Optimization and control of hybrid vehicles with considering penalty factors for emission and based on the PSO algorithm

This paper presents a new approach to optimizing of the control strategy in parallel hybrid vehicles. By proposing a new objective function and in order to efficient management of the power split between the combustion and electrical motors, fuel consumption and the amount of emissions are minimized with considering the penalty functions. Using the ADVISOR software, as one of the most common software used for a hybrid vehicle, as well as nonlinear equations of these vehicles, a parallel hybrid vehicle in the urban driving cycle of the United States is simulated. In this paper, an extended algorithm based on the Particle Swarm Optimization (PSO) is used. In the proposed PSO algorithm, the coefficients of contraction and the mirror effect of speed are used to increase the efficiency of the method. With the new constraints proposed for the PSO algorithm, the results have been improved. Also, simulation results are compared with the results obtained from the algorithms presented in recent papers. This comparison also shows the efficiency and accuracy of the proposed algorithm in optimizing the control parameters that lead to reduced fuel consumption and emissions from the vehicle.