- Title
- Operating expense optimization for EVs in multiple depots and charge stations environment using evolutionary heuristic method
- Creator
- Miao, Hui; Chen, Guo; Li, Chaojie; Dong, Zhao Yang; Wong, Kit Po
- Relation
- ARC.DE160100675 http://purl.org/au-research/grants/arc/DE160100675
- Relation
- IEEE Transactions on Smart Grid Vol. 9, Issue 6, p. 6599-6611
- Publisher Link
- http://dx.doi.org/10.1109/TSG.2017.2716927
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- journal article
- Date
- 2018
- Description
- 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.
- Subject
- batteries; optimization; logistics; mathematical model; heuristic algorithms; clustering algorithms
- Identifier
- http://hdl.handle.net/1959.13/1410316
- Identifier
- uon:36162
- Identifier
- ISSN:1949-3053
- Language
- eng
- Reviewed
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