A Joint Estimation Method for Suspension Status and Road Gradient Under Sparse Sensor Conditions

In the domain of engineering applications, this study addresses the challenge of achieving a unified estimation of the suspension relative velocity and road gradient during vehicle operation while mitigating the significant costs associated with automotive sensors. This paper proposes a simplified system model that integrates discretized vehicle vertical dynamics and longitudinal kinematics, intentionally excluding wheel dynamics. Utilizing the front axle vertical velocity, vehicle speed, and longitudinal and vertical accelerations as inputs, an estimator is employed in conjunction with the Extended Kalman filter algorithm to concurrently predict the relative velocity of the vehicle suspension, the sprung mass velocity, and the road gradient. The feasibility of the proposed methodology is corroborated through simulation experiments. Furthermore, real-world road tests validate the efficacy and timeliness of the joint estimation approach based on a “2 + 1” sensor arrangement. This methodology not only optimizes sensor system configuration and reduces engineering costs but also provides substantial technical support for further advancements in vehicle parameter estimation and suspension control applications.