Dynamic System Matching Of A Pure Electric Vehicle Based On CRUISE And Parameters Optimization

The world's oil energy is being consumed at a very fast pace,and there are few oil resources that have been proven,and in the future,there may be a terrible energy crisis in the world.The traditional automotive industry will be eliminated.At present,the domestic car ownership is increasing year by year,the traditional automobile exhaust emissions brought about by environmental pollution can't be underestimated,as the current cause of the northern city of the fog haze is one of the culprits of a large number of exhaust emissions.In this situation,the development of new energy vehicles is particularly important and urgent.Electric vehicle is the most promising new energy vehicles.As one of the core technology of electric vehicles,the design of power system parameters is related to the performance of electric vehicle qualityAiming to participate in its dynamic system parameters matching and optimization work is based on a pure electric vehicle(EV01)designed by a company from Shanghai in this paper.According to the parameters of the vehicle and the design objectives of the performance of the vehicle,the paper will make the initial theoretical match designation.Then,the vehicle simulation software AVL CRUISE is used to simulate the vehicle,and the rigs of the test rig are tested.The simulation results are compared with the experimental results.The correctness and accuracy of the vehicle simulation are obtained.Under the premise of ensuring the dynamic performance,the power system parameters are optimized to achieve the optimization process to improve the performance of the vehicle.This paper proposes an algorithm called PSO(particle swarm optimization).Then the optimized parameters are simulated again by CRUISE.The simulation results show that the performance of the vehicle has been improved greatly under the premise of ensuring the dynamic performance.The optimized performance indexes meet the design goal and have improved in varying degrees,which showed the optimization algorithm is rational and accuracy.