Journal of South China University of Technology(Natural Science) >

2023 , Vol. 51 >Issue 1: 84 - 94

DOI: https://doi.org/10.12141/j.issn.1000-565X.220050

Traffic & Transportation Engineering

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

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  • School of Mechanical and Automotive Engineering，South China University of Technology，Guangzhou 510640，Guangdong，China
罗玉涛（1972-），男，教授，博士生导师，主要从事新能源汽车和无人驾驶汽车研究。

Received date: 2022-02-02

  Online published: 2022-12-07

Supported by

MIIT Special Fund for High-Quality Development of Manufacturing Industry(R?2H?023?QT?001?20221009?001)

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Abstract

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.

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

Cite this article

LUO Yutao, WU Zhiqiang . Online Joint Estimation of Main States of Lithium-Ion Battery Based on DAEKF Algorithm[J]. Journal of South China University of Technology(Natural Science), 2023 , 51(1) : 84 -94 . DOI: 10.12141/j.issn.1000-565X.220050

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