Abstract: To resolve the problem of inaccurate prediction-horizon speeds in model predictive energy management algorithms for hybrid electric vehicles, the speed prediction based on machine learning is examined. First, a single-shaft parallel hybrid electric powertrain model equipped with a continuously variable transmission(CVT) is established. Then, the machine learning algorithms are utilized to predict the vehicle velocity in the future time horizons, and root mean squared error(RMSE) values of three different prediction methods are obtained, where the performance of the LSTM-NN is the best, followed by the feedforward neural network, and the support vector machine is the worst. Model predict control(MPC) is subsequently deployed to manage energy flow distributions. The effects of different prediction methods on fuel consumption and SOC are verified, and the effects of the prediction horizon size on the energy management performance are comparatively analyzed. Consequently, the minimum fuel consumption prediction horizon is determined. Finally, the performance comparisons between the predictive controller and traditional Dynamic programming(DP) and equivalent consnmption minimization strategy(ECMS) are made, illustrating that the machine learning driven predictive control is promising for reducing fuel consumption. It also facilitates realizing disturbance quantity prediction in the prediction horizon for model predictive control algorithms.

Key words: parallel hybrid electric vehicles, machine learning, LSTM, predictive energy management, model predictive control