Orderly charging strategy of battery electric vehicle driven by real-world driving data

Ye Tao, Miaohua Huang, Yupu Chen and Lan Yang

Energy, 2020, vol. 193, issue C

Abstract: The work preprocessed the real-world driving data of 1000 battery electric vehicles (BEVs) in Zhengzhou, China. Then a scheduling model of electric vehicles on time dimension was established based on the processed data. The mathematical model could meet the operation requirements of grid side and user side. The grid-side optimization minimized the system’s equivalent load fluctuation, and the user-side was optimized to maximize the charging capacity of electric vehicles. The mathematical model was solved by the genetic algorithm toolbox in Matlab software. