Analysis and Optimization of Ride Comfort of Four-Axle Electric Sightseeing Vehicle

This paper first developed a twelve degree-of-freedom (DOF) ride comfort simulation model based on a four-axle commercial vehicle. Next, the simulation results are analyzed to study the influence of various parameters of the vibration system on the ride comfort of the vehicle. The results show that the matching degree of the seat system parameters and the actual vehicle is low and needs to be optimized. Then, an improved particle swarm optimization (PSO) is proposed based on dynamic inertia weight and learning factors. The seat stiffness and seat damping coefficient are used as design variables, and the total weighted Root Mean Square(RMS) acceleration of the driver’s seat is used as the objective function. Use an improved particle swarm optimization (IPSO) to improve the vehicle’s ride comfort and obtain the optimal stiffness and damping coefficient. Finally, the weighted RMS acceleration before and after optimization is compared through the simulation analysis of ride comfort. In the case of B and C road input, the driver’s seat weighted RMS acceleration decreased by 13.1% and 15.0%, respectively. The effectiveness of the improved ride comfort model and the IPSO is proved. The study provides guidance for the analysis of vehicle ride comfort.