关键词: 纯电动汽车；能耗预测；线性回归；神经网络

Abstract: The driving range of electric vehicles is lower than traditional fuel vehicles at the same level price. The driving range problem seriously limits the promotion of electric vehicles in China. Therefore, predicting the energy consumption of electric vehicles can estimate the driving range accurately, reduce drivers' mileage anxiety and enhance consumers' purchase intention. Based on the real vehicle data collected by the electric vehicle monitoring platform, this paper proposes an energy consumption prediction method of multiple linear regression and radial basis function neural network. Firstly, the energy flow direction of electric vehicle is systematically analyzed, which mainly includes three parts: driving resistance energy consumption, braking recovered energy and accessory energy consumption. Based on the analysis of energy flow direction and considering the influence of temperature on the energy consumption, the data collected by the real vehicle are used to identify the parameters of the regression model, and the multiple linear regression model for energy consumption prediction is established. Compared with the actual value,the nonlinear error is obtained. Finally, the RBF neural network is used to fit the nonlinear residual of the regression model. The results shows that the model can improve the prediction accuracy of single model, and it is of practical significance in estimating the driving range of electric vehicles.

Key words: Electric vehicle; Energy consumption prediction; Linear regression; RBF neural network