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. 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. 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.

翻译：非对称阻尼技术在被动车辆悬架中广泛应用，通常推荐回弹阻尼为压缩阻尼的两到三倍。尽管该准则广为使用，但其依据仍主要源于经验积累，缺乏基于车辆动力学与激励条件的系统推导。本文提出了一种场景驱动的优化框架，为非对称阻尼的有效性提供了原理性解释。采用最小化四分之一车辆模型，通过标准化ISO 8608路面激励，分离出驾驶舒适性、轮胎接地性与瞬态响应之间权衡的关键机制。将回弹与压缩阻尼比作为独立设计变量，应用随机交叉熵算法在非凸仿真目标函数中识别最优配置。通过ISO 2631加权均方根加速度、轮胎-地面接触力变异系数及稳定时间评估性能。结果表明：在适度激励下对称阻尼通常足够，而恶劣工况下非对称阻尼成为必要——且常见回弹-压缩比并非普适常数，而是依赖于具体激励场景的近似最优解。