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

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

Abstract

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

CLC Number:

TM912.9

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 Edition), 2023, 51(1): 84-94.

Figures/Tables 15

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Table1

Fig.7

Fig.8

Fig.9

Fig.10

Table 2

Estimation results and errors of SOH under two working conditions"

参数	参数值
参数	验证工况1	验证工况2
初始最大容量/（A·h）	3.350	3.350
$C a c t$ 参考值/（A·h）	3.167	3.167
$C a c t$ 估计值/（A·h）	3.116	3.149
SOH参考值/%	94.54	94.54
SOH估计值/%	93.02	94.00
SOH估计误差/%	1.52	0.54

Table 2

Fig.11

Fig.12

Table 3

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参数	规格或取值
电池类型	三元锂离子电池
电池型号	NVR18650B
体系组分	NCM系
工作电压/V	2.50~4.20
额定电压/V	3.60
额定容量/（A·h）	3.35

工况	算法	最大误差/%
工况	算法	SOC	SOE	SOH
验证工况1	EKF	2.753	2.653	1.991
验证工况1	DAEKF	0.859	0.751	1.520
验证工况2	EKF	2.837	2.952	1.536
验证工况2	DAEKF	0.848	0.540	0.844

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