Optimizing passive vehicle suspensions: A cross-entropy approach to comfort and safety trade-offs
Résumé
Comfort and safety are conflicting objectives in passive vehicle suspension design. Comfort requires soft suspensions to minimize vibrations transmitted to passengers, while safety demands stiffer setups to maintain continuous tire-road contact. Asymmetric dampers offer a practical solution by providing different damping characteristics during compression and rebound, enabling a tailored response to varied road conditions. This work presents an optimization framework based on the Cross-Entropy method to design passive suspensions that strike a balance between these competing goals. The method integrates a quarter-car model, standardized road profiles, and a multi-objective performance function that accounts for ride comfort, safety margin, and wheel travel. Simulation results show that symmetric dampers are more effective for comfort-focused designs on rough roads, while asymmetric dampers enhance safety and maintain adequate comfort on roads with sudden changes. For instance, using asymmetric damping improved tire contact stability by over three percent with negligible impact on comfort. Compared to Genetic Algorithms, the Cross-Entropy method achieved better convergence rates. It required fewer evaluations to identify optimal configurations, demonstrating both computational efficiency and robustness in navigating the highly non-convex suspension design space. The findings support the use of asymmetric dampers in performance-oriented suspension systems, establishing a strong foundation for experimental validation through hardware-in-the-loop simulations and physical prototypes. This study advances the integration of stochastic optimization in vehicle dynamics, offering a reliable tool for optimizing passive suspension systems under realistic operating conditions.