Abstract: Accurately predicting the energy consumption of electric vehicles is essential for efficient vehicle path planning and charging. A multi-model fusion method for energy consumption prediction that takes into account charging behavior is proposed. Firstly, based on the limited parameters and sparse real vehicle data, the energy consumption calculation model is constructed. Then, the charging behavior model is created to analyze and extract features closely related to energy consumption. Finally, long short-term memory neural network(LSTM) is used to construct the energy consumption prediction model. The method is validated with real vehicle data. Results indicate that the proposed method accurately predicts the energy consumption for the given car model with differing starting battery states of charge(SOC), temperatures, and periods. The root mean square error(RMSE) recorded is 1.27, which shows a reduction of no less than 4.5% compared to the existing methods.