SOH Estimation Method Based on RBF-BLS for Low-Carbon and Safe Travel of Electric Vehicle

doi:10.16183/j.cnki.jsjtu.2023.051

Abstract

Abstract:

The charging safety of electric vehicle (EV) is closely related to the state of health (SOH) in power battery pack. Therefore, the high-performance and real-time estimation of SOH is an important basis for safety detection in the charging process. Power battery is deeply effected by factors such as complex structure, types of battery cell, driving habits, temperature, and charging behavior. Compared to SOH estimation methods based on experimental data from one or few battery cells, research on real-time SOH estimation of the power battery meets with insufficient problems in battery model, data getting, real-time, accuracy, and so on. Aimed at these drawbacks, a high performance SOH estimation method in power battery pack is proposed by introducing the broad learning system(BLS) optimized by radial basis function (RBF) into the empirical battery degradation model based on the idea of multi-method integration and fusion. First, the empirical degradation model and offline historical charging data are used to obtain the initial SOH value. Then, a radial basis function neural network is applied to get the initial weight matrix of the BLS to optimize the BLS method, and establish the RBF-BLS neural network. The estimation error can be trained by the RBF-BLS neural network and real-time charging data, and compensate for the initial SOH to gain a higher precise SOH value. Finally, a computer simulation example based on actual charging data from a charging operation enterprises is used to verify the effectiveness and superiority of the proposed method.

Key words: charging safety, state of health (SOH), empirical degradation model, broad learning system (BLS)

CLC Number:

LI Chunxi, QIAO Hanzhe, YAO Gang, JIANG Haoyu, CUI Xiangke, GE Quanbo. SOH Estimation Method Based on RBF-BLS for Low-Carbon and Safe Travel of Electric Vehicle[J]. Journal of Shanghai Jiao Tong University, 2024, 58(9): 1454-1464.

Figures/Tables 11

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算法	训练时长/s	预测精度/%	决定系数
RBF	0.674 00	93.83	0.957 6
BLS	0.045 25	98.75	0.997 9
RBF-BLS	0.419 50	99.18	0.998 4

算法	MAPE/%	RMSE
经验退化模型	2.709 4	2.589 7
经验退化与BP融合模型	0.963 4	1.036 2
经验退化与RBF-BLS融合模型	0.462 7	0.522 3