Battery state of charge estimation using temporal convolutional network based on electric vehicles operating data

Abstract

Accurate estimation of state of charge (SOC) is crucial for battery management system (BMS). Since most of the existing estimation methods are based on laboratory data, the accuracy of battery SOC estimation in real driving scenarios is difficult to be guaranteed. This paper proposes a method to estimate battery SOC using temporal convolutional network based on real-world operating data of electric vehicles (EVs). In view of the characteristics of large volume and high dimensionality of real-world EVs data, attention mechanisms are added to the time steps and input feature dimensions of the data, respectively, to further improve the model estimation accuracy. Meanwhile, this paper proposes a new method to correct the SOC value provided by the cloud platform through developing an improved capacity decay model applicable to the real-world EVs data. Then the corrected SOC is used as the label for neural network training. Experiments are conducted using one-year EVs data as the testing dataset, and the results show that the proposed model performances well, with root mean square error (RMSE) <0.67 % in spring and 0.99 % in winter. In comparison with other neural network models commonly used to predict battery SOC, the results show that the proposed model predicts more stable and accurate results.