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To solve the problem of layout design of charging stations in the early stage of the electric vehicle industry, the user’s satisfaction and the charging convenience are considered. An electric vehicle charging station site-selection model is established based on the kernel density analysis of the urban population. The goal of this model is maximum electric vehicle user satisfaction and the highest charging convenience. Then, according to model characteristics, the immune algorithm is designed and optimized to solve the model. The optimization of the immune algorithm includes two aspects. On the one aspect, judging that the stop condition is added in the mutation link. On the other aspect, two mutation operators are designed in the optimized immune algorithm. Finally, the simulation example is determined by a three-step method in Jinan City. The results show that the electric vehicle charging station site-selection model in this paper can better meet user needs compared with traditional models. Compared with the traditional immune algorithm, the convergence speed of the optimized immune algorithm is improved, and the proposed algorithm is superior to the traditional immune algorithm in terms of stability and accuracy.

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This paper proposes an optimization method for electric vehicle charging station locations considering dynamic charging demand. Firstly, the driving characteristics and charging characteristics of the electric vehicle are obtained based on the driving trajectory of the electric vehicle, and the charging demand is predicted using a Monte Carlo simulation. Then a mathematical model with the goal of minimizing the overall cost is constructed, and the impact on carbon emissions is considered in the model. In order to better solve the location model, an improved whale optimization algorithm based on a hybrid strategy is proposed. Finally, the location problem of Shenzhen electric taxi charging stations is analyzed as an example. The results show that when the number of charging stations is set to 19, the comprehensive cost is the smallest and the energy saving and emission reduction effect is good. The improved whale optimization algorithm also has higher solution accuracy and convergence speed than other classical algorithms.

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