dynamic performance during driving at −30 °C. The proposed method provides a promising solution to preheat the battery pack for EVs application at extremely low temperatures.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100465"},"PeriodicalIF":17.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096466","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}

引用次数: 0

Physics-enhanced U-net and deep reinforcement learning for automated optimization of pin-fin heat sinks in electric vehicle power modules 基于物理增强U-net和深度强化学习的电动汽车电源模块插片散热器自动优化

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-03 DOI: 10.1016/j.etran.2025.100463

Yubo Lian, Heping Ling, Gan Song, Jiapei Yang, Hanzhi Wang, Zhe Zhang, Shaokuan Mao, Bin He

The use of pin-fin structures in compact energy devices, such as electric vehicle power modules, is a widely adopted thermal management strategy to enhance heat transfer efficiency. In this study, we present an innovative deep learning framework that integrates a physics-enhanced U-net architecture with a deep reinforcement learning agent to achieve autonomous optimal design of pin-fin arrays. The physics-enhanced U-net is trained to predict thermal-flow fields, while the integrated deep reinforcement learning agent autonomously optimizes pin-fin configurations to minimize both pressure drop and junction temperature. First, we generate a high-fidelity training dataset through an automated computational pipeline that integrates COMSOL Multiphysics for thermal-flow field simulations with a custom Matlab script for parametric generation of 1080 training samples. Subsequently, we train our physics-enhanced U-net architecture to predict the velocity, pressure and temperature fields from various pin-fin structure inputs. The proposed model demonstrates both high prediction accuracy and robustness, achieving mean-squared-errors on the order of 10⁻⁴ for all output fields. As a result, the trained U-net model achieves exceptional prediction accuracy, demonstrating 93.9 % precision for pressure drop and 99.5 % for junction temperature. Finally, we integrate the deep reinforcement learning agent with the trained U-net model to establish an automated optimization framework for pin-fin design, enabling intelligent exploration of design space. The proposed deep learning framework successfully automates the optimization of pin-fin heat sinks for a high power density module. The model demonstrates exceptional capability in generating optimal designs, with the optimized configuration achieving an 8.8 K reduction in junction temperature and 11.3 % decrease in pressure drop comparing to a baseline design. These improvements can be translated into approximately 10 % augmentation in power output, which validates both the effectiveness and robustness of our deep learning driven design approach.

在电动汽车电源模块等紧凑型能源器件中，采用针翅结构是一种广泛采用的热管理策略，以提高传热效率。在本研究中，我们提出了一个创新的深度学习框架，该框架将物理增强的U-net架构与深度强化学习代理集成在一起，以实现引脚鳍阵列的自主优化设计。物理增强的U-net经过训练，可以预测热流场，而集成的深度强化学习代理可以自主优化引脚鳍配置，以最小化压降和结温。首先，我们通过自动化计算管道生成高保真度的训练数据集，该管道集成了COMSOL Multiphysics用于热流场模拟，以及用于参数化生成1080个训练样本的自定义Matlab脚本。随后，我们训练了物理增强的U-net架构，以预测来自不同鳍片结构输入的速度、压力和温度场。该模型具有较高的预测精度和鲁棒性，所有输出字段的均方误差均在10−4量级。结果，训练后的U-net模型达到了优异的预测精度，对压降的预测精度为93.9%，对结温的预测精度为99.5%。最后，我们将深度强化学习智能体与训练好的U-net模型相结合，建立了鳍片设计的自动化优化框架，实现了设计空间的智能探索。提出的深度学习框架成功地自动优化了高功率密度模块的鳍片散热器。该模型在生成优化设计方面表现出卓越的能力，与基线设计相比，优化后的配置实现了结温降低8.8 K，压降降低11.3%。这些改进可以转化为大约10%的功率输出增加，这验证了我们深度学习驱动设计方法的有效性和鲁棒性。

{"title":"Physics-enhanced U-net and deep reinforcement learning for automated optimization of pin-fin heat sinks in electric vehicle power modules","authors":"Yubo Lian, Heping Ling, Gan Song, Jiapei Yang, Hanzhi Wang, Zhe Zhang, Shaokuan Mao, Bin He","doi":"10.1016/j.etran.2025.100463","DOIUrl":"10.1016/j.etran.2025.100463","url":null,"abstract":"<div><div>The use of pin-fin structures in compact energy devices, such as electric vehicle power modules, is a widely adopted thermal management strategy to enhance heat transfer efficiency. In this study, we present an innovative deep learning framework that integrates a physics-enhanced U-net architecture with a deep reinforcement learning agent to achieve autonomous optimal design of pin-fin arrays. The physics-enhanced U-net is trained to predict thermal-flow fields, while the integrated deep reinforcement learning agent autonomously optimizes pin-fin configurations to minimize both pressure drop and junction temperature. First, we generate a high-fidelity training dataset through an automated computational pipeline that integrates COMSOL Multiphysics for thermal-flow field simulations with a custom Matlab script for parametric generation of 1080 training samples. Subsequently, we train our physics-enhanced U-net architecture to predict the velocity, pressure and temperature fields from various pin-fin structure inputs. The proposed model demonstrates both high prediction accuracy and robustness, achieving mean-squared-errors on the order of 10<sup>−4</sup> for all output fields. As a result, the trained U-net model achieves exceptional prediction accuracy, demonstrating 93.9 % precision for pressure drop and 99.5 % for junction temperature. Finally, we integrate the deep reinforcement learning agent with the trained U-net model to establish an automated optimization framework for pin-fin design, enabling intelligent exploration of design space. The proposed deep learning framework successfully automates the optimization of pin-fin heat sinks for a high power density module. The model demonstrates exceptional capability in generating optimal designs, with the optimized configuration achieving an 8.8 K reduction in junction temperature and 11.3 % decrease in pressure drop comparing to a baseline design. These improvements can be translated into approximately 10 % augmentation in power output, which validates both the effectiveness and robustness of our deep learning driven design approach.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100463"},"PeriodicalIF":17.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027081","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}

