Multi-objective Parameters Optimization for HEV Based on improved Particle Swarm Algorithm

• Published in: E3S web of conferences Vol. 118; p. 2005
• Main Authors: Ai, Ying, Gao, Yuanjie, Liu, dongsheng
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 01.01.2019
• Subjects: Adaptive control; Algorithms; Computer simulation; Controllers; Electric vehicles; Emissions; Energy; Energy efficiency; Energy management; Exhaust emission; Fuel consumption; Fuel economy; Fuzzy control; Hybrid electric vehicles; Machine learning; Mathematical functions; Multiple objective analysis; Optimization; Parameters; Product design; State of charge; Torque
• ISSN: 2267-1242; 2555-0403; 2267-1242
• DOI: 10.1051/e3sconf/201911802005

Hybrid electric vehicle fuel consumption and emissions are closely related to its energy management strategy. A fuzzy controller of energy management using vehicle torque request and battery state of charge (SOC) as inputs, engine torque as output is designed in this paper foe parallel hybrid electric vehicle. And a multi-objective mathematical function which purpose on maximize fuel economy and minimize emissions is also established, in order to improve the adaptive ability and the control precision of basic fuzzy controller, this paper proposed an improved particle swarm algorithm that based on dynamic learning factor and adaptive inertia weight to optimize the control parameters. Simulation results based on ADVISOR software platform show that the optimized energy management strategy has a better distribution of engine and motor torque, which helps to improved the vehicle’s fuel economy and exhaust emission performance.