Etransportation最新文献_第10页

Challenges and numerical solutions for multi-domain and multi-physics coupling in heterogeneous lithium-ion battery model simulation 非均质锂离子电池模型仿真中多域多物理场耦合的挑战及数值解决方案

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-07-31 DOI: 10.1016/j.etran.2025.100452

Qiyu Chen , Lance Zhao , Xinhong (Susan) Chen , Zhe Li

In electrochemistry, the heterogeneous model effectively characterizes the microstructural features of porous electrodes by distinctly resolving both solid and liquid phases with respective spatial distributions and interfacial interfaces. The model incorporates essential characteristics including particle size distributions and non-uniform porosity, enabling spatiotemporal representation of coupled physicochemical processes. However, modeling and numerically solving the heterogeneous model presents significant challenges. This study introduces computational solutions to critical challenges in heterogeneous lithium-ion battery simulation. (1) Distinct material phases occupy spatially resolved domains, with various phenomena occurring either bulk phases or interfaces. We develop domain decomposition/combination strategy with morphology-specific approaches. (2) Regions with similar compositions may exhibit significant variations in physical properties. Our novel transfer coefficient matrix method enables global solutions for concentration equations across interfaces with varying porosity. (3) Batteries represent inherently mass-charge coupled systems, where lithium-ion transport is driven by both electric potential and concentration gradients. The composite potential field method rigorously ensures flux continuity while resolving coupled transport mechanisms. We implement above methods to our self-developed simulation framework, rigorously validating accuracy against experimental measurements and COMSOL benchmarks. This work provides a fundamental theoretical foundation for both the development of next-generation ultra-high-performance batteries and the technological upgrade of industrial battery simulation software.

在电化学中，非均相模型通过清晰地分辨具有各自空间分布和界面的固相和液相，有效地表征了多孔电极的微观结构特征。该模型结合了粒径分布和非均匀孔隙率等基本特征，实现了耦合物理化学过程的时空表征。然而，异构模型的建模和数值求解提出了重大挑战。本研究为非均质锂离子电池模拟中的关键挑战引入了计算解决方案。(1)不同的材料相占据空间分辨域，不同的现象出现在体相或界面上。我们使用特定于形态的方法开发了域分解/组合策略。(2)成分相似的地区，其物理性质可能存在显著差异。我们的新传递系数矩阵方法可以实现跨不同孔隙度界面的浓度方程的全局解。(3)电池本质上是质量-电荷耦合系统，锂离子的输运是由电位和浓度梯度驱动的。复合势场法在求解耦合输运机制的同时严格保证了通量的连续性。我们将上述方法应用到我们自己开发的仿真框架中，严格验证了实验测量和COMSOL基准的准确性。本工作为下一代超高性能电池的开发和工业电池仿真软件的技术升级提供了基础理论基础。

{"title":"Challenges and numerical solutions for multi-domain and multi-physics coupling in heterogeneous lithium-ion battery model simulation","authors":"Qiyu Chen , Lance Zhao , Xinhong (Susan) Chen , Zhe Li","doi":"10.1016/j.etran.2025.100452","DOIUrl":"10.1016/j.etran.2025.100452","url":null,"abstract":"<div><div>In electrochemistry, the heterogeneous model effectively characterizes the microstructural features of porous electrodes by distinctly resolving both solid and liquid phases with respective spatial distributions and interfacial interfaces. The model incorporates essential characteristics including particle size distributions and non-uniform porosity, enabling spatiotemporal representation of coupled physicochemical processes. However, modeling and numerically solving the heterogeneous model presents significant challenges. This study introduces computational solutions to critical challenges in heterogeneous lithium-ion battery simulation. (1) Distinct material phases occupy spatially resolved domains, with various phenomena occurring either bulk phases or interfaces. We develop domain decomposition/combination strategy with morphology-specific approaches. (2) Regions with similar compositions may exhibit significant variations in physical properties. Our novel transfer coefficient matrix method enables global solutions for concentration equations across interfaces with varying porosity. (3) Batteries represent inherently mass-charge coupled systems, where lithium-ion transport is driven by both electric potential and concentration gradients. The composite potential field method rigorously ensures flux continuity while resolving coupled transport mechanisms. We implement above methods to our self-developed simulation framework, rigorously validating accuracy against experimental measurements and COMSOL benchmarks. This work provides a fundamental theoretical foundation for both the development of next-generation ultra-high-performance batteries and the technological upgrade of industrial battery simulation software.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100452"},"PeriodicalIF":17.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757613","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

Cloud-based SOC optimization for predictive energy management and zero emission zone compliance in PHEVs 基于云的SOC优化，用于插电式混合动力汽车的预测性能源管理和零排放区合规

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-07-12 DOI: 10.1016/j.etran.2025.100443

Paul Muthyala , Florian Wessel , Joschka Schaub , Stefan Pischinger

With deteriorating air quality in many cities worldwide failing to meet World Health Organization (WHO) standards, effective countermeasures are urgently needed. In response, cities are implementing zero-emission zones, restricting entry to only zero-emission vehicles like Battery Electric Vehicles and Fuel Cell Electric Vehicles. These measures aim to reduce urban air pollution and improve public health significantly. Despite their ability to operate in pure electric mode under city driving conditions, Plug-in Hybrid Electric Vehicles (PHEVs) are typically prohibited from zero-emission zones due to the potential use of their Internal Combustion Engines, which could compromise air quality improvement efforts. However, advancements in digital maps and Vehicle-to-Everything (V2X) technology present a viable solution to this challenge. Geofencing technology can now be employed to carefully plan and prepare PHEVs’ battery State of Charge (SOC), ensuring that SOC usage is strictly restricted within zero-emission zones.

This study proposes a predictive control strategy for PHEVs, utilizing route information from digital map providers to enable electric driving within zero-emission zones. To achieve this, a supervisory control with Dynamic Programming (DP) is developed in the upper layer to calculate an optimal SOC trajectory considering the zero-emission zone and guide the rule-based controller in the lower level. The high computational effort of DP is addressed by running it in the cloud. In addition, the optimization can be repeated multiple times during driving. The proposed methodology is tested and validated on a demonstrator vehicle in a real-world drive cycle.

随着世界上许多城市的空气质量不断恶化，未能达到世界卫生组织（世卫组织）的标准，迫切需要采取有效的对策。作为回应，各城市正在实施零排放区，限制纯电动汽车和燃料电池电动汽车等零排放车辆进入。这些措施旨在减少城市空气污染，显著改善公众健康。尽管插电式混合动力汽车（phev）能够在城市驾驶条件下以纯电动模式运行，但由于其内燃机的潜在使用可能会影响空气质量改善，因此通常禁止进入零排放区。然而，数字地图和车联网（V2X）技术的进步为这一挑战提供了可行的解决方案。地理围栏技术现在可以用于精心规划和准备插电式混合动力车的电池充电状态（SOC），确保SOC的使用严格限制在零排放区域内。本研究提出了一种插电式混合动力车的预测控制策略，利用数字地图提供商提供的路线信息，在零排放区域内实现电动驾驶。为了实现这一目标，在上层开发了一种基于动态规划（DP）的监督控制，以计算考虑零排放区域的最优SOC轨迹，并指导下层基于规则的控制器。DP的高计算量是通过在云中运行来解决的。此外，该优化可以在驾驶过程中多次重复。所提出的方法在一辆真实驾驶循环的演示车上进行了测试和验证。

{"title":"Cloud-based SOC optimization for predictive energy management and zero emission zone compliance in PHEVs","authors":"Paul Muthyala , Florian Wessel , Joschka Schaub , Stefan Pischinger","doi":"10.1016/j.etran.2025.100443","DOIUrl":"10.1016/j.etran.2025.100443","url":null,"abstract":"<div><div>With deteriorating air quality in many cities worldwide failing to meet World Health Organization (WHO) standards, effective countermeasures are urgently needed. In response, cities are implementing zero-emission zones, restricting entry to only zero-emission vehicles like Battery Electric Vehicles and Fuel Cell Electric Vehicles. These measures aim to reduce urban air pollution and improve public health significantly. Despite their ability to operate in pure electric mode under city driving conditions, Plug-in Hybrid Electric Vehicles (PHEVs) are typically prohibited from zero-emission zones due to the potential use of their Internal Combustion Engines, which could compromise air quality improvement efforts. However, advancements in digital maps and Vehicle-to-Everything (V2X) technology present a viable solution to this challenge. Geofencing technology can now be employed to carefully plan and prepare PHEVs’ battery State of Charge (SOC), ensuring that SOC usage is strictly restricted within zero-emission zones.</div><div>This study proposes a predictive control strategy for PHEVs, utilizing route information from digital map providers to enable electric driving within zero-emission zones. To achieve this, a supervisory control with Dynamic Programming (DP) is developed in the upper layer to calculate an optimal SOC trajectory considering the zero-emission zone and guide the rule-based controller in the lower level. The high computational effort of DP is addressed by running it in the cloud. In addition, the optimization can be repeated multiple times during driving. The proposed methodology is tested and validated on a demonstrator vehicle in a real-world drive cycle.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100443"},"PeriodicalIF":17.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721335","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

Mechanism of battery expansion failure due to excess solid electrolyte interphase growth in lithium-ion batteries 锂离子电池中过量固体电解质界面生长导致电池膨胀失效的机理

IF 15 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-07-09 DOI: 10.1016/j.etran.2025.100450

Dongdong Qiao , Xuezhe Wei , Jiangong Zhu , Guangxu Zhang , Shuai Yang , Xueyuan Wang , Bo Jiang , Xin Lai , Yuejiu Zheng , Haifeng Dai

Revealing the aging and failure mechanisms of lithium-ion batteries is crucial for extending battery life and improving battery safety. This paper presents a mechanism of solid electrolyte interphase (SEI) film overgrowth and battery failure caused by deep aging of cylindrical batteries. Firstly, multiple 18650-type cylindrical battery accelerated aging experiments were designed. Differential voltage analysis (dV/dQ) and electrochemical impedance spectroscopy (EIS) are used to investigate battery degradation mechanisms non-destructively. Secondly, batteries under different degradation degrees were disassembled, and the scanning electron microscope (SEM), liquid nitrogen cooled argon-ion cross-sectional polishing, and X-ray photoelectron spectroscopy (XPS) technology were used to investigate the surface and cross-sectional SEI evolution of electrodes. Obvious increases in SEI thickness and resistance occur when the battery capacity fade is less than 30 %. Finally, the mechanism of excessive growth of SEI on the graphite negative electrode surface of the cylindrical battery, leading to the expansion and rupture failure of the metal shell, was revealed. This work provides crucial insights for the safe service, management, and residual value assessment of lithium-ion batteries throughout their entire lifecycle.

揭示锂离子电池老化失效机理对延长电池寿命、提高电池安全性具有重要意义。本文研究了圆柱电池深度老化导致固体电解质界面膜过度生长和电池失效的机理。首先，设计了多个18650型圆柱电池加速老化实验。差分电压分析（dV/dQ）和电化学阻抗谱（EIS）用于非破坏性地研究电池退化机制。其次，对不同降解程度的电池进行拆解，利用扫描电镜（SEM）、液氮冷却氩离子截面抛光和x射线光电子能谱（XPS）技术研究电极表面和截面SEI演化。当电池容量衰减小于30%时，SEI厚度和电阻明显增加。最后，揭示了圆柱电池石墨负极表面SEI过度生长导致金属壳膨胀破裂失效的机理。这项工作为锂离子电池整个生命周期的安全服务、管理和剩余价值评估提供了至关重要的见解。

{"title":"Mechanism of battery expansion failure due to excess solid electrolyte interphase growth in lithium-ion batteries","authors":"Dongdong Qiao , Xuezhe Wei , Jiangong Zhu , Guangxu Zhang , Shuai Yang , Xueyuan Wang , Bo Jiang , Xin Lai , Yuejiu Zheng , Haifeng Dai","doi":"10.1016/j.etran.2025.100450","DOIUrl":"10.1016/j.etran.2025.100450","url":null,"abstract":"<div><div>Revealing the aging and failure mechanisms of lithium-ion batteries is crucial for extending battery life and improving battery safety. This paper presents a mechanism of solid electrolyte interphase (SEI) film overgrowth and battery failure caused by deep aging of cylindrical batteries. Firstly, multiple 18650-type cylindrical battery accelerated aging experiments were designed. Differential voltage analysis (dV/dQ) and electrochemical impedance spectroscopy (EIS) are used to investigate battery degradation mechanisms non-destructively. Secondly, batteries under different degradation degrees were disassembled, and the scanning electron microscope (SEM), liquid nitrogen cooled argon-ion cross-sectional polishing, and X-ray photoelectron spectroscopy (XPS) technology were used to investigate the surface and cross-sectional SEI evolution of electrodes. Obvious increases in SEI thickness and resistance occur when the battery capacity fade is less than 30 %. Finally, the mechanism of excessive growth of SEI on the graphite negative electrode surface of the cylindrical battery, leading to the expansion and rupture failure of the metal shell, was revealed. This work provides crucial insights for the safe service, management, and residual value assessment of lithium-ion batteries throughout their entire lifecycle.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100450"},"PeriodicalIF":15.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623667","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

Challenges and perspectives towards multi-physics modeling for porous electrode of ultrahigh performance durable polymer electrolyte membrane fuel cells 高性能耐用聚合物电解质膜燃料电池多孔电极多物理场建模的挑战与展望

IF 15 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-07-09 DOI: 10.1016/j.etran.2025.100449

Ning Wang , Tao Lai , Wenkai Wang , Zhiguo Qu , Xuhui Wen , Guangyou Xie , Wenquan Tao

The development of ultrahigh-performance, durable polymer electrolyte membrane fuel cells (PEMFCs) is crucial for achieving large-scale commercialization. A comprehensive insight into multi-physics phenomena within advanced porous electrode designs provide motivation for the ambitious targets. Modeling is an indispensable tool in multi-physics transfer understanding and offers a promising pathway for electrode structural designs and material architecture selections. Despite the progress, the modeling community continues to face significant challenges, including oversimplification, difficulties in coupling complex features, unclear physical knowledge, and unavoidable discrepancies. This perspective highlights the current status of porous electrode modeling, identifies ongoing challenges, and explores future directions for key technologies and potential countermeasures. Specifically, the characteristics and limitations of macro-scale, meso-scale, and micro-scale models regarding intricate porous electrode microstructures are compared, including ordered structure, mesoporous carbon support, various catalyst architectures, etc. Potential solutions to these challenges are proposed for the next generation of porous electrode designs. Furthermore, three alternatives to advancing cross-scale, full-morphology, and full-coupling modeling are developed and discussed, including layer-by-layer physical property transfer, interfacial data transfer and direct numerical simulation, and data-driven assisted cross-scale modeling, which are expected to be evaluated and validated in the foreseeable future.

开发高性能、耐用的聚合物电解质膜燃料电池（PEMFCs）是实现大规模商业化的关键。对先进多孔电极设计中的多物理现象的全面洞察为雄心勃勃的目标提供了动力。建模是理解多物理场转移不可缺少的工具，为电极结构设计和材料结构选择提供了一条有前途的途径。尽管取得了进展，建模界仍然面临着重大的挑战，包括过度简化、耦合复杂特征的困难、不明确的物理知识以及不可避免的差异。这一观点强调了多孔电极建模的现状，确定了存在的挑战，并探讨了关键技术和潜在对策的未来方向。具体来说，比较了复杂多孔电极微观结构的宏观尺度、中尺度和微观尺度模型的特点和局限性，包括有序结构、中孔碳载体、各种催化剂结构等。针对这些挑战提出了下一代多孔电极设计的潜在解决方案。此外，提出并讨论了推进跨尺度、全形态和全耦合建模的三种替代方法，包括逐层物理性质传递、界面数据传递和直接数值模拟以及数据驱动辅助跨尺度建模，这些方法有望在可预见的未来得到评估和验证。

