基于充电过程的锂电池SOH估计和RUL预测

doi:10.19799/j.cnki.2095-4239.2022.0165

摘要/Abstract

摘要：

车用锂离子电池的健康状态(state of health，SOH)和剩余寿命(remaining useful life，RUL)是锂离子电池的关键状态参数，为实现其准确的预估以保障整车安全可靠的运行，基于电动汽车充电过程提出一种改进高斯过程回归(Gaussian process regression，GPR)的锂电池SOH估计和RUL预测模型。首先以最大互信息系数(maximal information coefficient，MIC)、Pearson系数筛选充电过程的多元信息作为健康因子，基于主成分分析(principal components analysis，PCA)简化模型结构并使用粒子群算法和组合核函数改进高斯过程回归，实现车用锂离子电池SOH的准确在线估计以及RUL预测。通过NASA锂离子电池数据集验证了模型的有效性：测试电池SOH估计的最大均方根误差(root mean square error，RMSE)为0.0148，SOH预测的最大RMSE为0.0169，RUL预测的最大绝对误差为1个循环次数。

关键词: 锂离子电池, 健康状态, 剩余寿命, 高斯过程回归

Abstract:

The state of health (SOH) and the remaining useful life (RUL) of lithium-ion batteries for vehicles are key state parameters. Based on the charging process of electric vehicles, an improved Gaussian process regression (GPR) model for lithium battery SOH estimation and RUL prediction is proposed to achieve accurate estimation of SOH and RUL of the battery to ensure the safe and reliable operation of the vehicle. First, the maximal information coefficient (MIC) and Pearson coefficient are used to screen the multivariate information of the charging process as health factors. Further, the model structure is simplified using principal components analysis (PCA). The Gaussian process regression is then improved using particle swarm optimization and the combined kernel function. Finally, accurate online estimation of SOH and prediction of future RUL and SOH are realized. The validity of the model is verified with the NASA lithium-ion battery data set. This model outperforms other studies in terms of estimation and prediction accuracy. The maximum root mean square error (RMSE) of SOH estimation for test batteries is 0.0148, the maximum RMSE of SOH prediction is 0.0169, and the maximum absolute error of RUL prediction is 1 cycle.

Key words: lithium-ion battery, state of health, remaining useful life, gaussian process regression

中图分类号:

TM 912

李放, 闵永军, 王琛, 张涌. 基于充电过程的锂电池SOH估计和RUL预测[J]. 储能科学与技术, 2022, 11(10): 3316-3327.

Fang LI, Yongjun MIN, Chen WANG, Yong ZHANG. State of health estimation and remaining useful life predication of lithium batteries using charging process[J]. Energy Storage Science and Technology, 2022, 11(10): 3316-3327.

图/表 14

图1

图2

图3

图4

表1

图5

表2

表3

高斯过程回归的超参数"

电池型号	[a;b]	$[σ f 1 2; l 12; σ f 2 2; l 22; p]$
B0005	[0.0893; 1.6571]	[-2.2319; -4.5958; 1.7447; 4.0460; -2.1070]
B0006	[0.1588; 1.5570]	[0.9150; 1.7572; 3.3170; 0.9777; -4.7147]
B0007	[0.0761; 1.6475]	[4.5495; 2.2757; -4.1064; 3.9239; -0.8211]
B0018	[0.0824; 1.5801]	[-4.2300; -4.0529; 2.3312; 2.9800; -2.4340]

表3

图6

表4

图7

表5

图8

表6

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电池型号	相关性分析方法	T_∆_V	Temp _t_-max	T_CC	V₁₀₀₀	S_V	T_CV	T_CC/T_CV	Temp_max	Temp_average
B0005	Pearson	0.9961	0.9958	0.9963	-0.9820	0.9965	-0.9115	-0.9822	-0.5038	0.0119
B0005	MIC	0.9727	0.9672	0.9727	0.9702	0.9727	0.8037	0.9422	0.6004	0.3803
B0006	Pearson	0.9899	0.9904	0.9906	-0.9788	0.9907	-0.9372	-0.9523	0.3451	0.2543
B0006	MIC	0.9727	0.9591	0.9727	0.9727	0.9727	0.8005	0.9349	0.3811	0.4040
B0007	Pearson	0.9890	0.9923	0.9929	-0.9680	0.9927	-0.8828	-0.9799	-0.4357	0.0948
B0007	MIC	0.9727	0.9807	0.9807	0.9546	0.9727	0.6998	0.8926	0.6124	0.4110
B0018	Pearson	0.9854	0.9876	0.9803	-0.9727	0.9819	-0.4089	-0.8854	-0.0236	0.1611
B0018	MIC	0.9791	0.9697	0.9697	0.9791	0.9697	0.7087	0.9632	0.1847	0.1197

成分	特征值
F₁	4.9749
F₂	0.0236
F₃	0.0009
F₃	0.0005
F₅	0.0001

电池型号	PE-GPR		NN-GPR		NN+PE-GPR		PSO- NN+PE-GPR
电池型号	MAPE/%	RMSE	MAPE/%	RMSE	MAPE/%	RMSE	MAPE/%	RMSE
B0005	8.4889	0.0767	3.2967	0.0290	2.9605	0.0220	0.6426	0.0070
B0006	7.5277	0.0554	5.9013	0.0452	2.5350	0.0197	1.0376	0.0092
B0007	3.9387	0.0384	3.4364	0.0259	2.5054	0.0204	0.5022	0.0051
B0018	5.6665	0.0416	3.4991	0.0324	1.8643	0.0200	0.9604	0.0148

电池型号	PSO-NN+PE-GPR		文献[26]		RVM
电池型号	MAPE/%	RMSE	MAPE/%	RMSE	MAPE/%	RMSE
B0005	0.6426	0.0070	0.6000	0.0120	2.9019	0.0263
B0006	1.0376	0.0092	1.2000	0.0190	3.4733	0.0282
B0007	0.5022	0.0051	—	—	3.6751	0.0366
B0018	0.9604	0.0148	—	—	4.0011	0.0373

电池型号	GPR			文献[26]	文献[27]	二次函数拟合
电池型号	SOH预测 MAPE/%	SOH预测 RMSE	RUL绝对误差	RUL绝对误差	SOH预测 RMSE	SOH预测 MAPE/%	SOH预测RMSE	RUL绝对误差
B0005	0.9393	0.0086	0	1	0.0155	15.5748	0.1230	16
B0006	1.4152	0.0115	0	5	0.0281	4.8294	0.0329	6
B0007	0.7030	0.0064	1	—	0.0148	11.8410	0.0987	18
B0018	1.8957	0.0169	1	—	0.0143	4.9786	0.0446	2