Parameter identification and state of charge estimation of lithium-ion batteries

doi:10.19799/j.cnki.2095-4239.2022.0298

Abstract

Abstract:

It is crucial for the battery management system of electric vehicles to accurately estimate the state of charge (SOC) of lithium batteries. The offline parameter identification and the online parameter identification approaches of recursive least squares with forgetting factor are employed to identify the parameters in the equivalent circuit, where the second-order RC equivalent circuit model is employed to accurately model the battery, and, after ensuring the accuracy of the model meets the requirements, the extended Kalman filter (EKF) algorithm is employed to accurately estimate the battery's SOC. The simulation experiment was conducted with the federal urban driving schedule (FUDS) and urban dynamometer driving schedule (UDDS), and the standard SOC value in the experiment is compared with the SOC estimation values of offline identification and online identification. The experimental findings demonstrate that the average error of SOC estimation using the EKF algorithm under FUDS and UDDS conditions is less than 2.5%, and the average error of the online parameter identification model decreased by 0.7% and 0.9% than offline parameter identification model, respectively. The battery model under the online parameter identification approach is shown to have higher estimation accuracy and it is proved that the EKF algorithm can realize an accurate estimation of battery SOC.

Key words: lithium-ion battery, equivalent circuit model, forgetting factor recursive least square, extended Kalman filter

CLC Number:

TM 911

Zhicong LIU, Yanhui ZHANG. Parameter identification and state of charge estimation of lithium-ion batteries[J]. Energy Storage Science and Technology, 2022, 11(11): 3613-3622.

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电池参数	规格
类型	LiNMC
额定容量/Ah	2
标称电压/V	3.7
充电截止电压/V	4.2
放电截止电压/V	2.5

SOC	R₀/Ω	R₁/Ω	R₂/Ω	C₁/F	C₂/F
0.1	0.2282	0.0142	0.0084	3720.6	16308
0.2	0.2272	0.0147	0.0158	2224.4	18186
0.3	0.2228	0.0159	0.0131	2345.5	23497
0.4	0.2219	0.0130	0.0083	2261.9	36469
0.5	0.2324	0.0126	0.0083	2037.0	42751
0.6	0.2283	0.0133	0.0059	1592.8	54825
0.7	0.2261	0.0163	0.0061	1422.1	27437
0.8	0.2210	0.0178	0.0065	1286.2	46328
0.9	0.2230	0.0132	0.0056	1599.9	57086

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