A chaotic particle swarm optimization algorithm for solving optimal power system problem of electric vehicle

Tianjun Zhu,Zonghao Ma,Hongyan Zheng

doi:10.1177/1687814019833500

Abstract

Transportation of electrification has become a hot issue in recent decades and the large-scale deployment of electric vehicles has yet to be actualized. This article proposes a powertrain parameter optimization design approach based on chaotic particle swarm optimization algorithm. To improve the driving and economy performance of pure electric vehicles, chaotic particle swarm optimization algorithm is adopted in this study to optimize principal parameters of vehicle power system. Vehicle dynamic performance simulations were carried out in the Cruise software, and the simulation results before and after optimization were compared. Simulation results show that optimized vehicles by chaotic particle swarm optimization can meet the expected dynamic performance and the driving range has been greatly improved. Meanwhile, it is also viable that the parameters of the optimal objective function can achieve the purpose of balancing the driving performance and economic performance, which provides a reference for the development of vehicle dynamic performance.

Highlights

Energy crisis is an urgent issue nowadays, and more and more attention is paid to new energy resources
S Chen[11] used particle swarm optimization (PSO) algorithm for pure electric vehicles (EVs) transmission system; the results obtained proved that the vehicle economy and power have been effectively improved
Jiang et al.[15] applied an improved chaotic particle swarm optimization (ICPSO) algorithm to optimize the key parameters of energy management strategy

Summary

Introduction

Energy crisis is an urgent issue nowadays, and more and more attention is paid to new energy resources. S Chen[11] used particle swarm optimization (PSO) algorithm for pure EV transmission system; the results obtained proved that the vehicle economy and power have been effectively improved. Jiang et al.[15] applied an improved chaotic particle swarm optimization (ICPSO) algorithm to optimize the key parameters of energy management strategy. The principal parameters of powertrain (including peak power of motor, battery’s capacity and final drive ratio) are set as the optimized variables by CPSO, with the vehicle performance index (maximum speed, acceleration time, and maximum grade) being defined as the constraint conditions. The whole vehicle model contains a wide range of modules such as battery, gearbox, motor, brake, tire, and MATLAB DLL module is related to the economy of EVs and include strategy of regenerative braking. In the establishment of the constraints, it is necessary to take the dynamic performance and the ratio of the final drive into account, and consider the battery energy requirements decided by driving mileage under constant 60 km/h condition and the battery discharge power under the 55% SOC

Constraint conditions under the maximum climbing degree g3

Findings

Conclusion