期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 21, 期 12, 页码 5094-5109出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2019.2948596
关键词
Optimization; Cascading style sheets; Schedules; Companies; Heuristic algorithms; Electric vehicles; Differential evolution; mixed variables; electric vehicle; charging scheduling; transportation network
资金
- National Natural Science Foundation of China [61873095, 61873097, U1701267]
- Science and Technology Planning Project of Guangzhou [201904010211]
- Guangdong Natural Science Foundation Research Team Project [2018B030312003]
- GuangdongHong Kong Joint Innovation Platform Project [2018B050502006]
The increasing popularity of battery-limited electric vehicles puts forward an important issue of how to charge the vehicles effectively. This problem, commonly referred to as Electric Vehicle Charging Scheduling (EVCS), has been proven to be NP-hard. Most of the existing works formulate the EVCS problem simply as a constrained shortest path finding problem and treat it by discrete optimization. However, other variables such as the charging amount of energy and the charging option at a station need to be considered in practical use. This paper hence formulates the EVCS problem as a hierarchical mixed-variable optimization problem, considering the dependency among the station selection, the charging option at each station and the charging amount settings. To adapt to the new problem model, we specifically design a Mixed-Variable Differentiate Evolution (MVDE) as the scheduling algorithm for our proposed EVCS system. The MVDE contains several specific operators, including a charging station route construction, a hierarchical mixed-variable mutation operator and a constraint-aware evaluation operator. Experimental results validate the effectiveness of our proposed MVDE-based system on both synthetic and real-world transportation networks.
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