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State-of-charge estimation for lithium-ion battery during constant current charging process based on model parameters updated periodically

恒流 荷电状态 电流(流体) 锂离子电池 锂(药物) 常量(计算机编程) 国家(计算机科学) 离子 计算机科学 电荷(物理) 电池(电) 过程(计算) 时间常数 电气工程 材料科学 工程类 算法 化学 热力学 功率(物理) 有机化学 程序设计语言 内分泌学 物理 医学 量子力学 操作系统
作者
Shuzhi Zhang,Qiang Zhang,Dayong Liu,Xiaoyan Dai,Xiongwen Zhang
出处
期刊:Energy [Elsevier BV]
卷期号:257: 124770-124770 被引量:10
标识
DOI:10.1016/j.energy.2022.124770
摘要

With online established battery model, model-based estimation method can track battery state-of-charge (SOC) precisely under dynamic conditions. Nevertheless, both recursive least square-based and filter-based methods cannot distinguish whether the voltage difference comes from SOC difference or internal resistance difference during constant current (CC) conditions, further leading to erroneously identified model parameters and inaccurate SOC estimation. To address this issue, a novel SOC estimation method during CC charging process by fusion of global optimization algorithm and Kalman filter family algorithm is developed in this paper. Firstly, some key parameters that are helpful for initialization and lower/upper bounds setting for global optimization method are extracted from electric vehicles’ driving process. Secondly, considering the shortcomings in traditional global optimization methods, including possible premature convergence, slow search speed in the late stage and relatively large computational cost, an improved particle swarm optimization is designed to periodically update model parameters during CC charging process. With obtained model parameters, SOC is further tracked via extended Kalman filter (EKF). The verification results based on experimental data demonstrates that the developed method can significantly weaken the strong cross-interference between model parameters and SOC, further achieving much more accurate SOC estimation than existing dual/joint EKF during CC charging process. • A novel SOC online estimation method during CC charging process is proposed. • IPSO is designed to periodically update model parameters during CC charging process. • Some key parameters used for IPSO algorithm are extracted from EVs' driving process. • The cross-interference between model parameters and SOC can be greatly weakened. • The proposed method can track SOC much more precisely than existing dual/joint EKF.
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