Scenario-driven Optimization of Passive Vehicle Suspensions: Explaining the Effectiveness of Asymmetric Damping

Abstract Background Asymmetric damping is widely used in passive vehicle suspensions, with rebound damping often recommended to exceed compression damping by a factor of two to three. Despite its prevalence, this guideline remains largely empirical and lacks a systematic derivation based on vehicle dynamics and excitation conditions. Methods This paper presents a scenario-driven optimization framework that provides a principled explanation for the effectiveness of asymmetric damping. A minimal quarter-car model is employed to isolate the key mechanisms governing the trade-off between ride comfort, road holding, and transient response, using standardized ISO 8608 road excitations. Rebound and compression damping ratios are treated as independent design variables, and optimal configurations are identified via a stochastic Cross-Entropy algorithm applied to a non-convex, simulation-based objective function. Performance is assessed through ISO 2631 weighted RMS acceleration, tire–ground contact force variability, and settling time. Results The results show that symmetric damping is often sufficient under moderate excitation, whereas asymmetric damping becomes necessary under severe conditions, with commonly cited rebound-to-compression ratios emerging as scenario-dependent near-optimal solutions rather than universal constants.