Research on personalized charging strategy of electric bus under time-varying constraints

For electric bus fleets that have smaller battery capacity and use a short departure gap to recharge, due to the differences in remaining capacity of battery, allowable charging time, and bus line energy consumption, a unified charging strategy cannot further extend the battery service life, reduce the operating costs of the fleet, and ensure the bus punctuality. To solve this problem, a personalized charging strategy for electric bus fleet based on genetic algorithm and nearest neighbor algorithm was proposed, in which the remaining battery capacity, the allowable charging time and the line energy consumption are taken as time-varying constraints, a long-term charging experimental dataset of batteries under different charging strategies are used to construct the nearest neighbor regression model, and a penalty function is used to provide evolutionary pressure. Simulation and experimental analysis show that the algorithm can efficiently find the optimal charging strategy within the solution space allowed by penalty function constraints, effectively extend the service life of the power battery, and ensure the punctuality of the vehicle.