Abstract:  In view of the lack of speed prediction accuracy in the predictive energy management strategy of hybrid electric vehicles, the fuel economy is reduced, a speed prediction method based on wavelet decomposition (WD) and dual channel convolutional neural network (CNN) is proposed to improve the speed prediction accuracy. Firstly, wavelet decomposition is used to decompose the original vehicle speed sequence into multiple components to reduce the nonstationarity of the original vehicle speed sequence; Secondly, each component is sent to two parallel convolutional neural networks for feature extraction, and then sent to long short-term memory neural network (LSTM) for prediction after feature fusion; Then, the final speed prediction result is obtained by superimposing the prediction results of each component. Finally, based on the results of vehicle speed prediction, the energy management strategy based on model predictive control is established to optimize the power source output in the prediction time domain. The simulation results show that the prediction accuracy of the speed prediction method proposed in this paper is 58.96% higher than that of the CNN-LSTM network model under CLTCP conditions. The fuel consumption of the predictive control strategy proposed in this paper is 13.3% higher than that of the dynamic programming strategy, but the fuel consumption is 18.98% lower than that of the rule-based strategy, which verifies the effectiveness of the speed prediction method and the predictive energy management strategy.