Lithium-ion battery equivalent model over full-range state of charge based on electrochemical process simplification

电化学 电池(电) 锂离子电池 离子 荷电状态 航程(航空) 锂(药物) 材料科学 过程(计算) 等效电路 电荷(物理) 化学 计算机科学 电极 热力学 物理 电气工程 物理化学 电压 工程类 复合材料 有机化学 功率(物理) 医学 量子力学 内分泌学 操作系统
作者
Dafang Wang,Li Xu,Jingwei Wang,Qi Zhang,Bowen Yang,Ziwei Hao
出处
期刊:Electrochimica Acta [Elsevier BV]
卷期号:389: 138698-138698 被引量:22
标识
DOI:10.1016/j.electacta.2021.138698
摘要

• A new equivalent model with high accuracy over full-range SOC is proposed. • The electrochemical processes are approximately described in the model. • Online parameters identification is applied to increase the adaption of the model. • The model is suitable for application to battery state estimation in BMS. State estimation is a key issue of battery management system (BMS) to improve the energy utilization of lithium-ion battery in electric vehicle, the performance of which relies on the accuracy of equivalent model over full-range sate of charge (SOC). However, the widely applied equivalent circuit model (ECM) has limitations at low SOC range. In this paper, an optimized equivalent model is proposed combining the lithium-ion battery internal electrochemical processes with the ECM. For one thing, the proposed model is able to offer high accuracy by considering solid-phase diffusion into the update of open circuit voltage (OCV) and describing the polarization at the solid-liquid interface from the electrochemical perspective. For another thing, the proposed approximate method for describing internal electrochemical micro-variables with external electric macro-variables allows the model to avoid a series of partial differential equations. In addition, online parameter identification based on the forgetting factor based recursive least square method (FFRLS) makes the proposed model well-adapted to dynamic working conditions. Compared with the ECM under multiple working conditions, the proposed model is proven to provide a better performance over full-range SOC especially at the low-range area below 20%.

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