The combinatorial optimization by Genetic Algorithm and Neural Network for energy storage system in Solar Energy Electric Vehicle

We investigated the optimal sizing of the energy storage system in a solar energy electric vehicle (SEEV) system. A model system was constructed for this that includes the photovoltaic power, the lead-acid battery and a flywheel.The optimal sizing can be considered as a constrained optimization problem: minimization the total capital cost of energy storage system in SEEV, subject to the main constraint of the loss of power supply probability (LPSP). The genetic algorithm or combinatorial optimization by genetic algorithm and neural network were used in this paper. And the decision variables are not only the capacity of batteries in traditional methods, but also the capacity of flywheel. Studies have proved that the optimization algorithms used can converge well and they are feasible. Combinatorial optimization by genetic algorithm and neural network can lessen the calculation time, with the results change little.