{"title":"Challenges and perspectives towards multi-physics modeling for porous electrode of ultrahigh performance durable polymer electrolyte membrane fuel cells","authors":"Ning Wang , Tao Lai , Wenkai Wang , Zhiguo Qu , Xuhui Wen , Guangyou Xie , Wenquan Tao","doi":"10.1016/j.etran.2025.100449","DOIUrl":"10.1016/j.etran.2025.100449","url":null,"abstract":"<div><div>The development of ultrahigh-performance, durable polymer electrolyte membrane fuel cells (PEMFCs) is crucial for achieving large-scale commercialization. A comprehensive insight into multi-physics phenomena within advanced porous electrode designs provide motivation for the ambitious targets. Modeling is an indispensable tool in multi-physics transfer understanding and offers a promising pathway for electrode structural designs and material architecture selections. Despite the progress, the modeling community continues to face significant challenges, including oversimplification, difficulties in coupling complex features, unclear physical knowledge, and unavoidable discrepancies. This perspective highlights the current status of porous electrode modeling, identifies ongoing challenges, and explores future directions for key technologies and potential countermeasures. Specifically, the characteristics and limitations of macro-scale, meso-scale, and micro-scale models regarding intricate porous electrode microstructures are compared, including ordered structure, mesoporous carbon support, various catalyst architectures, etc. Potential solutions to these challenges are proposed for the next generation of porous electrode designs. Furthermore, three alternatives to advancing cross-scale, full-morphology, and full-coupling modeling are developed and discussed, including layer-by-layer physical property transfer, interfacial data transfer and direct numerical simulation, and data-driven assisted cross-scale modeling, which are expected to be evaluated and validated in the foreseeable future.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100449"},"PeriodicalIF":15.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633204","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 safety of electric aircraft Batteries: Degradation and thermal runaway behavior at extreme altitudes 提高电动飞机电池的安全性：在极端高度的退化和热失控行为

IF 15 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-07-08 DOI: 10.1016/j.etran.2025.100448

Wenjie Jiang , Canbing Li , Xinxi Li , Yuhang Wu , Yunjun Luo , Dequan Zhou , Zhaowei Lin , Kang Xiong , Jianzhe Liu

The operating performance and thermal safety of lithium-ion batteries (LIBs) in high-altitude scenarios are prime concerns for their reliable applications in various fields. High-altitude environments, characterized by low ambient pressure and temperature, can accelerate LIB degradation and increase the risk of thermal runaway (TR). Unlike previous studies focusing solely on ambient pressure or ambient temperature, this work quantifies their high-altitude coupled effects on the battery performance as well as the TR characteristics. Herein, experiments and simulations are combined to analyze the hybrid pulse charge/discharge behavior, direct current internal resistance (DCIR), over-discharge and recharge/re-discharge performance, and TR characteristics of 26650 NiCoMn LIBs under various ambient pressure and temperature conditions. The results show that low ambient pressure at 20 kPa increases the DCIR of LIB by 7.16 mΩ, raises battery temperature by 4.3 °C, lowers energy efficiency to 92.2 %, and advances TR occurrence with mass loss increasing to 7.5 g. Low ambient temperature at 50 °C causes abrupt changes in battery voltage (up to 6.8009 V during pulse charge and down to 2.0641 V during pulse discharge) and increases the DCIR to 284.8 mΩ. When low ambient pressure and low ambient temperature are combined, energy efficiency decreases to 93.5 % and the peak TR temperature of LIB reduces to 214.2 °C at 20 kPa & −50 °C. The research elucidates the relationship between performance/TR behaviors of LIB and individual/coupled environmental factors, shedding new insights into the operation and safety of LIB in the aviation sector. This facilitates to establishing tailored LIB designs and adaptive thermal management strategies to mitigate failure risks in high-altitude applications.

锂离子电池在高海拔环境下的工作性能和热安全性是其在各个领域可靠应用的首要问题。低气压、低温度的高海拔环境会加速锂电池降解，增加热失控（TR）的风险。与以往的研究只关注环境压力或环境温度不同，这项工作量化了它们对电池性能和TR特性的高海拔耦合效应。本文将实验与仿真相结合，分析了26650 NiCoMn lib在不同环境压力和温度条件下的混合脉冲充放电行为、直流内阻（DCIR）、过放电和充放电/再放电性能以及TR特性。结果表明，在20 kPa的低环境压力下，锂离子电池的DCIR提高了7.16 mΩ，电池温度提高了4.3℃，能量效率降低到92.2%，并促进了TR的发生，质量损失增加到7.5 g。在50℃的低环境温度下，电池电压突变（脉冲充电时高达6.8009 V，脉冲放电时下降到2.0641 V）， DCIR增加到284.8 mΩ。低环境压力和低环境温度相结合时，电能效率降至93.5%，在20 kPa &时，锂离子电池的峰值TR温度降至214.2℃；−50°C。本研究阐明了LIB性能/TR行为与个体/耦合环境因素之间的关系，为航空领域LIB的运行和安全提供了新的见解。这有助于建立量身定制的LIB设计和自适应热管理策略，以降低高海拔应用中的故障风险。

{"title":"Enhancing safety of electric aircraft Batteries: Degradation and thermal runaway behavior at extreme altitudes","authors":"Wenjie Jiang , Canbing Li , Xinxi Li , Yuhang Wu , Yunjun Luo , Dequan Zhou , Zhaowei Lin , Kang Xiong , Jianzhe Liu","doi":"10.1016/j.etran.2025.100448","DOIUrl":"10.1016/j.etran.2025.100448","url":null,"abstract":"<div><div>The operating performance and thermal safety of lithium-ion batteries (LIBs) in high-altitude scenarios are prime concerns for their reliable applications in various fields. High-altitude environments, characterized by low ambient pressure and temperature, can accelerate LIB degradation and increase the risk of thermal runaway (TR). Unlike previous studies focusing solely on ambient pressure or ambient temperature, this work quantifies their high-altitude coupled effects on the battery performance as well as the TR characteristics. Herein, experiments and simulations are combined to analyze the hybrid pulse charge/discharge behavior, direct current internal resistance (DCIR), over-discharge and recharge/re-discharge performance, and TR characteristics of 26650 NiCoMn LIBs under various ambient pressure and temperature conditions. The results show that low ambient pressure at 20 kPa increases the DCIR of LIB by 7.16 mΩ, raises battery temperature by 4.3 °C, lowers energy efficiency to 92.2 %, and advances TR occurrence with mass loss increasing to 7.5 g. Low ambient temperature at 50 °C causes abrupt changes in battery voltage (up to 6.8009 V during pulse charge and down to 2.0641 V during pulse discharge) and increases the DCIR to 284.8 mΩ. When low ambient pressure and low ambient temperature are combined, energy efficiency decreases to 93.5 % and the peak TR temperature of LIB reduces to 214.2 °C at 20 kPa & −50 °C. The research elucidates the relationship between performance/TR behaviors of LIB and individual/coupled environmental factors, shedding new insights into the operation and safety of LIB in the aviation sector. This facilitates to establishing tailored LIB designs and adaptive thermal management strategies to mitigate failure risks in high-altitude applications.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100448"},"PeriodicalIF":15.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604499","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

