A comparative study on the optimal non-linear seat and suspension design for an electric vehicle using different population-based optimisation algorithms

Abstract

This paper presents a comparative study on the optimal non-linear seat and suspension design for an in-wheel-motor driven electric car using half vehicle model (HVM) by integrating different population-based optimisation techniques. The vehicle and the human body models are used to determine the optimal values leading to better ride comfort by means of different optimisation methods, including the particle swarm optimisation (PSO), the differential evolution (DE), and the genetic algorithm (GA). Since the non-linear approach reflects more realistic vibration behaviour than the linear one, the seat and suspension springs are assumed to have cubic progressive characteristics in the mathematical model. An objective function is proposed according to the feasible ideal solution of the root-mean-square (RMS) values of the seat, vehicle, and head accelerations and the suspension deflections considering the constrained functions. The results demonstrate that overall vibration amplitudes are significantly reduced and different techniques can provide a better reduction in different cases.