Control Strategy of Semi-Active Suspension Based on Road Roughness Identification

Taking the semi-active suspension system as the research object, the forward model and inverse model of a continuous damping control (CDC) damper are established based on the characteristic test of the CDC damper. A multi-mode semi-active suspension controller is designed to meet the diverse requirements of vehicle performance under different road conditions. The controller parameters of each mode are determined using a genetic algorithm. In order to achieve automatic switching of the controller modes under different road conditions, a method is proposed to identify the road roughness based on the sprung mass acceleration. The average of the ratio between the squared sprung mass acceleration and the vehicle speed within a specific time window is taken as the identification indicator for road roughness. Simulation results show that the proposed road roughness identification method can accurately identify smooth roads (Class A–B), slightly rough roads (Class C), and severely rough roads (Class D–H). The designed multi-mode semi-active suspension controller automatically adapts to the identified road roughness, resulting in improved ride comfort on severely rough roads and improved handling performance on smooth roads. Finally, a real vehicle test is performed. The test results show that the proposed road roughness identification method can effectively distinguish between a well-paved roads and rough roads. In addition, the ride comfort of the vehicle is significantly improved in the comfort mode of the controller on rough roads.