Trip-Oriented Model Predictive Energy Management Strategy for Plug-in Hybrid Electric Vehicles

Abstract

Energy management strategies play a critical role in performance optimization of plug-in hybrid electric vehicles (PHEVs). In order to attain effective energy distribution of PHEVs, a predictive energy management strategy is proposed in this study based on real-time traffic information. First, an exponentially varied model for the velocity prediction is established, of which the tunable decay coefficient is regulated by the supported vector machine (SVM). In this manner, the prediction precision is improved. Then, by properly simplifying the powertrain model, the state of charge (SOC) reference trajectory is generated based on the fast dynamic programming (DP) with fast calculation speed and consideration of the traffic information. Moreover, the typical DP algorithm is leveraged to solve the nonlinear rolling optimization problem for minimizing the operating cost in a receding horizon. Simulation results demonstrate that the proposed algorithm can reach 92.83% operating savings, compared with that of the traditional DP; and save 6.18% cost compared with the MPC algorithm only with reference of the trip duration.

Keywords

• Plug-in hybrid electric vehicles (PHEVs)
• energy management
• model predictive control (MPC)
• traffic information
• state of charge (SOC) reference