Stiffness-damping matching of all-terrain vehicle intelligent suspension with a two-stage optimization algorithm

Abstract

The all-terrain vehicle (ATV) equipped with magnetorheological(MR) damper and air springs can adjust suspension stiffness and damping based on prevailing driving conditions to enhance ride comfort and driving safety. However, the challenge lies in how to match the stiffness and damping to achieve maximum vibration attenuation for various working conditions. In this paper, a two-stage optimization algorithm is proposed. Initially, using the dynamic equation and the vehicle bias design requirements, the first-stage optimization determines the parameter range for the air spring’s characteristic curve and the damping coefficient range for the damper. Subsequently, a comprehensive vehicle model is established based on Carsim, and the optimization goal is set by combining GB/T 4970-2009 and driving safety indices. The theoretically derived parameter range serves as the optimization boundary. The second stage employs a genetic algorithm (GA) to obtain specific values for stiffness and damping parameters. Finally, the effectiveness of the proposed method is verified through real-vehicle off-road experiments. The results demonstrate that, compared with the original passive suspension, the intelligent suspension with stiffness-damping matching significantly attenuates pitch and roll acceleration in the 0.5–2 Hz range and vertical acceleration in the 4–12.5 Hz range under various loads.