Model predictive control-based energy management strategy with vehicle speed prediction for hybrid electric vehicles

Abstract

The speed of a hybrid electric vehicle is a critical factor that affects its energy management performance. In this study, we focus on the importance of solving the problem of inaccurate speed prediction in the energy management strategy (EMS) and application of dynamic programming (DP) needs to know the entire driving cycle. A gated recurrent unit neural network (GRU-NN) speed predictive model based on machine learning is developed by using the model predictive control (MPC) framework and solved in the prediction domain by employing DP. The neural network is trained on the training set, which is a collection of standard driving cycles. The results are compared with other two types of speed predictive models to verify the effects of different parameters of different speed predictive models on the state of charge and fuel consumption under Urban Dynamometer Driving Schedule driving cycle. Simulation shows that MPC based on the GRU-NN speed predictive model can effectively improve the fuel economy of hybrid electric vehicles, with a 94.14% fuel economy, which proves its application potential. Finally, the GRU-NN speed predictive model is applied under the Real-World Driving Cycle, whose fuel consumption has a fuel economy of 91.95% compared with that of the original rule-based EMS.