Mechanical information enhanced battery state-of-health estimation 机械信息增强了电池健康状态的估计

IF 15 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-07-04 DOI: 10.1016/j.etran.2025.100440

Xubo Gu , Xinyuan Wang , Yao Ren , Wenqing Zhou , Xun Huan , Jason Siegel , Weiran Jiang , Ziyou Song

Accurate estimation of the state of health (SOH) is crucial for the safe operation of batteries. Mechanical features, in particular, offer significant potential for improving SOH estimation by directly reflecting key internal processes within batteries. However, research on the contribution of mechanical features to SOH estimation remains limited. This study demonstrates the effectiveness of mechanical features for SOH estimation in pouch cells under various operating conditions and scenarios. The results show that mechanical features provide reliable SOH estimates across different temperatures, C-rates, and charging profiles, and they are especially robust under real-world driving conditions. The mechanical features typically achieve at least a 28.26% reduction in prediction error. Notably, in the driving scenario, the mean absolute percentage error reaches an impressive low of 0.65%. Furthermore, this work introduces an evaluation framework to systematically benchmark features derived from electrical, thermal, and mechanical signals based on their overall predictive capabilities. Finally, detailed physical interpretations are provided to explain the effectiveness of mechanical features.

准确估计电池的健康状态（SOH）对电池的安全运行至关重要。特别是机械特性，通过直接反映电池内部的关键过程，为改善SOH估计提供了巨大的潜力。然而，力学特征对SOH估计的贡献研究仍然有限。本研究证明了在各种操作条件和场景下，袋状电池中SOH估计的机械特征的有效性。结果表明，机械特性可以在不同温度、c -速率和充电模式下提供可靠的SOH估计，并且在实际驾驶条件下尤其可靠。机械特征通常可以使预测误差降低至少28.26%。值得注意的是，在驾驶场景中，平均绝对百分比误差达到了令人印象深刻的0.65%的低点。此外，本研究还引入了一个评估框架，基于电、热和机械信号的整体预测能力，系统地对其特征进行基准测试。最后，提供了详细的物理解释来解释力学特征的有效性。

{"title":"Mechanical information enhanced battery state-of-health estimation","authors":"Xubo Gu , Xinyuan Wang , Yao Ren , Wenqing Zhou , Xun Huan , Jason Siegel , Weiran Jiang , Ziyou Song","doi":"10.1016/j.etran.2025.100440","DOIUrl":"10.1016/j.etran.2025.100440","url":null,"abstract":"<div><div>Accurate estimation of the state of health (SOH) is crucial for the safe operation of batteries. Mechanical features, in particular, offer significant potential for improving SOH estimation by directly reflecting key internal processes within batteries. However, research on the contribution of mechanical features to SOH estimation remains limited. This study demonstrates the effectiveness of mechanical features for SOH estimation in pouch cells under various operating conditions and scenarios. The results show that mechanical features provide reliable SOH estimates across different temperatures, C-rates, and charging profiles, and they are especially robust under real-world driving conditions. The mechanical features typically achieve at least a 28.26% reduction in prediction error. Notably, in the driving scenario, the mean absolute percentage error reaches an impressive low of 0.65%. Furthermore, this work introduces an evaluation framework to systematically benchmark features derived from electrical, thermal, and mechanical signals based on their overall predictive capabilities. Finally, detailed physical interpretations are provided to explain the effectiveness of mechanical features.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100440"},"PeriodicalIF":15.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572421","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

Battery temperature anomaly early warning for electric vehicles under real driving conditions using a temporal convolutional network 基于时间卷积网络的电动汽车实际行驶工况下电池温度异常预警

IF 15 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-07-03 DOI: 10.1016/j.etran.2025.100445

Shaopeng Li , Hui Zhang , Daniela Anna Misul , Federico Miretti , Matteo Acquarone , Naikan Ding , Dingan Ni , Ninghao Hou , Yanjie He , Yijun Zhang , Yifan Sun

For preventing thermal runaway accidents in electric vehicles (EVs), it is crucial to conduct early warning for temperature anomaly in battery pack. Based on data collected by a naturalistic driving experiment with 20 EVs, this study proposes a temporal convolutional network (TCN) algorithm for battery temperature anomaly prediction. Firstly, 40 features encompassing battery signals, thermal management state, ambient temperature, and driving condition are extracted from micro-segments. Then, the most effective input features are selected between the 40 features through maximum information coefficient (MIC) correlation analysis, and the principal component analysis (PCA). After obtaining the optimal hyperparameters, the TCN model is trained using the data from four EVs. The model's performance in predicting temperature is assessed over the data of the remaining 16 vehicles. The results demonstrate that the model achieves accurate prediction with the maximum and minimum mean relative error (MRE) of 0.0132 and 0.0072 across the 16 test vehicles. Moreover, the model proves to be robust against different testing seasons, SOCs, and traffic conditions. Compared to convolutional neural network (CNN), long short-term memory network (LSTM), and CNN-LSTM models with same hyperparameters, the developed TCN model consistently obtains the lowest MRE on both training and testing. For two kinds of scenarios where the probe temperature changes slowly and rapidly, the TCN model can predict an impending temperature anomaly up to 40 min in advance, and forecast the temperature anomaly within the future 8 min, respectively. Among the 16 vehicles, 81.25 % demonstrate a high prognosis accuracy, with an average F1 score of 0.951 across 10 of the vehicles. Thus, the proposed method can provide accurate battery temperature anomaly early warning for EVs under actual driving conditions.

