Powertrain parameters and control strategy optimization of a novel master-slave electric-hydraulic hybrid vehicle

Abstract

ABSTRACT The electric-hydraulic hybrid vehicle (EHHV) is an important research area of hybrid electric vehicles (HEV), which provides a competitive project compared to other hybrid technologies. This paper conducts comprehensive research on a master-slave electric-hydraulic hybrid vehicle (MSEHHV). After an integrated driving cycle, the battery state of charge (SOC) values for MSEHHV and electric vehicle (EV) are 44.65% and 38.27%. The economy of the MSEHHV is verified, which is obviously superior to the EV. To further explore the energy conservation potential of the MSEHHV, the research proposes a cooperative optimization method of powertrain parameters and control strategy. Specifically, the optimization objective is to improve SOC. The response surface method (RSM) fits the functional relation between design variables and optimization objective. An optimization model is constructed based on the response surface model. Ultimately, the particle swarm optimization (PSO) algorithm is used for the optimal solution to obtain the optimal parameter combination. To evaluate the adaptability of the method, the performance of three models in the actual driving cycle is compared. Simulation results suggest that the energy consumption of the optimized MSEHHV is 33.41% and 6.33% lower than that of EV and initial MSEHHV. The research provides a valuable reference for the optimal design of electric-hydraulic hybrid technology.