基于多新息辨识算法的锂离子电池等效电路模型参数辨识

doi:10.19799/j.cnki.2095-4239.2023.0358

储能科学与技术 ›› 2023, Vol. 12 ›› Issue (10): 3155-3169.doi: 10.19799/j.cnki.2095-4239.2023.0358

基于多新息辨识算法的锂离子电池等效电路模型参数辨识

林鹏¹(), 刘涛², 金鹏³^,⁴^,⁵(), 王震坡³, 王生捷¹, 袁红升¹, 马泽¹, 狄宇¹

^1.北京机械设备研究所，北京 100854
^2.中国人民解放军63936部队，北京 100024
^3.北京理工大学机械与车辆学院，电动车辆国家工程研究中心，北京 100081
^4.北方工业大学电气与控制工程学院
^5.北京电动车辆协同创新中心，北京 100144

收稿日期:2023-05-25 修回日期:2023-07-20 出版日期:2023-10-05 发布日期:2023-10-09
通讯作者: 金鹏 E-mail:leenzi@163.com;jpzy216@163.com
作者简介:林鹏（1988—），男，博士，高级工程师，研究方向为储能/动力电池建模、电池管理系统，E-mail：leenzi@163.com；

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

Peng LIN¹(), Tao LIU², Peng JIN³^,⁴^,⁵(), Zhenpo WANG³, Shengjie WANG¹, Hongsheng YUAN¹, Ze MA¹, Yu DI¹

^1.Beijing Mechanical Equipment Institut, Beijing 100854, China
^2.Vehicle Research Institutee, Beijing 100024, China
^3.National Engineering Research Center of Electric Vehicles, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
^4.School of Electrical and Control Engineering, North China University of Technology
^5.Collaborative Innovation Center of Electric Vehicle in Beijing, Beijing 100144, China

Received:2023-05-25 Revised:2023-07-20 Online:2023-10-05 Published:2023-10-09
Contact: Peng JIN E-mail:leenzi@163.com;jpzy216@163.com

摘要/Abstract

摘要：

实时、准确地获得电池模型的参数可提高电池状态估计的精度。常用的系统辨识算法和智能优化算法不仅实时性差，而且辨识精度低。为了解决等效电路模型的参数辨识及提高等效电路模型参数的辨识精度，本文通过直接离散的方法建立了能够同时辨识二阶RC(resistance-capacitance)等效电路模型和PNGV(partnership for a new generation of vehicles)模型参数的差分方程。基于多新息算法辨识理论，提出了带遗忘因子的多新息辅助模型扩展递推最小二乘(FMIAELS)算法。FMIAELS算法只需利用电池的电流及端电压即可实现等效电路模型参数的实时、精确辨识。实验验证结果表明，在不同温度、工况和老化程度下，FMIAELS算法可精确地辨识电池的模型参数，误差约为常用的系统辨识算法和智能优化算法的1/3。FMIAELS算法也能实现开路电压(OCV)的精确辨识，在不同脉冲下辨识的OCV的精度也明显优于常用的系统辨识算法和智能优化算法，其平均误差仅有0.22%。

关键词: 等效电路模型, 模型参数辨识, 多新息辨识算法, 锂离子电池

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

中图分类号:

TM 912.8

林鹏, 刘涛, 金鹏, 王震坡, 王生捷, 袁红升, 马泽, 狄宇. 基于多新息辨识算法的锂离子电池等效电路模型参数辨识[J]. 储能科学与技术, 2023, 12(10): 3155-3169.

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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基于多新息辨识算法的锂离子电池等效电路模型参数辨识

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

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