融合阻抗模型与扩展卡尔曼滤波的锂离子电池荷电状态估算

doi:10.16088/j.issn.1001-6600.2020061109

摘要/Abstract

摘要： 随着电动汽车(electric vehicles,EV)的发展,电池荷电状态(state of charge,SOC)估计受到越来越多关注。荷电状态的精确估计对于电动汽车的能量管理至关重要,然而,估算精度成为限制其发展的瓶颈。本文在阻抗谱分析基础上,利用恒相元件(constant phase element,CPE)导出简化的电池阻抗模型,从而建立模型的状态方程和观测方程;针对锂电池的非线性特性,引入扩展卡尔曼滤波(extended Kalman filtering,EKF),通过在阻抗模型上与EKF算法的融合对锂离子电池进行SOC准确估算;建立电池测试台,通过仿真和电池动态工况试验验证。结果表明,与其他模型和EKF算法相比,所提出的SOC估算方法能有效提高SOC估算精度,并将误差控制在±1％以内,具有较好的收敛性和鲁棒性。

关键词: 锂离子电池, 电动汽车, 荷电状态估计, 阻抗模型, 扩展卡尔曼滤波

Abstract: With the development of electric vehicle (EV), the estimation of battery state of charge (SOC) has attracted more and more attention. Accurate estimation of state of charge is very important for the energy management of electric vehicles. However, the accuracy of estimation has become the bottleneck of its development. Based on the analysis of impedance spectrum, a simplified impedance model of battery is derived by using constant phase element (CPE). By introducing EKF, a battery test platform is established, through the fusion with the EKF algorithm on the impedance model to estimate the SOC of lithium-ion battery accurately. The results of simulation and dynamic state test show that compared with other models and EKF algorithm, the SOC estimation method proposed has good performances in convergence and robustness, which could control the error within ±1%.

Key words: lithium-ion battery, electric vehicles, estimation of SOC, impedance model, extended Kalman filtering

中图分类号:

TM912

王瑞, 宋树祥, 夏海英. 融合阻抗模型与扩展卡尔曼滤波的锂离子电池荷电状态估算[J]. 广西师范大学学报（自然科学版）, 2021, 39(3): 1-10.

WANG Rui, SONG Shuxiang, XIA Haiying. Estimation of Lithium Battery SOC with Fusion Impedance Model and Extended Kalman Filtering[J]. Journal of Guangxi Normal University(Natural Science Edition), 2021, 39(3): 1-10.

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