Estimated state of health for retired lithium batteries using kernel function and hyperparameter optimization

doi:10.19799/j.cnki.2095-4239.2023.0918

Abstract

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

CLC Number:

TM 912

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.

Figures/Tables 16

Table 1

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Table 2

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Table 3

The statistical errors of SOH estimation for four different kernel functions"

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

Table 3

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Fig. 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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