引用次数: 0

Cross-Domain Feature-Based Battery State-of-Health Estimation from Rest Period for Real-World Electric Vehicles 基于跨域特征的电动汽车静息期电池健康状态估计

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-09-02 DOI: 10.1016/j.etran.2025.100471

Siyi Tao , Jiangong Zhu , Yuan Li , Bo Jiang , Wei Chang , Haifeng Dai , Xuezhe Wei

Accurate power battery state-of-health (SOH) estimation is essential for ensuring the stable and reliable operation of electric vehicles (EVs). However, the diversity of charging methods and battery materials (nickel-cobalt-manganese (NCM) and lithium iron phosphate (LFP)) poses challenges for generalizing SOH estimation on field data. In this study, we propose a general cross-domain feature extraction method that integrates time-domain (TD) and frequency-domain (FD) features, along with inter-cell inconsistency features, from a two-minute post-charging rest period. Leveraging datasets from 106 real EVs encompassing 17,729 charging cycles and 28 laboratory cells with 10,912 charging cycles, we employ lightweight tree-based models for reliable and rapid SOH estimation. For EVs equipped with five different capacities of NCM and LFP batteries under various charging conditions, a single unified model is employed across all cases, yielding a mean absolute percentage error (MAPE) of less than 1.94% and a maximum error (MAXE) below 6.28%. This study highlights the potential of features from post-charging rest period to enable high-accuracy SOH estimation in real-world conditions, contributing to reduced costs and improved efficiency for future TWh-scale power battery market.

准确的动力电池健康状态（SOH）估计是保证电动汽车稳定可靠运行的关键。然而，充电方法和电池材料（镍钴锰（NCM）和磷酸铁锂（LFP））的多样性给现场数据的SOH估计带来了挑战。在这项研究中，我们提出了一种通用的跨域特征提取方法，该方法结合了充电后两分钟休息时间的时域（TD）和频域（FD）特征以及细胞间不一致特征。利用106辆真实电动汽车的17,729个充电周期和28个实验室电池的10,912个充电周期的数据集，我们采用轻量级的基于树的模型进行可靠和快速的SOH估计。对于搭载5种不同容量NCM和LFP电池的电动汽车，在不同充电条件下均采用统一模型，平均绝对百分比误差（MAPE）小于1.94%，最大误差（MAXE）小于6.28%。这项研究强调了充电后休息期的特征在现实条件下实现高精度SOH估计的潜力，有助于降低成本，提高未来太瓦时规模的动力电池市场的效率。

{"title":"Cross-Domain Feature-Based Battery State-of-Health Estimation from Rest Period for Real-World Electric Vehicles","authors":"Siyi Tao , Jiangong Zhu , Yuan Li , Bo Jiang , Wei Chang , Haifeng Dai , Xuezhe Wei","doi":"10.1016/j.etran.2025.100471","DOIUrl":"10.1016/j.etran.2025.100471","url":null,"abstract":"<div><div>Accurate power battery state-of-health (SOH) estimation is essential for ensuring the stable and reliable operation of electric vehicles (EVs). However, the diversity of charging methods and battery materials (nickel-cobalt-manganese (NCM) and lithium iron phosphate (LFP)) poses challenges for generalizing SOH estimation on field data. In this study, we propose a general cross-domain feature extraction method that integrates time-domain (TD) and frequency-domain (FD) features, along with inter-cell inconsistency features, from a two-minute post-charging rest period. Leveraging datasets from 106 real EVs encompassing 17,729 charging cycles and 28 laboratory cells with 10,912 charging cycles, we employ lightweight tree-based models for reliable and rapid SOH estimation. For EVs equipped with five different capacities of NCM and LFP batteries under various charging conditions, a single unified model is employed across all cases, yielding a mean absolute percentage error (MAPE) of less than 1.94% and a maximum error (MAXE) below 6.28%. This study highlights the potential of features from post-charging rest period to enable high-accuracy SOH estimation in real-world conditions, contributing to reduced costs and improved efficiency for future TWh-scale power battery market.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100471"},"PeriodicalIF":17.0,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004530","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}

引用次数: 0