A Suspension Model Based on Semi-Recursive Method and Data-Driven Compliance Characteristics

Many methods have been proposed to accurately compute a vehicle’s dynamic response in real-time. The semi-recursive method, which models using relative coordinates rather than dependent coordinates, has been proven to be real-time capable and sufficiently accurate for kinematics. However, not only kinematics but also the compliance characteristics of the suspension significantly impact a vehicle’s dynamic response. These compliance characteristics are mainly caused by bushings, which are installed at joints to reduce vibration and wear. As a result, the use of relative or joint coordinates fails to account for the effects of bushings, leading to a lack of compliance characteristics in suspension and vehicle models developed with the semi-recursive method. In this research, we propose a data-driven approach to model the compliance characteristics of a double wishbone suspension using the semi-recursive method. First, we create a kinematic double wishbone suspension model using both the semi-recursive method and multibody simulation software. Next, we enhance this model by incorporating bushings in the simulation software and derive compliance data from the simulations. Finally, by correcting the semi-recursive method’s results using the output from a neural network trained on the compliance data, we improve the accuracy of the proposed method. Moreover, due to the efficiency of the neural network, the proposed method’s computational efficiency is largely unaffected.