Online Joint Estimation of Main States of Lithium-Ion Battery Based on DAEKF Algorithm

被引：0

作者：

Luo Y. ^{[1
]}

Wu Z. ^{[1
]}

机构：

[1] School of Mechanical and Automotive Engineering, South China University of Technology, Guangdong, Guangzhou

来源：

Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science) | 2023年 / 51卷 / 01期

关键词：

double adaptive extended Kalman filter; electric vehicle; lithium-ion battery; multi-state online joint estimation; multi-time scale;

D O I：

10.12141/j.issn.1000-565X.220050

中图分类号：

学科分类号：

摘要：

In order to realize the online joint estimation of three major states of ternary lithium-ion battery, namely SOC (State of charge), SOH (State of Health) and SOE (State of Energy), and to deal with the open-loop cumulative error caused by various noises in the actual use of electric vehicles, and, furthermore, to improve the stability of online estimation of lithium-ion battery, this paper proposed an online joint estimation method of the three major states of ternary lithium-ion battery in multiple time scales based on double adaptive extended Kalman filter (DAEKF). In the investigation, the state space equation of DAEKF algorithm is derived based on the second-order RC model, and the parameters are identified online by the recursive least square method with forgetting factor (FFRLS). The SOC and SOE of lithium-ion battery are estimated online in the micro time scale, and the SOH of lithium-ion battery is estimated online in the macro time scale. Thus, the online joint estimation of the three major states of lithium-ion battery can be realized. Finally, the proposed method was verified by experiments under different operating conditions of NVR18650B ternary lithium-ion battery. The experimental results show that the proposed method can rapidly converge the model parameters under the two verification conditions; that the estimation errors of SOC and SOE in the micro time scale are kept within 1%, and the estimation errors of SOH in the macro time scale are kept within 1. 6%; and that, as compared with the EKF algorithm, the proposed method has a higher estimation accuracy and better estimation convergence and stability. © 2023 South China University of Technology. All rights reserved.

引用

页码：84 / 94

页数：10

共 22 条

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