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Operating Expense Optimization for EVs in Multiple Depots and Charge Stations Environment Using Evolutionary Heuristic Method
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DOI：10.1109/TSG.2017.2716927.png)
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In this paper, an operating cost optimization problem of electric vehicles (EVs) is studied in a large-scale logistics and transportation network. An extended EV operational model is proposed for a multiple depots and charge stations environment where practical constraints are included. In the proposed model, new practical mathematical schemes are proposed to describe the constraints. Then, a new two-step clustering heuristic optimization (TCHO) method is developed to minimize the total operating cost of the EV routes while satisfying all the constraints. In the first step, a novel heuristic edge sharing assigning algorithm is designed to split the large scale logistic network into different clusters. In the second step, a new shortest path heuristic method is developed to minimize the total expense of the EV routes for each cluster. Furthermore, based on the TCHO, a novel discrete differential evolution-TCHO is proposed to improve the performance on solving the problem. The effectiveness of the proposed models and methods is verified by comprehensive numerical simulations where the well-known vehicle routing problem benchmarks are applied.
Keyword:
Electric vehicle
multiple depots
charge stations
heuristic method
discrete differential evolution
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