Control Strategy of Power Intelligent System of Electric Vehicle Based on Improved Particle Swarm Optimization

HEV (Hybrid Electric Vehicle) control strategy can control the engine, motor and other systems to work in the best state on the premise of meeting the road conditions, so that the overall efficiency of the whole system is the highest. The method of setting weight coefficient has its inherent limitations in dealing with multi-objective and conflicting optimization problems, so the control performance of vehicle power intelligent system will determine the performance of vehicle. In this paper, the control strategy of power intelligent system of electric vehicle based on improved PSO (Particle swarm optimization) is studied. The advantages of PSO and SA (Simulated Annealing) are used to organically combine the two algorithms, that is, SA mechanism is added in the process of updating the particle position and velocity of PSO algorithm, the difference of particle fitness before and after updating is calculated, and the search result is accepted by Metropolis criterion probability, which makes the PSO algorithm appear jumping, and the SA-PSO algorithm is obtained. The results show that under the same cycle condition, the fuel consumption is reduced by 2.53%, the exhaust emission HC is reduced by 7.S9%, the CO is reduced by 7.05% and the NOx is reduced by 9.37%. The fuzzy neural network energy control strategy has obvious effect on reducing fuel consumption and exhaust emission, and has very high reference value for further optimizing the parallel HEV energy control system.