Multiobjective optimization for semi-active electromagnetic vehicle suspensions

In this paper, a multi-objective optimization process is used to design and optimize a semi-active hybrid electromagnetic suspension system. To control the performance of the vehicle, the magnetorheological (MR) damper modeled by Bouc-Wen model is utilized in semi-active suspension that its energy is supplied from harvested energy by electromagnetic generator. The performance of the suspension system is evaluated by ride comfort, road holding and absolute regenerated power criteria. A two-degree of freedom (2-DOF) quarter car model included semi-active suspension system and electromagnetic generator is used to analyze the system. To improve the performance of the vehicle, the genetic algorithm (GA) is used to solve the multi-parameter optimization problem. The Pareto front results obtained from GA show that the ride comfort and handling stability are two conflicting design criteria. To compare the optimized cases with the not-optimized suspension system the response of the system in time and frequency domains is employed. The results show that for the overall optimized case the absolute regenerative power and ride comfort can be improved significantly compared with the not-optimized case. Also, according to the frequency responses, only about the first natural frequency of the vehicle body, the ride comfort quality decreases for the overall optimized case.