Ride Comfort Analysis and Multivariable Co-Optimization of the Commercial Vehicle Based on an Improved Nonlinear Model

The performance of a suspension system is affected by the behavior and posture of its components. However, published studies usually conduct this research using the based-equivalent model without considering the characteristic curves or postures. In this paper, an improved ride comfort model that considers three nonlinearities in suspensions is first developed, and this model is validated through experimental results and demonstrates good accuracy. Then, the dynamic response is presented to investigate the effects of multilevel suspension parameters and nonlinear factors on ride comfort, and it is concluded that the front chassis suspension is the most significant system for ride comfort. Next, a multivariable co-optimization method based on the improved model is proposed to obtain more accurate optimized results that are more suitable for automotive applications. Subsequently, a multiobjective genetic algorithm (MGA) is applied to obtain the Pareto solution set. Furthermore, comparing the RMS value before and after optimization shows an obvious reduction, with averages of 19.7%, 17.8%, and 12.0% for the weighted root mean square (RMS) of the driver seat acceleration and the RMS of the working spaces of the front chassis suspension and the rear chassis suspension, respectively. Finally, the results are also verified by experiments, indicating that the improved ride comfort model and the multivariable co-optimization method are feasible and practical.