Research on Multi-Time Scale SOP Estimation of Lithium-Ion Battery Based on H8 Filter

被引:5
|
作者
Li, Ran [1 ,2 ]
Li, Kexin [1 ,2 ]
Liu, Pengdong [1 ,2 ]
Zhang, Xiaoyu [3 ]
机构
[1] Minist Educ, Automot Elect Drive Control & Syst Integrat Engn R, Harbin 150080, Peoples R China
[2] Harbin Univ Sci & Technol, Sch Elect & Elect Engn, Harbin 150080, Peoples R China
[3] Nankai Univ, Coll Artificial Intelligence, Tianjin 300110, Peoples R China
来源
BATTERIES-BASEL | 2023年 / 9卷 / 04期
关键词
FFRLS; H8; filtering; SOC; SOP; UNSCENTED KALMAN FILTER; STATE-OF-CHARGE; PARAMETER;
D O I
10.3390/batteries9040191
中图分类号
O646 [电化学、电解、磁化学];
学科分类号
081704 ;
摘要
Battery state of power (SOP) estimation is an important parameter index for electric vehicles to improve battery utilization efficiency and maximize battery safety. Most of the current studies on the SOP estimation of lithium-ion batteries consider only a single constraint and rarely pay attention to the estimation of battery state on different time scales, which can reduce the accuracy of SOP estimation and even cause safety problems. In view of this, this paper proposes a multi-time scale and multi-constraint SOP estimation method for lithium-ion batteries based on H8 filtering. Firstly, a second-order RC equivalent circuit model is established with a ternary lithium-ion monolithic battery as the research object, and parameter identification is performed by using the recursive least squares method with a forgetting factor. Secondly, the H8 filtering algorithm is applied to estimate the state of charge (SOC), and then the joint multi-time scale multi-constrained SOC-SOP estimation is performed. Finally, the joint estimation algorithm is validated under UDDS conditions. The mean absolute value relative error (MARE) of SOC estimation is 1.17%, and the MARE of SOP estimation at different time scales is less than 1.6%. The results indicate the high accuracy and strong robustness of the joint estimation method.
引用
收藏
页数:25
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