Abstract
In recent years, with the gradual depletion of fossil energy and increasing environmental pollution, electric vehicles (EVs) have developed rapidly as one of the main substitutes for traditional fuel vehicles. Based on this, this paper firstly establishes a comprehensive optimization objective function including geographic information, charging time and charging amount. On the basis of determining the objective function, a quantum particle swarm optimization algorithm is proposed to solve the problem of optimal charging pile location and its number distribution. Secondly, a prediction model for the spatiotemporal distribution of electric vehicle users' charging demands based on fuzzy inference algorithm was proposed, and the charging load distribution curves of different functional areas were obtained by using Monte Carlo simulation method, which solved the spatiotemporal distribution of users' charging demands. Then, from the perspective of users, and taking into account the two-way cost of vehicle owners and charging station operators, a method of charging station location and capacity based on electric vehicle charging probability model is further proposed, which solves the problem of gradual expansion or reduction of charging piles. Finally, a multi-objective optimization model is established to solve the problem of optimal solutions for charging and battery swapping.
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