基于IPSO-LSTM的新能源汽车锂电池健康状态监测

doi:10.16265/j.cnki.issn1003-3033.2023.09.1250

中国安全科学学报 ›› 2023, Vol. 33 ›› Issue (9): 94-102.doi: 10.16265/j.cnki.issn1003-3033.2023.09.1250

基于IPSO-LSTM的新能源汽车锂电池健康状态监测

刘丹¹^,²(), 王瑞虎¹^,², 吕伟¹^,²^,^**(), 秦岭³, 林水春²^,⁴

¹ 武汉理工大学中国应急管理研究中心,湖北武汉 430070
² 武汉理工大学安全科学与应急管理学院,湖北武汉 430070
³ 武汉理工大学汽车工程学院,湖北武汉 430070
⁴ 格力钛新能源股份有限公司,广东珠海 519040

收稿日期:2023-03-10 修回日期:2023-06-21 出版日期:2023-09-28
通讯作者:
**吕伟 (1989—),男,河南信阳人,博士,副教授,博士生导师,主要从事城市公共安全、应急疏散与避难安置、车辆交通及行人动力学研究。E-mail:weil@whut.edu.cn。

作者简介:

刘丹 (1985—),男,湖南邵阳人,博士,副教授,博士生导师,主要从事机器学习与智能计算、风险评估与应急决策、安全系统建模与仿真等方面的研究。E-mail:liudan8575@whut.edu.cn。

秦岭副教授

基金资助:
国家社会科学基金一般项目资助(23GLB280); 火灾科学国家重点实验室开放课题(HZ2021-KF11); 中央高校基本科研业务费(2022IVA108)

SOH monitoring of new energy vehicle lithium batteries based on IPSO-LSTM

LIU Dan¹^,²(), WANG Ruihu¹^,², LYU Wei¹^,²^,^**(), QIN Ling³, LIN Shuichun²^,⁴

¹ China Research Center for Emergency Management, Wuhan University of Technology, Wuhan Hubei 430070, China
² School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan Hubei 430070, China
³ School of Automotive Engineering, Wuhan University of Technology, Wuhan Hubei 430070, China
⁴ Gree Altairnano New Energy Co., Ltd., Zhuhai Guangdong 519040, China

Received:2023-03-10 Revised:2023-06-21 Published:2023-09-28

摘要/Abstract

摘要：

为监测新能源汽车锂电池的健康状态(SOH),防范电池故障引发安全事故风险,提出改进粒子群算法(IPSO)和长短期记忆(LSTM)神经网络相结合的模型,监测锂电池的SOH。首先,采用Spearman相关性分析法,提取锂电池SOH监测的健康因子;其次,采用线性惯性权重和非对称学习因子改进传统粒子群算法(PSO),利用IPSO算法对LSTM模型的隐含层神经元个数、神经元失活率、批处理值进行关键参数寻优,进一步优化LSTM模型,建立IPSO-LSTM锂电池SOH监测模型;最后,以新能源汽车主流采用的18650锂电池数据集验证IPSO-LSTM模型,并对比分析BP、LSTM和PSO-LSTM这3种模型。结果表明:IPSO-LSTM模型的平均绝对误差(MAE)在0.02以内、均方根误差(RMSE)在0.03以内,监测误差在15%以内,相较于BP、LSTM、PSO-LSTM模型,IPSO-LSTM模型的误差指标值均最小,模型具有更高的精度和稳定性。

关键词: 改进粒子群算法(IPSO), 长短期记忆(LSTM), 新能源汽车, 锂电池, 健康状态(SOH)

Abstract:

In order to monitor the SOH of lithium batteries of new energy vehicles and prevent the risk of safety accidents caused by battery failures, a model combining IPSO and LSTM was proposed. First, Spearman correlation analysis method was used to extract the SOH of lithium batteries. Secondly, the linear inertia weight and asymmetric learning factor were used to improve the traditional particle swarm optimization(PSO) algorithm, and the IPSO algorithm was used to optimize the key parameters of the hidden layer neuron number, neuron inactivation rate and batch processing value of the LSTM model, and then the LSTM model was further optimized, and the IPSO-LSTM lithium battery SOH monitoring model was established. Finally, the model was verified with the 18650 lithium battery data set which was the mainstream of new energy vehicles, and compared with BP, LSTM and PSO-LSTM models. The results show that the mean absolute error (MAE) of IPSO-LSTM model is less than 0.02, the root mean square error (RMSE) is less than 0.03, and the monitoring error is less than 15%. Compared with BP, LSTM and PSO-LSTM model, the error index value of IPSO-LSTM model is the smallest, and the model has higher accuracy and stability.

