An ensemble learning velocity prediction-based energy management strategy for a plug-in hybrid electric vehicle considering driving pattern adaptive reference SOC

The fuel economy of a plug-in hybrid electric vehicle is largely dependent on the battery energy usage during various driving cycles. In this research, within the model predictive control (MPC) principle, an Ensemble Learning Velocity Prediction (ELVP)-based energy management strategy (EMS) considering the driving pattern Adaptive Reference State of Charge (AR-SOC) is proposed. Firstly, the existing methods including Markov chain (MC), back propagation (BP) and radial basis function (RBF) neural network (NN)-based velocity prediction models are described. Then, these models are embedded into MPC-based EMS respectively, and the validation results show that the NN performs better than the MC by comparing the prediction precision, computational cost, and resultant vehicular fuel economy. By incorporating these prior knowledges, a novel ensemble learning velocity prediction method is established by blending BP-NN and RBF-NN. Subsequently, based on the expected trip distance and the velocity prediction results, an adaptive reference SOC (AR-SOC) trajectory planning method is developed to direct the distribution of battery energy for different driving patterns. Combining with the ELVP and the AR-SOC, the MPC-based EMS derives the optimal torque-distribution decisions. Finally, the validation results indicate that the proposed strategy achieves superior fuel economy under various driving cycle compared with the benchmark strategies.