Identification of lithium-ion battery equivalent circuit model parameters based on the multi-innovation identification algorithm

doi:10.19799/j.cnki.2095-4239.2023.0358

Abstract

Abstract:

This study aims to obtain the battery model parameters in real time and effectively improve the battery state estimation accuracy. The commonly used system identification and intelligent optimization algorithms have poor real-time performances and low identification accuracies. To address the issue on the equivalent circuit model identification and improve the identification accuracy of the equivalent circuit model parameters, this study establishes a difference equation that identifies the parameters of the second-order resistance-capacitance equivalent circuit model and the Partnership for a New Generation of Vehicles model through a direct discretization method. A multi-innovation auxiliary model extended recursive least squares algorithm with a forgetting factor (FMIAELS) is proposed based on the identification theory of the multi-information algorithm. The FMIAELS algorithm realizes a real-time and accurate identification of the equivalent circuit model parameters by using only the current and the terminal voltage of a battery. The experimental verification results demonstrate that the FMIAELS algorithm accurately identifies the battery model parameters under different temperatures, working conditions, and states of health. The error is about 1/3 that of the common system identification and intelligent optimization algorithms. Moreover, the FMIAELS algorithm accurately identifies the open-circuit voltage (OCV). Under various working conditions, its OCV identification accuracy is significantly better than that of the common system identification and intelligent optimization algorithms, yielding only a 0.22% average error.

Key words: equivalent circuit model, model parameter identification, multi-innovation identification algorithm, lithium ion battery

CLC Number:

TM 912.8

Peng LIN, Tao LIU, Peng JIN, Zhenpo WANG, Shengjie WANG, Hongsheng YUAN, Ze MA, Yu DI. Identification of lithium-ion battery equivalent circuit model parameters based on the multi-innovation identification algorithm[J]. Energy Storage Science and Technology, 2023, 12(10): 3155-3169.

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指标	脉冲			算法
指标	MPN	MNP	HPPC	算法
MAE/mV	1.18	1.88	3.00	FMIAELS
	5.80	6.60	6.39	RELS
	3.18	4.24	4.09	FRLS
	545.69	530.2	120.82	RSNA
	295.83	286.57	72.44	MRAS
	8.98	9.42	7.14	ADE
RMSE/mV	1.89	3.09	4.72	FMIAELS
	6.90	7.60	8.67	RELS
	3.85	4.76	7.35	FRLS
	1996.12	1938.31	331.05	RSNA
	1077.18	1039.89	305.25	MRAS
	4.86	11.65	8.10	ADE
WMAPE/%	0.04	0.06	0.09	FMIAELS
	0.18	0.21	0.29	RELS
	0.10	0.13	0.13	FRLS
	16.85	16.37	3.72	RSNA
	9.13	8.85	2.23	MRAS
	0.19	0.29	0.22	ADE

指标	脉冲			算法
指标	MPN	MNP	HPPC	算法
MAE/mV	33.13	33.62	75.59	FMIAELS
	36.10	35.87	78.20	RELS
	38.01	34.73	84.83	FRLS
	5923.89	5174.48	9208.49	RSNA
	5868.99	5108.72	8931.14	MRAS
	31.69	29.34	73.39	ADE
RMSE/mV	38.38	37.44	78.34	FMIAELS
	41.10	40.91	81.40	RELS
	44.00	40.78	88.19	FRLS
	21630.03	18884.44	25434.77	RSNA
	21407.39	18756.21	25195.59	MRAS
	36.45	32.74	77.16	ADE
WMAPE/%	0.97	1.04	2.33	FMIAELS
	1.13	1.27	2.52	RELS
	1.17	1.07	2.61	FRLS
	182.88	160.15	283.57	RSNA
	181.19	158.11	275.03	MRAS
	0.98	0.91	2.26	ADE

指标	脉冲			算法
指标	MPN	MNP	HPPC	算法
MAE/mV	4.04	6.80	4.46	FMIAELS
	17.50	22.22	18.20	RELS
	10.98	14.05	15.45	FRLS
	11.75	16.47	11.83	ADE
RMSE/mV	9.17	10.06	10.13	FMIAELS
	20.60	25.10	25.00	RELS
	12.90	18.15	18.55	FRLS
	16.79	20.74	16.97	ADE
WMAPE/%	0.12	0.21	0.14	FMIAELS
	0.54	0.69	0.56	RELS
	0.34	0.44	0.48	FRLS
	0.37	0.51	0.36	ADE

指标	脉冲			算法
指标	MPN	MNP	HPPC	算法
MAE/mV	2.59	3.17	3.16	FMIAELS
	5.80	9.20	5.20	RELS
	3.13	5.01	4.3	FRLS
	4.89	7.52	7.36	ADE
RMSE/mV	3.50	4.10	5.64	FMIAELS
	6.90	10.70	6.27	RELS
	3.71	5.50	6.27	FRLS
	6.21	9.23	8.48	ADE
WMAPE/%	0.08	0.10	0.10	FMIAELS
	0.18	0.28	0.28	RELS
	0.10	0.16	0.13	FRLS
	0.15	0.23	0.23	ADE

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