Applying fuzzy logic control to analyze real-time control for charging and discharging power of electric vehicles

Issue title: Fuzzy logic systems for transportation engineering

Guest editors: Dalin Zhang, Sabah Mohammed and Alessandro Calvi

Article type: Research Article

Authors: Liu, Feng^{; *}

Affiliations: Beijing Institute of Technology (BIT), Beijing, China

Correspondence: [*] Corresponding author. Feng Liu, Beijing Institute of Technology (BIT), Beijing, China. E-mail: liufeng01@tsinghua.org.cn.

Abstract: The disorderly charging of large-scale electric vehicles will aggravate the peak-valley difference of the power grid, and affect the power quality and life of the transformer. The fuzzy logic control strategy for charging and discharging optimization of charging vehicles under the framework of fuzzy logic control from the perspective of the group is considered in this article. A real-time control method based on the clustering characteristics of the charging end time is proposed according to the different charging requirements of the connected electric vehicles and fuzzy logic control is adopted to solve the problem of optimal charging and discharging power of the entire cluster and a single electric vehicle. A fuzzy logic control model considering the charging and discharging of electric vehicles is established orienting at minimize daily load fluctuations and control penalties in the upper layer. The charging and discharging cost of electric vehicle owners is considered to solve the optimal control problem of the charging and discharging power of a single electric vehicle. Taking the data of the typical regional distribution network load as an example, it is verified that the real-time charging optimization strategy under fuzzy logic control through simulation can ensure the reliable operation of the power grid while considering the interests of all parties.

Keywords: Electric vehicle cluster, interaction between electric vehicle and power grid (v2 g), clustering, double-tier optimization

DOI: 10.3233/JIFS-189978

Journal: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 4, pp. 4921-4927, 2021