| Summary: | The accurate estimation of battery state of charge (SOC) for modern electric vehicles is crucial for the range and performance of electric vehicles. This paper focuses on the historical driving data of electric buses and focuses on the extraction of driving condition feature parameters and data preprocessing. By selecting relevant parameters, a set of characteristic parameters for specific driving conditions is established, a process of constructing a battery SOC prediction model based on a Long short-term memory (LSTM) network is proposed, and different hyperparameters of the model are identified and adjusted to improve the accuracy of the prediction results. The results show that the prediction results can reach 1.9875% Root Mean Square Error (RMSE) and 1.7573% Mean Absolute Error (MAE) after choosing appropriate hyperparameters; this approach is expected to improve the performance of battery management systems and battery utilization efficiency in the field of electric vehicles.
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