An adaptive receding horizon-based flexible mode switching control strategy of parallel hybrid electric vehicles

Abstract

Mode switching is crucial to efficient drive in variable driving conditions for hybrid electric vehicles (HEVs), thus many scholars are attracted to implement its high-quality control. The receding horizon control method is an attractive method to solve this problem. However, the choice of prediction horizon is arduous due to the contradiction between the optimization performance and the computational cost. Motivated by this issue, this paper proposes an adaptive receding horizon control (RHC) method, which contains optimization layer (OPL) and feedback control layer (FCL). First, an OPL adaptive receding horizon control method is proposed to obtain the optimal control command, in which a novel adaptive factor, considering the proportional between the maximum traction motor torque and powertrain demand torque, is proposed to adjust the prediction horizon length and control constraints. Then, regarding OPL output as the reference trajectory, a lower-RHC method is designed in FCL to track the reference trajectory. Finally, the proposed control method is validated in simulation and bench experiment. Compared with the existing method, the clutch trajectory is optimized by RHC with adaptive prediction horizon and constrains, and the anti-disturbance ability is enhanced by the lower-RHC. The simulation and bench experiment results indicate that the 0–40 km/h acceleration time and jerk are reduced using the proposed method, respectively.