Optimization of Suspension Settings Using Genetic Algorithms for Improved Handling and Ride Comfort on Different Terrains: A Quarter Car Model

This paper presents an optimization approach using genetic algorithms to improve the handling and ride comfort of a quarter car model on different terrains. The suspension settings are optimized with the objective of minimizing the root mean square (RMS) value of the vertical acceleration of the sprung mass, while also reducing the lateral acceleration of the vehicle during cornering. The quarter-car model is simulated on three different terrains, including a smooth road, a rough road, and a sinusoidal road, to evaluate the performance of the optimized suspension settings. The results indicate that the optimized suspension settings significantly improve the ride comfort and handling of the vehicle on all three terrains. The RMS value of the vertical acceleration of the sprung mass is reduced by an average of 0.38, 0.21, and 0.42 on the smooth, rough, and sinusoidal roads, respectively, compared to the unoptimized suspension settings. Moreover, the lateral acceleration of the vehicle during cornering is reduced by 0.2, 1.2, and 1.6 on the smooth, rough, and sinusoidal roads, respectively, demonstrating improved handling. The optimization of suspension settings using genetic algorithms proves to be an effective approach for improving the ride comfort and handling of a quarter car model on different terrains. The results demonstrate a significant reduction in the RMS value of the vertical acceleration of the vehicle and lateral acceleration during cornering, which translates to a more comfortable and stable ride for passengers.