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The control strategy in hybrid electric vehicles (HEV) determines the energy management between an internal combustion (IC) engine and an electric machine to generate the power required to drive the vehicle. In this study, the parameters of an electric assist control strategy of HEV, which is used in ADVISOR software, are optimized by minimizing the fuel consumption while maintaining the engine emissions below the Euro3 standard. In this way, a parallel HEV has been simulated in two drive cycles and the optimal parameters of controller are obtained by the genetic algorithm (GA). The simulation results show that by optimizing the control parameters, the fuel consumption decreases while satisfying the Euro3 standards and other constraints in various drive cycles. Also, different values for optimal parameters have been extracted. This indicates that the optimal parameters of the controller are dependent on the drive cycle.

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In this paper, a new nonlinear model for magnetorheological (MR) damper based on the bouc-wen model is identified. This model including the hysteresis loop is linear with respect to the input current. This advantage makes it easy to use the proposed model in semi-active suspension systems as the inverse model. For better comparison, a polynomial model developed in the prevalent papers is also designed. The performance evaluation of two models shows that the proposed bouc-wen model has higher accuracy in the smaller velocity area. At the rest of the paper, an LQR controller designed for calculation of the active suspension force is integrated with the two identified inverse models. The simulation studies are carried out for active and semi-active suspension systems on a standard road. The results show that the simplified nonlinear bouc-wen model has better performance in controlling the semi active suspension system compared with the other models. In high amplitude velocities of the damper, the performance of two models is close to each other.