Path planning and charging strategy for electric logistics vehicles with clustering non-dominated sorting
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Abstract:
The limited battery capacity, long charging time and inadequate supporting facilities of electric logistics vehicles restrict their effective promotion in the logistics and distribution field. In this paper, an improved cluster non-dominated sorting genetic algorithm (AP-NSGA-II) is proposed to solve the multi-objective route optimization problem of electric logistics vehicles. A charging strategy is established:dividing the distribution clusters by designing weighted AP clusters to avoid the randomness and blindness of the initial population, and reducing the running time and complexity of the non-dominated sorting algorithm by the scale of distribution points within the clusters. According to the distribution and distance relationship of charging stations, the electric logistics vehicles execute partial charging strategy. Finally, the effectiveness of the algorithm is demonstrated by simulation experiments, and the differences between full charging and partial charging conditions for electric logistics vehicles are compared.
