Transformer-Based Prediction of Charging Time for Pure Electric Vehicles

The arrangement of charging time for pure electric vehicles is a crucial part of the daily life of car owners，directly affecting the convenience and comfortable experience of their travel. However，there are still challenges such as insufficient charging station resources and the need for advanced planning for charging. To solve the problem of car owners being unable to use the vehicle immediately due to insufficient battery，a charging time prediction solution based on the Transformer model is proposed to help car owners better plan their daily itinerary. In order to better understand the degree of battery performance degradation and capacity loss，the capacity method is used to evaluate the health status of batteries，and the charging behavior of drivers is analyzed to construct the characteristics of battery charging behavior. Savitzky Golay filter is used to smooth out the features representing battery attenuation and perform cumulative transformation，so that the features can more comprehensively represent battery information. Then the Pearson correlation coefficient and LASSO（Least Absolute Shrinkage and Selection Operator）regression algorithm are coupled to obtain the optimal feature set through secondary screening. Finally，using the Transformer model's strong attention mechanism，the charging time is predicted. Through experimental data verification，this scheme can accurately and quickly predict the charging time of pure electric vehicles，with a determination coefficient of 0.999 and a running speed of 156 ms. © 2024 SAE-China. All rights reserved.