Simultaneous Optimization of Ride Comfort and Energy Harvesting Through a Regenerative, Active Suspension System Using Genetic Algorithm

Active suspension systems have long been recognized as an effective means of improving ride comfort and vehicle handling. However, high energy consumption and a lack of economic justification have hindered their commercial adoption in the industry. In order to address the challenges, this research proposed an innovative control structure that utilizes linear electromagnetic actuators capable of functioning in both motor and generator modes. To implement the proposed method, a suitable vehicle dynamic model available within the Adams software was selected. An analytical model corresponding to the software model was then extracted and verified to ensure its accuracy and reliability for use in GA optimization algorithms. Assuming only ride maneuvers, a feedback control structure based on meaningful terms in vehicle dynamics was developed. Then by using a GA algorithm, the ride comfort and energy harvesting criteria were simultaneously optimized. Finally, by exploiting the most suitable set of coefficients in the developed control structure, the suspension system showed the ability to recover up to 650 watts of power on rough roads, while leading to a 45% improvement in ride comfort.