Optimizing the Geometrical Dimensions of the Seat Suspension Equipped with a Negative Stiffness Structure Based on a Genetic Algorithm

Based on the negative stiffness structure (NSS) designed on the seat suspension and the effect of the geometrical parameters of the designed NSS on improving the driver's ride comfort, a new optimal method of the multi-objective genetic algorithm (MOGA) is then researched and applied for optimizing the stiffness ratio beta of the seat suspension and the geometrical parameters alpha(1) and alpha(2) of the NSS to further improve the driver's ride comfort and health. The reduction of the root-mean-square (RMS) displacement of the driver's seat (X-RMSs), the weighted RMS acceleration of the driver's seat (a(RMSs)), and the seat effective amplitude transmissibility (SEAT) of the seat suspension are chosen as the objective functions. The study results show that with the optimal parameters of alpha(1) = 1.355, alpha(2) = 1.001, and beta = 0.511, the seat suspension using the optimized NSS has a good effect on isolating low-frequency vibration under various excitation sources of the random, harmonic, and bumpy functions. Particularly, the results of X-RMSs, a(RMSs), and SEAT with the optimized NSS are remarkably reduced by 13.35%, 22.51%, and 22.47%, respectively, compared to the designed NSS, and by 44.65%, 69.45%, and 69.44% in comparison without the NSS under a random excitation of the floor of the cab or vehicle. Therefore, this research results not only contribute to the existing body of knowledge on the seat suspensions equipped with the NSS but also can provide an important reference for optimal design or control of the seat suspension to further improve its isolating efficiency.