Fuzzy logic based multi-objective optimization of active suspension system of 4x4 in-wheel motor driven electrical vehicle

In this thesis, fuzzy logic based multi-objective optimization of a nonlinear active suspension system of 4x4 in-wheel motor-driven electrical vehicle is studied by considering real working conditions such as roll angle and load transfer of the suspension systems. In this regard, a nonlinear full electrical vehicle model with quadratic tire stiffness and cubic suspension stiffness with eleven degrees of freedom and a seat-driver model with five degrees of freedom implemented and optimized by the guidelines introduced in ISO 2731-1 to assess ride and health criteria. Selected objective functions are comprised of weighted root mean square head acceleration, root mean square seat acceleration, crest factor, vibration dose value, the amplitude of head root mean square acceleration to seat root mean square acceleration, the amplitude of upper torso root mean square acceleration to seat root mean square acceleration, and root mean square upper torso acceleration. In addition to these, rarely considered rollover effect was investigated. Root mean square suspension displacement, root mean square tire displacement, root mean square in-wheel motor displacement, and roll angle were selected as constraints. Optimization was carried out with NSGA-II algorithm. Design variables for the passive system are; stiffnesses and dampers of suspension, in-wheel motor, and seat. Then, a fuzzy logic controller coupled with a proportional derivative controller optimized for best ride comfort and health criterion. Presented optimization results demonstrated a significant improvement over the passive system with fuzzy logic controller, and the load transfer index showed no adverse change between models concerning the rollover condition.