Optimizing vehicle-to-grid charging strategies using genetic algorithms under the consideration of battery aging

Lithium-ion battery aging tests show that battery lifetime can be strongly influenced by the operating conditions, particularly by the state of charge and the cycle depth. Therefore a genetic optimization algorithm is applied to optimize the charging behavior of a plug-in hybrid electric vehicle (PHEV) connected to the grid with respect to maximizing energy trading profits in a vehicle-to-grid (V2G) context and minimizing battery aging costs at the same time. The simulation shows that the algorithm is able to increase the battery lifetime drastically and therefore reduces the mobility costs for the vehicle owner.