Operating Expense Optimization for EVs in Multiple Depots and Charge Stations Environment Using Evolutionary Heuristic Method

Hui Miao, Guo Chen, Chaojie Li, Zhao Yang Dong, Kit Po Wong

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

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 (HESAA) is designed to split the large scale logistic network into different clusters. In the second step, a new Shortest Path Heuristic (SPH) 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 (DDE-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.

Original languageEnglish
Article number7953580
Pages (from-to)6599-6611
Number of pages13
JournalIEEE Transactions on Smart Grid
Volume9
Issue number6
DOIs
Publication statusPublished - Nov 2018

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