In this paper, we study the electric vehicle path problem with a soft time window (EVRP-TW), considering both partial charging strategies and nonlinear energy consumption for electric vehicles. We first introduce a nonlinear energy consumption model that takes into account vehicle load, driving speed, and other factors. Secondly, we establish an electric vehicle distribution path planning model considering nonlinear energy consumption and charging strategy. The charging cost and battery loss cost generated in the charging process of electric vehicles are incorporated into the objective function, and coordinates the vehicle driving path and charging strategy to make the lowest total distribution cost for the enterprise. In order to solve the model effectively, we design an improved particle swarm genetic hybrid algorithm (PSO-GA). Finally, the validity of the model and algorithm is verified by taking the actual electric vehicle delivery business of J enterprise as an example. We compare the best delivery scenarios for electric vehicles under partial charging strategy and full charging strategy. The results show that the vehicle has significant advantages over the full charging strategy in terms of charging time, driving distance, and delivery cost in the nonlinear energy consumption case with the partial charging strategy; the PSO-GA algorithm proposed in the paper can significantly improve the quality of the solution compared with the classical algorithm.


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    Title :

    Distribution Route Optimization of Electric Vehicles Considering Charging Strategy and Nonlinear Energy Consumption


    Additional title:

    Uncertainty and Operations Research


    Contributors:
    Li, Xiang (editor) / Xu, Xiaofeng (editor) / Qi, Ding qin (author) / Mao, Zhi yong (author)

    Conference:

    International Conference on Intelligent Transportation and Logistics with Big Data & International Forum on Decision Sciences ; 2023 ; Qingdao, China August 11, 2023 - August 13, 2023



    Publication date :

    2024-07-01


    Size :

    21 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English






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