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A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm

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  • Fanrong Kong

    (School of Software Engineering, Tongji University, Shanghai 201804, China
    Shanghai Development Center of Computer Software Technology, Shanghai 201112, China
    Shanghai Industrial Technology Institute, Shanghai 201206, China)

  • Jianhui Jiang

    (School of Software Engineering, Tongji University, Shanghai 201804, China)

  • Zhigang Ding

    (Shanghai Development Center of Computer Software Technology, Shanghai 201112, China
    Shanghai Industrial Technology Institute, Shanghai 201206, China)

  • Junjie Hu

    (Department of Electrical Engineering, Center for Electric Power and Energy, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark)

  • Weian Guo

    (Sino-German College of Applied Sciences, Tongji University, Shanghai 201804, China)

  • Lei Wang

    (School of Electronics and Information Science, Tongji University, Shanghai 201804, China)

Abstract

To alleviate the emission of greenhouse gas and the dependence on fossil fuel, Plug-in Hybrid Electrical Vehicles (PHEVs) have gained an increasing popularity in current decades. Due to the fluctuating electricity prices in the power market, a charging schedule is very influential to driving cost. Although the next-day electricity prices can be obtained in a day-ahead power market, a driving plan is not easily made in advance. Although PHEV owners can input a next-day plan into a charging system, e.g., aggregators, day-ahead, it is a very trivial task to do everyday. Moreover, the driving plan may not be very accurate. To address this problem, in this paper, we analyze energy demands according to a PHEV owner’s historical driving records and build a personalized statistic driving model. Based on the model and the electricity spot prices, a rolling optimization strategy is proposed to help make a charging decision in the current time slot. On one hand, by employing a heuristic algorithm, the schedule is made according to the situations in the following time slots. On the other hand, however, after the current time slot, the schedule will be remade according to the next tens of time slots. Hence, the schedule is made by a dynamic rolling optimization, but it only decides the charging decision in the current time slot. In this way, the fluctuation of electricity prices and driving routine are both involved in the scheduling. Moreover, it is not necessary for PHEV owners to input a day-ahead driving plan. By the optimization simulation, the results demonstrate that the proposed method is feasible to help owners save charging costs and also meet requirements for driving.

Suggested Citation

  • Fanrong Kong & Jianhui Jiang & Zhigang Ding & Junjie Hu & Weian Guo & Lei Wang, 2017. "A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm," Energies, MDPI, vol. 10(9), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1333-:d:110797
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    References listed on IDEAS

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    Cited by:

    1. Yizhi Cheng & Peichao Zhang & Xuezhi Liu, 2019. "Collaborative Autonomous Optimization of Interconnected Multi-Energy Systems with Two-Stage Transactive Control Framework," Energies, MDPI, vol. 13(1), pages 1-21, December.
    2. Yajing Gao & Shixiao Guo & Jiafeng Ren & Zheng Zhao & Ali Ehsan & Yanan Zheng, 2018. "An Electric Bus Power Consumption Model and Optimization of Charging Scheduling Concerning Multi-External Factors," Energies, MDPI, vol. 11(8), pages 1-17, August.
    3. Baodi Zhang & Fuyuan Yang & Lan Teng & Minggao Ouyang & Kunfang Guo & Weifeng Li & Jiuyu Du, 2019. "Comparative Analysis of Technical Route and Market Development for Light-Duty PHEV in China and the US," Energies, MDPI, vol. 12(19), pages 1-23, September.

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