锂(药物)
电化学
离子
电解质
扩散
锂离子电池
粒子(生态学)
材料科学
荷电状态
健康状况
粒子群优化
计算机科学
生物系统
核工程
电池(电)
化学
工程类
算法
热力学
电极
物理
物理化学
生物
有机化学
功率(物理)
医学
海洋学
地质学
内分泌学
作者
Junya Shao,Junfu Li,Weizhe Yuan,Changsong Dai,Zhen‐Bo Wang,Ming Zhao,Michael Pecht
标识
DOI:10.1016/j.est.2023.106788
摘要
Obtaining the State of Health of lithium-ion batteries and mastering its degradation laws are crucial for the utilization of Electric Vehicles. However, the prediction of discharge capacity of lithium-ion batteries requires high accuracy, which is subject to the variation of cells and the uncertainty of operating conditions. In this work, a discharge capacity prognostics method for lithium-ion batteries is developed based on a simplified electrochemical coupled aging mechanism model. Firstly, the solid-phase diffusion process is analyzed by using a simplified electrochemical model, and the particle rupture stress at different C rates is obtained. Then, based on the aging mechanisms in terms of Solid Electrolyte Interphase (SEI) layer growth model and particle volume expansion model, the SEI growth rate and correlated aging kinetics parameters are optimized by using particle swarm optimization algorithm. Finally, combined with the further analysis of aging mechanisms and variation of model parameters at early, middle, and late stage of degradation, the developed discharge capacity prediction method is verified at separate stages for batteries at 1C, 2C and 3C respectively, with the average relative error of full life cycle no more than 4 %.
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