Key words: improved particle swarm optimization (IPSO), long short-term memory(LSTM), new energy vehicle, lithium battery, state of health (SOH)

刘丹, 王瑞虎, 吕伟, 秦岭, 林水春. 基于IPSO-LSTM的新能源汽车锂电池健康状态监测[J]. 中国安全科学学报, 2023, 33(9): 94-102.

LIU Dan, WANG Ruihu, LYU Wei, QIN Ling, LIN Shuichun. SOH monitoring of new energy vehicle lithium batteries based on IPSO-LSTM[J]. China Safety Science Journal, 2023, 33(9): 94-102.

图/表 12

图1

图2

图3

图4

表1

健康特征相关性分析结果

健康特征	Spearman系数
健康特征	B5	B6	B7	B18
$T 1$	0.954	0.957	0.952	0.917
$T 2$	-0.885	-0.914	-0.852	-0.812
$F 1$	0.953	0.957	0.952	0.917
$F 2$	-0.963	-0.951	-0.890	-0.863
$F 3$	0.954	0.956	0.950	0.920
$F 4$	-0.876	-0.911	-0.852	-0.746
$F 5$	0.949	0.955	0.945	0.851
$F 6$	-0.867	-0.889	-0.830	-0.743
$A 1$	0.037	0.127	0.018	-0.191
$A 2$	-0.337	0.089	-0.056	-0.108

表1

图5

图6

表2

IPSO参数设置

IPSO参数	参数值
粒子数量	20
迭代次数	20
学习因子	$c 1, m a x =$ 1.5, $c 1, m i n =$ 0.7 $c 2, m a x =$ 1.5, $c 2, m i n =$ 0.7
惯性权重	$ω m a x$ =0.9, $ω m i n =$ 0.4

表2

表3

4种模型参数设置

监测模型	参数设置
IPSO-LSTM	$l 1$ =[60,90], $l 2$ =[40,60],d=[30,50], $α$ =[0.18,0.23], $β$ =[10,30], $G m a x =$ 30
PSO-LSTM	$l 1$ =[60,90], $l 2$ =[40,60],d=[30,50], $α$ =[0.18,0.23], $β$ =[10,30], $G m a x =$ 30 $ω = 0.5, c 1 = 1, c 2 = 1$
LSTM	$l 1$ =75, $l 2$ =50,d=40, $α$ =0.2, $β$ =20, $G m a x =$ 30
BP	lr = 0.01, $G m a x =$ 1 000,输入层节点=5,输出层节点数=1

表3

图7

表4

4种模型的RUL监测结果

型号	RUL	BP			LSTM			PSO-LSTM			IPSO-LSTM
型号	RUL	$E R$	$E a$	$E r$ /%	$E R$	$E a$	$E r$ /%	$E R$	$E a$	$E r$ /%	$E R$	$E a$	$E r$ /%
B5	26	44	18	69.2	33	7	26.9	28	2	7.7	27	1	3.8
B6	15	21	6	40.0	19	4	26.6	16	1	6.7	15	0	0
B7	62	29	33	53.2	57	5	8.1	66	4	6.5	63	1	1.6
B18	16	—	—	—	19	3	18.8	19	3	18.8	14	2	12.5

表4

表5

参考文献 18

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型号	BP		LSTM		PSO-LSTM		IPSO-LSTM
型号	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE
B5	0.059 3	0.070 7	0.032 9	0.036 0	0.019 7	0.024 5	0.010 0	0.015 0
B6	0.073 9	0.086 4	0.039 1	0.042 4	0.028 3	0.035 4	0.018 3	0.027 2
B7	0.047 3	0.057 9	0.021 7	0.025 7	0.011 5	0.018 5	0.009 9	0.016 4
B18	0.074 8	0.085 8	0.040 4	0.044 8	0.024 8	0.031 1	0.013 9	0.025 4

基于IPSO-LSTM的新能源汽车锂电池健康状态监测

SOH monitoring of new energy vehicle lithium batteries based on IPSO-LSTM

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