Co-optimization of speed planning and energy management for intelligent fuel cell hybrid vehicle considering complex traffic conditions

Fuel cell hybrid vehicles (FCHVs) offer great opportunities to reduce vehicle's operation cost and mitigate environmental impact. However, high-quality real-time energy management of FCHV is a difficult task due to different influences from complex traffic environments, such as dynamic changes of preceding and rear vehicle state, road slope and road coefficient. To address this problem, a cooperative control strategy is designed to achieve simultaneous speed planning and energy management promotion in this study. The upper control layer leverages a gradient-based model prediction control (GRAMPC) based on the fast projection gradient method to plan the speed sequence according to the information of future driving conditions as well as the real-time state of the preceding and rear vehicles. The bottom layer applies model prediction control (MPC) to achieve real-time preferable energy allocation, and a multi objective control function is considered for the total cost minimization of energy management in terms of hydrogen consumption and battery life extension. The simulation results reveal that under the constraints of a dynamic environment, the proposed control strategy in planning state can reduce the traction power, hydrogen consumption, global cost, and battery degradation by 2.65%, 1.9%, 2.39%, and 8.14%, respectively.(c) 2022 Elsevier Ltd. All rights reserved.