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1.中北大学 能源与动力工程学院,山西 太原 030051
2.中北大学 高端装备可靠性技术山西省重点实验室,山西 太原 030051
李琪(1992-), 女, 硕士生, 主要从事新能源汽车动力系统的研究。
李晓杰(1979-), 男, 高级实验师, 博士, 主要从事新能源汽车动力系统的研究。E-mail: lixiaojie16@nuc.edu.cn。
收稿:2024-06-21,
纸质出版:2025-06-30
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李琪, 董小瑞, 李晓杰. 基于SEI膜增厚的锂电池老化-电化学-热模型与验证[J]. 中北大学学报(自然科学版), 2025, 46(3): 306-315.
LI Qi, DONG Xiaorui , LI Xiaojie. Modeling and validation of lithium battery aging-electrochemical-thermal behavior based on SEI film thickening[J]. Journal of North University of China(Natural Science Edition), 2025, 46(3): 306-315.
李琪, 董小瑞, 李晓杰. 基于SEI膜增厚的锂电池老化-电化学-热模型与验证[J]. 中北大学学报(自然科学版), 2025, 46(3): 306-315. DOI: 10.62756/jnuc.issn.1673-3193.2024.06.0010.
LI Qi, DONG Xiaorui , LI Xiaojie. Modeling and validation of lithium battery aging-electrochemical-thermal behavior based on SEI film thickening[J]. Journal of North University of China(Natural Science Edition), 2025, 46(3): 306-315. DOI: 10.62756/jnuc.issn.1673-3193.2024.06.0010.
在锂离子电池的长期使用过程中, 固体电解质界面膜(SEI膜)的形成和增厚是导致电池性能劣化和寿命缩短的关键因素。为准确评估锂电池的健康状态和热行为, 本文提出了一种基于SEI膜增厚影响的老化-电化学-热耦合模型。首先, 模型基于经典的电化学方程、 电池产热理论以及能量守恒方程, 考虑了SEI膜增厚导致的内阻增加和容量衰减, 建立了锂离子电池的电化学-热耦合模型。其次, 对模型参数进行了Arrhenius方程的修正, 并使用遗传算法结合COMSOL与MATLAB进行联合仿真, 构建了双层径向模型用以精确辨识电池的热物性参数。最后, 在25 ℃下进行了不同倍率的充放电实验, 获取了电压-容量特性曲线和温升曲线, 并与模型仿真结果进行了对比。实验结果显示, 该老化-电化学-热耦合模型能够在各倍率下有效预测电池的健康状态和热特性, 能够有效评估容量衰减状态和健康状态, 以及有效预测容量衰退轨迹, 容量保持率的最大绝对误差仅为2.616%, 验证了模型的准确性和可靠性。本文研究可为电动汽车电池管理系统的开发提供理论基础。
In the long-term use of lithium-ion batteries, the formation and thickening of the solid electrolyte interphase (SEI) film are key factors that lead to performance degradation and shortened lifespan. To accurately assess the health state and thermal behavior of lithium batteries, this paper proposed an aging-electrochemical-thermal coupling model based on the impact of SEI thickening. Firstly, the model, based on classical electrochemical equations, battery heat generation theory, and energy conservation equations, considered the internal resistance increase and capacity decay caused by SEI thickening to establish an electrochemical-thermal coupling model for lithium-ion batteries. Secondly, we modified the model parameters using the Arrhenius equation and conduct joint simulations using genetic algorithms combined with COMSOL and MATLAB to constructed a double-layer radial model for precise identification of the thermal physical parameters of the battery. Finally, different rate charge-discharge experiments were conducted at 25°C to obtain voltage-capacity characteristic curves and temperature rise curves, which were compared with the model simulation results. The experimental results show that the aging-electrochemical-thermal coupling model can effectively predict the battery’s health state and thermal characteristics at various discharge rates. It can accurately assess the capacity degradation and health status, as well as predict the capacity degradation trajectory with a maximum absolute error of only 2.616% of capacity retention rate, validating the model’s accuracy and reliability. This provides a solid theoretical foundation for the development of battery management systems for electric vehicles.
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