Particle swarm optimization and fuzzy control for energy management and torque optimization in a electro-hydromechanical power coupling vehicle

Abstract

Currently, electric vehicles are rapidly advancing. A novel mechatronic and electrohydraulic power transmission system, known as the Induction Asynchronous Mechanical-Electric-Hydraulic Power Coupler (IA-MEHPC), has been introduced. This innovation aims to mitigate maximum motor torque and improve dynamic response during start-stop operations. This system combines an induction motor with a swash plate axial piston pump/motor to achieve the conversion of electrical, mechanical, and hydraulic energy. A rule-based control strategy was established based on energy flow and validated through steady-state simulations, demonstrating the advantages of the Electro-Hydromechanical Power Coupling Vehicle (EHPCV) in energy management. Additionally, a response surface model and particle swarm optimization algorithm were used to optimize four key parameters to maximize battery State of Charge (SOC) and minimize speed error. The proposed fuzzy controller, which uses vehicle speed and accumulator pressure as variables, significantly reduced the peak torque of the motor while improving overall efficiency. The simulation results indicate that the energy consumption rates improved by 3.12%, 4.53%, 1.9%, and 3.11% under the 10-15, FTP-75, WLTC, and real-world conditions, respectively. The optimized strategy proves to be more reasonable, resulting in an improved state of charge.