Kinematics & compliance validation of a vehicle suspension and steering kinematics optimization using neural networks

Abstract

Physical and virtual K & C analyses are performed to achieve the vehicle dynamics targets by finding the opti-mum variables such as the position of hardpoints or stiff-nesses of bushings. However, finding appropriate design variables that meet all the aims is challenging. This paper evaluates a hardpoint optimization approach to attain sus-pension K & C characteristic objectives with the design of experiments, neural networks, and genetic algorithm, based on a reference compact-sized prototype vehicle. The MBD model correlation is provided to optimize the hardpoints to improve the vehicle's steering kinematics concerning Ackerman error and camber angle variation that are out of target in baseline suspension. The results showed that NN based optimization strategy to define the hardpoints has sig-nificantly improved targeted characteristics compared to conventional response surface methods in the limited design space.