考虑不同充电策略的锂电池健康状态区间估计

doi:10.16183/j.cnki.jsjtu.2022.347

摘要/Abstract

摘要：

评估锂离子电池健康状态(SOH)对于电池使用、维护、管理和经济性评价都有十分重要的意义,但当前锂电池SOH估计方法多针对特定充电策略,采用确定性估计模型,无法反映电池退化过程中的随机性、模糊性等不确定性信息.为此,提出一种适用于不同充电策略的锂电池SOH区间估计方法.该方法针对不同充电策略的电池循环充放电数据提取多个特征参数,通过交叉验证自动选择针对特定充电策略的最优特征参数组合.另外,考虑到锂电池全生命期循环次数有限,属于小样本问题,提出集成支持向量回归与分位数回归优势的支持向量分位数回归模型(SVQR)进行锂电池SOH区间估计.选用放电程度较深的锂电池充放电循环数据作为训练集,对SVQR模型进行离线训练,训练好的模型用于不同充电策略下锂电池SOH在线估计.采用具有不同充电策略的数据集验证所提方法,实验结果表明:所提方法适用于不同充电策略,且估计结果优于分位数回归法、分位数回归神经网络法和高斯过程回归法.

关键词: 锂离子电池, 健康状态, 区间估计, 充电策略, 支持向量分位数回归

Abstract:

State of health (SOH) estimation of lithium-ion (Li-ion) batteries is of great importance for battery use, maintenance, management, and economic evaluation. However, the current SOH estimation methods for Li-ion batteries are mainly targeted at specific charging strategies by using deterministic estimation models, which cannot reflect uncertain information such as randomness and fuzziness in the battery degradation process. To this end, a method for estimating the SOH interval of Li-ion batteries applicable to different charging strategies is proposed, which extracts multiple feature parameters from the cyclic charging and discharging data of batteries with different charging strategies, and automatically selects the optimal combination of feature parameters for a specific charging strategy by using the cross-validation method. In addition, considering the limited number of cycles in the whole life cycle of Li-ion batteries as a small sample, support vector quantile regression (SVQR), which integrates the advantages of support vector regression and quantile regression, is proposed for the estimation of SOH interval of lithium-ion batteries. Li-ion battery charge/discharge cycle data with deep discharge degree is selected as the training set for offline training of the SVQR model, and the trained model is used for online estimation of the SOH of Li-ion batteries of different charging strategies. The proposed method is validated using three datasets with different charging strategies. The experimental results show that the proposed method is applicable to different charging strategies and the estimation results are better than those of quantile regression, quantile regression neural network and Gaussian process regression.

Key words: lithium-ion battery, state of health (SOH), interval estimation, charging strategy, support vector quantile regression (SVQR)

中图分类号:

TM912

张孝远, 张金浩, 杨立新. 考虑不同充电策略的锂电池健康状态区间估计[J]. 上海交通大学学报, 2024, 58(3): 273-284.

ZHANG Xiaoyuan, ZHANG Jinhao, YANG Lixin. Interval Estimation of State of Health for Lithium Batteries Considering Different Charging Strategies[J]. Journal of Shanghai Jiao Tong University, 2024, 58(3): 273-284.

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数据集	充电协议	标称电压/V	标称容量/(mA·h)
CALCE	CC-CV	4.2	1100
Oxford	CC	3.7	740
TRI	2-step	3.3	1100

数据集数据划分	CALCE		Oxford		TRI
数据集数据划分	训练集	测试集	训练集	测试集	训练集	测试集
电池标号	CS36	CS35; CS37; CS38	Cell1	Cell3; Cell7; Cell8	b0c31	b0c32; b0c34; b0c35
循环次数	791	716; 857; 849	78	76; 77; 76	839	683; 683; 640

单体电池	方法	AIS	MPICD	MAPE
CS35	QR	-0.1472	1.679	12.39
	QRNN	-0.1499	3.792	11.05
	SVQR	-0.0582	0.97	6.62
	GPR	-0.0524	3.109	16.32
CS37	QR	-0.1211	1.816	12.07
	QRNN	-0.1262	1.575	10.78
	SVQR	-0.0513	0.707	5.46
	GPR	-0.0416	0.959	14.57
CS38	QR	-0.1256	4.773	12.39
	QRNN	-0.1285	2.773	11.03
	SVQR	-0.0868	1.64	7.29
	GPR	-0.0567	2.232	18.1

单体电池	方法	AIS	MPICD	MAPE
Cell3	QR	-1.946	1.91	105.36
	QRNN	-1.614	1.68	87.19
	SVQR	-0.43	1.34	19.892
	GPR	-0.698	2.49	32.63
Cell7	QR	-1.904	11.19	104.17
	QRNN	-1.567	3.96	84.75
	SVQR	-0.402	3.75	18.99
	GPR	-0.651	2.12	32.48
Cell8	QR	-1.929	2.709	105.35
	QRNN	-1.611	2.57	87.51
	SVQR	-0.4	0.322	20.54
	GPR	-0.705	3.218	32.56

单体电池	方法	AIS	MPICD	MAPE
b0c32	QR	-0.0312	1.934	12.39
	QRNN	-0.0283	1.2	11.05
	SVQR	-0.0234	1.811	6.62
	GPR	-0.0411	0.035	16.326
b0c34	QR	-0.0304	2.274	12.07
	QRNN	-0.0273	1.51	10.78
	SVQR	-0.0198	2.67	5.46
	GPR	-0.0368	0.416	14.57
b0c35	QR	-0.0362	3.49	12.39
	QRNN	-0.034	2.561	11.03
	SVQR	-0.0331	3.972	7.29
	GPR	-0.0508	1.13	18.1