Study On Simulation Of The Control Strategy For Extended-range Electric Vehicle Based On Genetic Algorithm

As a means of transportation that satisfies everyday driving range, savesenergy and reduces carbon emission, extend range electric vehicle (EREV) iscurrently the mainstream development object in the automobile industry all overthe world. This paper studies the improvement of EREV power performance andfuel economical efficiency, which is of great significance to solve environmentalpollution, exhausted energy and development of electric vehicle.In accordance with EREV working mode and function characteristics and onthe basis of EREV control strategy and vehicle energy flow analysis, this paperstudies how the range extender works and influences vehicle performance, andputs forward with extender design based on genetic algorithm optimizationdesign requirements and advisor simulation software.With the focus on power battery, the paper analyzes SOC battery packcharge and discharge strategy, energy feedback braking control strategy andextender energy control strategy, optimizes the design of extender engine poweroutput and torque as well as generator torque by way of genetic algorithm.Models are selected and parameters are matched for key components based on alarge number of data analysis. With genetic algorithm combined with advisorsimulation software, the paper studies the application scope and realizationmethod of genetic algorithm, designs genetic algorithm main program andcompletes the design in accordance with the genetic algorithm optimization steps.EREV model is established in advisor for UDDC condition. Simulationresearch is also carried out on the maximum speed, acceleration performance,climbing ability, fuel economic efficiency and emission through advisor’s vehicleperformance testing function before the simulation results are comparatively analyzed and verified.