为防止电动汽车热失控事故的发生，对电池组温度异常进行预警是至关重要的。基于20辆电动汽车的自然驾驶实验数据，提出了一种基于时间卷积网络（TCN）的电池温度异常预测算法。首先，从微段中提取电池信号、热管理状态、环境温度和驾驶状态等40个特征。然后，通过最大信息系数（MIC）相关分析和主成分分析（PCA），从40个特征中选择最有效的输入特征。在获得最优超参数后，使用4辆电动汽车的数据对TCN模型进行训练。该模型在预测温度方面的性能通过其余16辆车的数据进行评估。结果表明，该模型对16辆试验车辆的最大和最小平均相对误差（MRE）分别为0.0132和0.0072，达到了较好的预测效果。此外，该模型对不同的测试季节、soc和交通条件都具有鲁棒性。与具有相同超参数的卷积神经网络（CNN）、长短期记忆网络（LSTM）和CNN-LSTM模型相比，所建立的TCN模型在训练和测试上均获得了最低的MRE。对于探头温度变化缓慢和快速的两种情景，TCN模式分别可以提前40 min和8 min预测即将发生的温度异常。16辆车中，81.25%的预测准确率较高，10辆车的F1平均得分为0.951。因此，该方法可为电动汽车在实际行驶工况下提供准确的电池温度异常预警。

{"title":"Battery temperature anomaly early warning for electric vehicles under real driving conditions using a temporal convolutional network","authors":"Shaopeng Li , Hui Zhang , Daniela Anna Misul , Federico Miretti , Matteo Acquarone , Naikan Ding , Dingan Ni , Ninghao Hou , Yanjie He , Yijun Zhang , Yifan Sun","doi":"10.1016/j.etran.2025.100445","DOIUrl":"10.1016/j.etran.2025.100445","url":null,"abstract":"<div><div>For preventing thermal runaway accidents in electric vehicles (EVs), it is crucial to conduct early warning for temperature anomaly in battery pack. Based on data collected by a naturalistic driving experiment with 20 EVs, this study proposes a temporal convolutional network (TCN) algorithm for battery temperature anomaly prediction. Firstly, 40 features encompassing battery signals, thermal management state, ambient temperature, and driving condition are extracted from micro-segments. Then, the most effective input features are selected between the 40 features through maximum information coefficient (MIC) correlation analysis, and the principal component analysis (PCA). After obtaining the optimal hyperparameters, the TCN model is trained using the data from four EVs. The model's performance in predicting temperature is assessed over the data of the remaining 16 vehicles. The results demonstrate that the model achieves accurate prediction with the maximum and minimum mean relative error (MRE) of 0.0132 and 0.0072 across the 16 test vehicles. Moreover, the model proves to be robust against different testing seasons, SOCs, and traffic conditions. Compared to convolutional neural network (CNN), long short-term memory network (LSTM), and CNN-LSTM models with same hyperparameters, the developed TCN model consistently obtains the lowest MRE on both training and testing. For two kinds of scenarios where the probe temperature changes slowly and rapidly, the TCN model can predict an impending temperature anomaly up to 40 min in advance, and forecast the temperature anomaly within the future 8 min, respectively. Among the 16 vehicles, 81.25 % demonstrate a high prognosis accuracy, with an average F1 score of 0.951 across 10 of the vehicles. Thus, the proposed method can provide accurate battery temperature anomaly early warning for EVs under actual driving conditions.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100445"},"PeriodicalIF":15.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623777","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

Dynamic bus charge scheduling by model predictive control to maximize local PV surplus power utilization 基于模型预测控制的动态充电调度，最大化局部光伏剩余电量利用率

IF 15 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-07-03 DOI: 10.1016/j.etran.2025.100441

Fumiaki Osaki , Yu Fujimoto , Yutaka Iino , Yuto Ihara , Masataka Mitsuoka , Yasuhiro Hayashi

As the electrification of public transport and the adoption of variable renewable energy accelerate the transition to carbon neutrality, integrating local photovoltaic (PV) surplus power into electric bus charging operations becomes increasingly critical. However, uncertainties in PV generation and traffic delays often reduce the effective utilization of PV surplus due to missed charging opportunities. To address these challenges, this study proposes a dynamic charging scheduling method based on model predictive control (MPC), which adaptively updates the schedule using quasi-real-time, district-scale information. The framework integrates real-time traffic delays in the General Transit Feed Specification format (GTFS Realtime), smart meter measurements, and meteorological satellite observations—data sources currently available in real cities. At each update step, the system forecasts PV surplus power using a machine learning model that captures temporal weather conditions and localized PV surplus trends around charging stations, while detecting bus delays at each station. Based on this information, the optimal charging schedule is updated every 30 min to adaptively maximize PV surplus utilization. Numerical experiments simulating an entire year demonstrate the effectiveness of the proposed method. Compared to a fixed day-ahead schedule and a rule-based charging method, it improves the annual average PV surplus utilization rate by up to 11.9% and reduces annual average grid power purchases by up to 15.6%. These results highlight the potential of combining MPC with quasi-real-time, district-scale data to proactively and robustly integrate renewable energy into public electric bus operations under uncertainty.

