基于核函数和超参数优化的退役锂电池健康状态估计

doi:10.19799/j.cnki.2095-4239.2023.0918

摘要/Abstract

摘要：

退役锂电池的健康状态(SOH)估计对于电池再利用和环境可持续性至关重要，考虑到电池退役前使用条件的不确定性，为进一步实现数据驱动方法对退役锂电池SOH的精确估计，本研究提出一种改进高斯过程回归(GPR)模型的SOH估计方法。首先，收集退役锂电池的循环充放电数据，在考虑温度影响的同时，使用容量增量分析(ICA)和电化学阻抗谱(EIS)等方法，获取统计健康特征来表征退役锂电池的老化特性，并使用Pearson相关系数对所选统计特征进行相关性分析，筛选出与SOH相关性高的健康特征，消除特征冗余性。然后，基于单一核函数学习老化特征能力有限和传统超参数寻优方法效率不足的特点，将线性核函数和对角平方指数核函数结合，以更好地适应电池SOH估计任务中的多样性，同时，使用鲸鱼算法(WOA)对估计模型的超参数进行优化，以确保最佳拟合效果，建立改进的GPR估计模型以提高估计的精确性。最后，采用NASA电池数据集中具有不同初始健康状况的四个不同电池，来验证所提出方法的有效性，结果表明，本文所提方法可以提供准确的SOH估计，其中平均绝对误差均小于1.75%且均方根误差均小于2.42%。

关键词: 退役锂电池, 健康状态, 鲸鱼算法, 核函数, 高斯过程回归

Abstract:

Given the uncertainty surrounding preretirement battery conditions, this study aims to advance the data-driven approach for accurately estimating retired lithium batteries' state of health (SOH). To achieve this, an enhanced SOH estimation method based on the Gaussian process regression (GPR) model is proposed. Initially, cyclic charge and discharge data from retired lithium batteries are gathered, and statistical health characteristics are derived to depict the aging properties. Methods such as capacity increment analysis (ICA) and electrochemical impedance spectroscopy (EIS) are employed to consider temperature effects. The Pearson correlation coefficient was used to assess the correlation between selected statistical features and health characteristics, identifying those highly correlated with SOH to eliminate the feature redundancy. Subsequently, acknowledging the limitations of individual kernel functions and conventional hyperparameter optimization techniques, a hybrid approach combining linear and diagonal square exponential kernel functions is introduced to better accommodate the diverse nature of battery SOH estimation tasks. The whale optimization algorithm (WOA) is then applied to optimize the hyperparameters of the estimation model, ensuring optimal fitting. This leads to the establishment of an improved GPR estimation model to enhance estimation accuracy. Finally, the effectiveness of the proposed method was validated using four different cells with varied initial health conditions from the NASA battery dataset. Results demonstrate the method's capability to provide accurate SOH estimation, with a mean absolute error below 1.75% and a root mean square error below 2.42%.

Key words: retired lithium-ion batteries, state of health, whale optimization algorithm, kernel functions, Gaussian process regression

中图分类号:

TM 912

李臣, 张会林, 张建平. 基于核函数和超参数优化的退役锂电池健康状态估计[J]. 储能科学与技术, 2024, 13(6): 2010-2021.

Chen LI, Huilin ZHANG, Jianping ZHANG. Estimated state of health for retired lithium batteries using kernel function and hyperparameter optimization[J]. Energy Storage Science and Technology, 2024, 13(6): 2010-2021.

图/表 16

表1

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

表2

图11

表3

四种不同核函数的SOH估计的统计误差"

序号	核函数	估计误差/%
		NO.5		NO.6		NO.7		NO.18
		MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE
1	$k L I N i s o (x, x')$	1.6	1.9	0.51	0.68	1.67	2.04	0.52	0.64
2	$k S E i s o (x, x')$	7.32	10.24	3.69	4.75	10.95	15.09	0.93	1.32
3	$k S E i s o_S E i s o (x, x')$	7.32	10.24	3.69	4.75	10.95	15.09	0.93	1.32
4	$k L I N i s o_S E i s o (x, x')$	2.82	3.79	0.33	0.48	1.53	1.96	0.3	0.42

表3

图12

图13

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实验参数	B0005	B0006	B0007	B0018
额定容量/Ah	2	2	2	2
额定电压/V	3.7	3.7	3.7	3.7
电压上/下限/V	4.2/2.7	4.2/2.5	4.2/2.2	4.2/2.5
充/放电电流/A	1.5/2	1.5/2	1.5/2	1.5/2
温度/℃	24	24	24	24

电池编号	训练样本数	选取区间 (循环次数)	测试样本数	选取区间 (循环次数)
B0005	50	75～124	37	125～161
B0006	46	63～108	31	109～139
B0007	40	86～125	43	126～168
B0018	52	45～96	36	97～132

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