Research on a multi-objective hierarchical prediction energy management strategy for range extended fuel cell vehicles

Abstract

In this paper, a multi-objective hierarchical prediction energy management strategy is proposed to achieve optimal fuel cell life economy and energy consumption economy for a range extended fuel cell vehicle. First, a global state of charge rapid planning method is proposed based only on the expected driving distance. Then, the vehicle speed information in the prediction horizon is estimated by a vehicle speed prediction module based on the back propagation neural network. According to the predicted speed and state of charge reference, a novel fusion algorithm that combines the direct configuration method and sequential quadratic programming is proposed to achieve optimal fuel cell life economy and energy consumption economy in the prediction horizon. Simulation results validate that the proposed strategy can effectively reduce the operating costs compared with that of the charge depletion-charge sustaining strategy and the equivalent consumption minimization strategy, thereby proving the feasibility of the proposed strategy.