基于Gauss过程分数位回归模型的锂电池SOH估计

doi:10.3969/j.issn.1674-8484.2024.06.010

摘要/Abstract

摘要：

为了提升锂电池使用安全性，延缓其寿命退化，提出了一种Gauss过程分位数回归模型的锂电池健康状态（SOH）方法。该模型结合了准Gauss过程回归与非平稳时间序列分析以及分位数回归的优点，可有效处理健康特征数据非线性及时变性问题，具有模型参数的自适应调节能力，从而提升SOH估计的精确性和鲁棒性；通过美国国家航空航天局的电池数据集，基于不同温度的数据验证了所提出模型的有效性。结果表明：该模型的SOH估计结果的平均绝对误差（MAE）、均方根误差（RMSE）和平均绝对百分比误差（MAPE）分别为0.002 8、0.003 8、0.003 4，模型运行时间为0.008 1 s，与灰狼优化Guass过程回归模型以及文献中3种典型模型估计对比，其RMSE结果精度分别提高了0.019 9、0.003 0、0.019 6、0.002 0，证明所提出模型具有较好的鲁棒性和估计结果的高精确性。

关键词: 锂离子电池, Gauss过程分位数回归, 健康状态（SOH）, 非平稳特征, 参数调整

Abstract:

To improve the safety of lithium batteries and delay their life degradation, a Gaussian process quantile regression model was proposed for estimating the state of health (SOH) of lithium batteries. The model combined the advantages of quasi-Gaussian process regression with non-stationary time series analysis and quantile regression, which can effectively deal with the nonlinear and time-varying problem of health characteristic data, and had the ability of adaptive adjustment of model parameters, thus improving the accuracy and robustness of SOH estimation. The validity of the proposed model was verified by the NASA battery dataset based on different temperatures. The results show that the mean absolute error (MAE)、root mean square error (RMSE) and mean absolute percentage error (MAPE) of the SOH estimation results of the model are 0.002 8, 0.003 8, and 0.003 4, respectively, and the model runtime is 0.008 1 s. Comparing the results with the Gray Wolf Optimization Guassian process regression model and three typical model estimation in the literature, the accuracy of RMSE is improved by 0.019 9, 0.003 0, 0.019 6, and 0.002 0, respectively, proving that the proposed model is more robust and has high accuracy in estimation results.

Key words: lithium-ion battery, Gaussian process quantile regression, state of health (SOH), non-stationary features, parameter tuning

中图分类号:

TM921.0

张金秀, 闫彩红, 任桂周. 基于Gauss过程分数位回归模型的锂电池SOH估计[J]. 汽车安全与节能学报, 2024, 15(6): 886-894.

ZHANG Jinxiu, YAN Caihong, REN Guizhou. Estimation on state of health of lithium battery based on Gaussian process quantile regression model[J]. Journal of Automotive Safety and Energy, 2024, 15(6): 886-894.

图/表 12

参考文献 25

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健康特征	r	ρ	τ
H1	0.958 3	0.974 7	0.877 6
H2	0.982 0	0.983 0	0.898 7
H3	-0.918 8	-0.917 4	-0.742 4
H4	0.996 6	0.996 0	0.957 4

健康特征	r	ρ	τ
H1	0.958 3	0.974 7	0.877 6
H2	0.982 0	0.983 0	0.898 7
H3	-0.918 8	-0.917 4	-0.742 4
H4	0.996 6	0.996 0	0.957 4

模型	MAE	RMSE	MAPE	t / s
GPRQ训练集	0.003 6	0.005 8	0.004 7	0.008 1
GPRQ测试集	0.002 8	0.003 8	0.003 4	0.008 1
GWO-GPR测试集	0.005 4	0.006 7	0.006 7	0.008 8

模型	MAE	RMSE	MAPE	t / s
GPRQ训练集	0.003 6	0.005 8	0.004 7	0.008 1
GPRQ测试集	0.002 8	0.003 8	0.003 4	0.008 1
GWO-GPR测试集	0.005 4	0.006 7	0.006 7	0.008 8

电池	GPRQ	CSVGPR^[20]	IGPR^[21]	ALO-GPR^[24]	GWO-GPR^[25]
B0005	0.006 5	0.007 8	0.012 7	0.009 3	0.006 7
B0007	0.004 3	0.023 9	0.024 2	0.006 3	0.007 3