Optimal Eco-driving Control for Plug-in Hybrid Electric Vehicles Based on Neural Network

With the development of intelligent and connected vehicles, a novel approach for energy-saving, i.e. eco-driving control, has attracted much attention from relative researchers. The combination of eco-driving control and plug-in hybrid electric vehicles provide an opportunity to achieve further energy-saving for transportation. In this paper, an optimal eco-driving control strategy is proposed for plug-in hybrid electric vehicles based on the neural network. In order to mitigate the huge computational cost of velocity optimization and powertrain control, an efficient hierarchical optimal control strategy is proposed. An artificial neural network is constructed for the modeling of optimal energy cost. This optimal energy cost model is applied as objective function in the solving of the optimal eco-driving problem. The simulation results show that the proposed method can improve fuel economy by 4.29-12.71%, compared with conventional eco-driving control strategy. The neural network based optimal energy cost model significantly heightens the computational efficiency, with small sacrifice for fuel economy compared to optimal bench mark.