Optimal Control Strategy of Electric Vehicles Based on the Adaptive Mutation of PSO Algorithm

The development of technology concerned of electric vehicles and the expansion of Charging facilities scale, researching on Vehicle-to-Grid (V2G) technology has been increasing concerned in recently. Not only taking power from the grid to charge the batteries on these vehicles, but the power could also be transferred into the grid from their batteries when parked, which is known as Vehicle-to-Grid(V2G) concept. The key research technique is proposing a control strategy for scheduling usage of available energy storage capacity from electric vehicles reasonably and effectively in a certain scale plot. This paper establish the mathematical model which concludes two constrain conditions to simulate electric vehicles level the load curb. Then a new adaptive mutation particle swarm optimizer, which is based on the variance of the population's fitness is presented. A critical adaptive mutation operator is put forth in this algorithm in which mutation probability is decided according to the variance of the population's fitness and the current best solution, which improve the ability of algorithm to break away from local optimum. The last, example results show applicability and effectiveness of the algorithm which schedule vehicle for leveling a load curb.