随着公共交通的电气化和可变可再生能源的采用加速了向碳中和的过渡，将当地光伏（PV）剩余电力整合到电动公交车充电操作中变得越来越重要。然而，光伏发电的不确定性和交通延迟往往会因错过充电机会而降低光伏剩余的有效利用。为了解决这些问题，本研究提出了一种基于模型预测控制（MPC）的动态充电调度方法，该方法利用准实时的区域尺度信息自适应更新充电调度。该框架集成了通用交通馈送规范格式（GTFS Realtime）的实时交通延迟、智能电表测量和气象卫星观测，这些数据源目前在实际城市中可用。在每个更新步骤中，系统使用机器学习模型预测光伏剩余电量，该模型捕获时间天气条件和充电站周围的局部光伏剩余趋势，同时检测每个充电站的公交车延误。基于这些信息，每30分钟更新一次最优充电计划，以自适应最大化光伏剩余利用率。一整年的数值模拟实验证明了该方法的有效性。与固定日前计划和基于规则的充电方式相比，该方案可将年平均光伏剩余利用率提高11.9%，将年平均电网购电量降低15.6%。这些结果突出了MPC与准实时、区域尺度数据相结合的潜力，可以在不确定的情况下主动、稳健地将可再生能源整合到公共电动巴士运营中。

{"title":"Dynamic bus charge scheduling by model predictive control to maximize local PV surplus power utilization","authors":"Fumiaki Osaki , Yu Fujimoto , Yutaka Iino , Yuto Ihara , Masataka Mitsuoka , Yasuhiro Hayashi","doi":"10.1016/j.etran.2025.100441","DOIUrl":"10.1016/j.etran.2025.100441","url":null,"abstract":"<div><div>As the electrification of public transport and the adoption of variable renewable energy accelerate the transition to carbon neutrality, integrating local photovoltaic (PV) surplus power into electric bus charging operations becomes increasingly critical. However, uncertainties in PV generation and traffic delays often reduce the effective utilization of PV surplus due to missed charging opportunities. To address these challenges, this study proposes a dynamic charging scheduling method based on model predictive control (MPC), which adaptively updates the schedule using quasi-real-time, district-scale information. The framework integrates real-time traffic delays in the General Transit Feed Specification format (GTFS Realtime), smart meter measurements, and meteorological satellite observations—data sources currently available in real cities. At each update step, the system forecasts PV surplus power using a machine learning model that captures temporal weather conditions and localized PV surplus trends around charging stations, while detecting bus delays at each station. Based on this information, the optimal charging schedule is updated every 30 min to adaptively maximize PV surplus utilization. Numerical experiments simulating an entire year demonstrate the effectiveness of the proposed method. Compared to a fixed day-ahead schedule and a rule-based charging method, it improves the annual average PV surplus utilization rate by up to 11.9% and reduces annual average grid power purchases by up to 15.6%. These results highlight the potential of combining MPC with quasi-real-time, district-scale data to proactively and robustly integrate renewable energy into public electric bus operations under uncertainty.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100441"},"PeriodicalIF":15.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588837","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

Hybrid fusion for battery degradation diagnostics using minimal real-world data: Bridging laboratory and practical applications 混合融合电池退化诊断使用最小的真实世界数据：桥接实验室和实际应用

IF 15 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-07-02 DOI: 10.1016/j.etran.2025.100446

Yisheng Liu , Boru Zhou , Tengwei Pang , Guodong Fan , Xi Zhang

Unpredictability of battery lifetime has been a key stumbling block to technology advancement of safety-critical systems such as electric vehicles and stationary energy storage systems. In this work, we present a novel hybrid fusion strategy that combines physics-based and data-driven approaches to accurately predict battery capacity. This strategy, implemented via a convolutional neural network, achieves an average estimation error of only 0.63 % over the entire battery lifespan, utilizing merely 45 real-world data segments along with over 1.7 million simulated data segments derived from random partial charging cycles. By leveraging a thoroughly validated reduced-order electrochemical model, we extract typical aging patterns from laboratory aging data and extend them into a more comprehensive parameter space, encompassing diverse battery aging states in potential real-world applications while accounting for practical cell-to-cell variations. By bridging the gap between controlled laboratory experiments and real-world usage scenarios, this method highlights the significant potential of transferring underlying knowledge from high-fidelity physics-based models to data-driven models for predicting the behavior of complex dynamical systems.

电池寿命的不可预测性一直是电动汽车和固定式储能系统等安全关键系统技术进步的主要障碍。在这项工作中，我们提出了一种新的混合融合策略，结合了基于物理和数据驱动的方法来准确预测电池容量。该策略通过卷积神经网络实现，在整个电池寿命期间平均估计误差仅为0.63%，仅使用45个真实数据段以及来自随机部分充电周期的170多万个模拟数据段。通过利用经过彻底验证的降阶电化学模型，我们从实验室老化数据中提取典型的老化模式，并将其扩展到更全面的参数空间，包括潜在的实际应用中的各种电池老化状态，同时考虑到实际电池之间的变化。通过弥合受控实验室实验和现实世界使用场景之间的差距，该方法突出了将基础知识从高保真物理模型转移到数据驱动模型以预测复杂动力系统行为的巨大潜力。

