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

Fanrong Kong, Jianhui Jiang, Zhigang Ding, Junjie Hu, Weian Guo and Lei Wang
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Fanrong Kong: School of Software Engineering, Tongji University, Shanghai 201804, China
Jianhui Jiang: School of Software Engineering, Tongji University, Shanghai 201804, China
Zhigang Ding: Shanghai Development Center of Computer Software Technology, Shanghai 201112, 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

Energies, 2017, vol. 10, issue 9, 1-18

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.

Keywords: plug-in hybrid electrical vehicle; personalized statistic driving model; heuristic algorithm; rolling optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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