Short-Term Vehicle Speed Prediction Based on Back Propagation Neural Network

In the face of energy and environmental problems, how to improve the economy of fuel cell vehicles (FCV) effectively and develop intelligent algorithms with higher hydrogen-saving potential are the focus and difficulties of current research. Based on the Toyota Mirai FCV, this paper focuses on the short-term speed prediction algorithm based on the back propagation neural network (BP-NN) and carries out the research on the short-term speed prediction algorithm based on BP-NN. The definition of NN and the basic structure of the neural model are introduced briefly, and the training process of BP-NN is expounded in detail through formula derivation. On this basis, the speed prediction model based on BP-NN is proposed. After that, the parameters of the vehicle speed prediction model, the characteristic parameters of the working condition, and the input and output neurons are selected to determine the topology of the vehicle speed prediction model. And then the training and prediction of the vehicle speed prediction model are completed. Compared with the speed prediction based on radial basis function neural network (RBF-NN) under the selected working conditions, the results show that the root mean square error (RMSE) and mean absolute error (MAE) of speed prediction based on BP-NN are 1.02 km/h and 0.95 km/h, respectively, it illustrates that the speed prediction based on BP-NN has a higher prediction accuracy. The research results lay a foundation for the development of an energy management strategy for FCV combined with short-term vehicle speed prediction.