{"title":"Hybrid fusion for battery degradation diagnostics using minimal real-world data: Bridging laboratory and practical applications","authors":"Yisheng Liu , Boru Zhou , Tengwei Pang , Guodong Fan , Xi Zhang","doi":"10.1016/j.etran.2025.100446","DOIUrl":"10.1016/j.etran.2025.100446","url":null,"abstract":"<div><div>Unpredictability of battery lifetime has been a key stumbling block to technology advancement of safety-critical systems such as electric vehicles and stationary energy storage systems. In this work, we present a novel hybrid fusion strategy that combines physics-based and data-driven approaches to accurately predict battery capacity. This strategy, implemented via a convolutional neural network, achieves an average estimation error of only 0.63 % over the entire battery lifespan, utilizing merely 45 real-world data segments along with over 1.7 million simulated data segments derived from random partial charging cycles. By leveraging a thoroughly validated reduced-order electrochemical model, we extract typical aging patterns from laboratory aging data and extend them into a more comprehensive parameter space, encompassing diverse battery aging states in potential real-world applications while accounting for practical cell-to-cell variations. By bridging the gap between controlled laboratory experiments and real-world usage scenarios, this method highlights the significant potential of transferring underlying knowledge from high-fidelity physics-based models to data-driven models for predicting the behavior of complex dynamical systems.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100446"},"PeriodicalIF":15.0,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549436","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

Dynamic control of stack temperature prevents abnormal flooding in 60 kW PEM fuel Cells: Modeling and 2000h road validation 60kw PEM燃料电池堆温动态控制防止异常泛油：建模和2000h道路验证

IF 15 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-07-01 DOI: 10.1016/j.etran.2025.100447

Shuai Zhu, Po Hong, Pingwen Ming, Cunman Zhang, Bing Li, Weibo Zheng

Water content inside the stack affects durability of the proton exchange membrane fuel cell in vehicle. Gas temperature and relative humidity at stack inlet are important factors affecting the water content. This paper proposes a model-based dynamic control of stack temperature to prevent abnormal flooding in a 60 kW PEM fuel cell stack with experiment validation. To be specific, a hydrothermal dynamic model of air supply subsystem including gas-gas humidifier is established by taking into consideration heat exchange between air supply subsystem and environment, heat capacity of humidifier and influence of liquid water at stack outlet on exchange of heat and water in humidifier. Simulation result shows that during load change, liquid water at stack outlet and thermal response of parts of air supply subsystem (particularly the humidifier) dominate large latency and multi-stage dynamic response of gas temperature and relative humidity at stack inlet. Experiment is performed on a 60 kW fuel cell system. During load increase, gas temperature at stack inlet rises in four stages, which is consistent with simulation result. During load decrease, average high frequency impedance, air temperature at stack inlet and average cell voltage of the stack are gradually decreased and reach stable state in about 2000s. Experiment result validates the dynamic model and discovers abnormal phenomenon of flooding for the stack at 87A. Accordingly, a control strategy for water management by adjusting stack temperature is further developed to adapt to variable environment condition. Finally, road test indicates that the water management strategy effectively reduces degradation rate of cell voltage to −2.18μV/h within 2000h from winter to autumn.

车用质子交换膜燃料电池的堆内含水量影响其耐久性。烟道入口气体温度和相对湿度是影响烟道含水率的重要因素。本文提出了一种基于模型的燃料电池堆温动态控制方法，并进行了实验验证。具体而言，考虑送风分系统与环境的热交换、加湿器的热容以及烟囱出口液态水对加湿器内热水交换的影响，建立了包括气-气加湿器在内的送风分系统的水热动力学模型。仿真结果表明，在负荷变化过程中，烟囱出口液态水和部分送风子系统（特别是加湿器）的热响应主导了烟囱进口气体温度和相对湿度的大滞后、多级动态响应。实验在60kw燃料电池系统上进行。在负荷增加过程中，烟囱进口温度呈4个阶段上升，与模拟结果一致。在负荷下降过程中，平均高频阻抗、堆入口空气温度和堆芯平均电压在2000年左右逐渐降低，达到稳定状态。实验结果验证了动态模型的正确性，发现了87A叠堆的异常泛水现象。在此基础上，进一步提出了一种通过调节堆温来控制水管理的策略，以适应变化的环境条件。道路试验结果表明，在冬季至秋季的2000h内，水管理策略有效地降低了电池电压的降解率至- 2.18μV/h。

{"title":"Dynamic control of stack temperature prevents abnormal flooding in 60 kW PEM fuel Cells: Modeling and 2000h road validation","authors":"Shuai Zhu, Po Hong, Pingwen Ming, Cunman Zhang, Bing Li, Weibo Zheng","doi":"10.1016/j.etran.2025.100447","DOIUrl":"10.1016/j.etran.2025.100447","url":null,"abstract":"<div><div>Water content inside the stack affects durability of the proton exchange membrane fuel cell in vehicle. Gas temperature and relative humidity at stack inlet are important factors affecting the water content. This paper proposes a model-based dynamic control of stack temperature to prevent abnormal flooding in a 60 kW PEM fuel cell stack with experiment validation. To be specific, a hydrothermal dynamic model of air supply subsystem including gas-gas humidifier is established by taking into consideration heat exchange between air supply subsystem and environment, heat capacity of humidifier and influence of liquid water at stack outlet on exchange of heat and water in humidifier. Simulation result shows that during load change, liquid water at stack outlet and thermal response of parts of air supply subsystem (particularly the humidifier) dominate large latency and multi-stage dynamic response of gas temperature and relative humidity at stack inlet. Experiment is performed on a 60 kW fuel cell system. During load increase, gas temperature at stack inlet rises in four stages, which is consistent with simulation result. During load decrease, average high frequency impedance, air temperature at stack inlet and average cell voltage of the stack are gradually decreased and reach stable state in about 2000s. Experiment result validates the dynamic model and discovers abnormal phenomenon of flooding for the stack at 87A. Accordingly, a control strategy for water management by adjusting stack temperature is further developed to adapt to variable environment condition. Finally, road test indicates that the water management strategy effectively reduces degradation rate of cell voltage to −2.18μV/h within 2000h from winter to autumn.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"25 ","pages":"Article 100447"},"PeriodicalIF":15.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623666","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