Enhanced ride performance of electric vehicle suspension system based on genetic algorithm optimization

Parameter optimization of active suspension in in-wheel motor driven electric vehicle using genetic algorithm (GA) is presented. In such vehicles, placing the motors in the wheel results in an increase in the unsprung mass, which greatly deteriorates the suspension ride comfort performance and road holding ability. Structures with suspended shaftless direct-drive motors have the potential to improve the road holding capability and ride performance. The GA is applied to obtain the optimal parameters under different road profiles. Parameters of the motor suspension (damping and stiffness coefficients), vehicle suspension and active controller are optimized based on quarter vehicle model. The optimization process aims to minimize the vertical acceleration of sprung mass and motor, dynamic force transmitted to the motor as well as suspension working space and road holding capability. The performance of the vehicle with passive suspension, active suspension with unoptimized parameters and optimized parameters are compared. The results show that active suspension with optimized parameters significantly outperforms other suspensions in motor ride performance.