基于弛豫过程特征提取的锂离子电池健康状态估计

doi:10.19799/j.cnki.2095-4239.2023.0510

摘要/Abstract

摘要：

锂离子电池是当前固定式电化学储能的重要方式，电池健康状态(state of health，SOH)估计对于锂电池安全稳定运行具有重要意义。目前，健康特征的提取集中在电池的充电阶段，对静置阶段，即弛豫阶段提取健康特征的方法较少。本文基于电池充放电后弛豫阶段曲线，提出了一种从弛豫阶段提取健康特征并结合高斯过程回归进行SOH估计的方法。首先，根据三元离子电池的加速循环老化测试数据，分析了弛豫阶段时间常数的变化规律，采用了幂函数进行建模，较好地反映了电池端电压在弛豫阶段的变化。其次，提取了能够表征弛豫阶段的关键特征，结合高斯过程回归建立了电池SOH估计模型。最后，在不同老化电流倍率的电池上进行了精度验证，比较了采集15分钟和采集60分钟弛豫曲线时的误差结果，也比较了高斯过程回归方法相较于支持向量机与树回归方法的精度，并在多个荷电状态下(state of charge，SOC)验证了SOH估计精度。研究结果表明，所提出的SOH估计模型，在验证上其均方根误差最优可达到0.6%，在采用15分钟数据进行SOH估计时，均方根误差仍能小于1%，有着良好的估计效果。

关键词: 动力锂离子电池, 健康状态估计, 弛豫模型, 高斯过程回归, 健康特征

Abstract:

Lithium-ion battery is an important part of the fixed electrochemical energy storage, and the estimation of its state of health (SOH) is of great significance for its safe and stable operation. At present, health-feature extraction is focused on the charging stage of a battery, and few methods have been developed to extract health features at the relaxation stage. This study proposes a method to extract health features from the relaxation stage and use Gaussian process regression (GPR) to estimate the SOH. First, based on the accelerated cycle aging test data of Li-NMC batteries, the variation of the time constant at the relaxation stage is analyzed, and a power function, which reflects the terminal voltage in the relaxation stage well, is used for modeling. Second, the key features that can characterize the relaxation stage are extracted, and the SOH estimation model is established based on GPR. Finally, the accuracy was verified using batteries with different aging current multiplicities. The results are compared when the model is generated using 15- and 60-minute relaxation-stage curves. The accuracy of the Gaussian-process regression method is also compared with support vector machine and tree regression method, and the accuracy of SOH estimation was verified under multiple states of charge. The proposed SOH estimation model was validated to achieve an optimal root mean square error (RMSE) of 0.6%; the RMSE is still less than 1% when using 15-minute data for SOH estimation.

Key words: lithium-ion battery, SOH estimation, relaxation stage, gaussian process regression, feature extraction

中图分类号:

TM 912

耿陈, 孟锦豪, 彭乔, 刘天琪, 曾雪洋, 陈刚. 基于弛豫过程特征提取的锂离子电池健康状态估计[J]. 储能科学与技术, 2023, 12(11): 3479-3487.

Chen GENG, Jinhao MENG, Qiao PENG, Tianqi LIU, Xueyang ZENG, Gang CHEN. Estimation of the state of health of lithium-ion batteries based on feature extraction of the relaxation process[J]. Energy Storage Science and Technology, 2023, 12(11): 3479-3487.

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电池名称	充电电流
Cell1和Cell2	1 A
Cell3和Cell4	2 A
Cell5和Cell6	4 A

算法	回归树方法		SVM回归方法		GPR回归方法
数据长度	60 min	15 min	60 min	15 min	60 min	15 min
RMSE	0.0190	0.0246	0.0154	0.0127	0.0064	0.009
R²	0.91	0.85	0.94	0.96	0.99	0.98
MAE	0.0119	0.0132	0.0105	0.0080	0.0046	0.0057

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