Multi-objective optimization of the suspension parameters in the in-wheel electric vehicle

Article type: Research Article

Authors: Li, Ruihua

Affiliations: College of Electrical and Mechanical Engineering, Xuchang University, Xuchang, Henan 461000, China | E-mail: raiderlee@163.com

Correspondence: [*] Corresponding author: College of Electrical and Mechanical Engineering, Xuchang University, Xuchang, Henan 461000, China. E-mail: raiderlee@163.com.

Abstract: The hub motor significantly increases the unsprung mass of electric in-wheel vehicles, which deteriorates the ride comfort and safety of vehicles and which can be effectively improved by optimizing the main suspension parameters of vehicles reasonably, so a multi-objective optimization method of main suspension parameters based on adaptive particle swarm algorithm is proposed and the dynamic model of a half in-wheel electric vehicle is established. Taking the stiffness coefficient of the suspension damping spring and damping coefficient of the damper as independent variables, the vertical acceleration of the body, the pitch acceleration and the vertical impact force of the hub motor as optimization variables, and the dynamic deflection of the suspension and the dynamic load of the wheel as constraint variables, the multi-objective optimization function is constructed, and the parameters are simulated and optimized under the compound pavement. The simulation results show that the vertical acceleration and pitch acceleration are reduced by 20.2% and 18.4% respectively, the vertical impact force of the front hub motor is reduced by 3.7%, and the ride comfort and safety are significantly improved.

Keywords: In-wheel electric vehicle, suspension, multi-objective optimization, adaptive particle swarm algorithm

DOI: 10.3233/JCM-204821

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 21, no. 4, pp. 1013-1020, 2021