Parameter optimization of plug-in hybrid electric vehicle based on quantum genetic algorithm

Abstract

The parameters of the power system and the control strategy parameters are coupled,they work together to affect the power, economy and emission of the plug-in hybrid electric vehicle. Firstly, the real-time optimal control strategy based on the optimum of the vehicle power system’s efficiency is developed. Secondly, the cost of the power system and indicators of vehicle’s power performance are taken as constraints, the fuel consumption and emissions considering the engine’s cold-heat effect are taken as the optimization objective, and in order to obtain the optimum solution of the power system’s parameters and control strategy parameters, the standard genetic algorithm (SGA) and the quantum genetic algorithm (QGA) are applied to coordinated optimize them respectively. Finally, the simulation model of the vehicle system is established. After simulation, the simulation results show that compared with the original vehicle, the fuel economy and emission performance of the vehicle which parameters optimized by SGA and QGA has greatly improved, compared to the parameters optimization by SGA, the optimization by QGA can suppress premature convergence and have better fuel economy and emission performance.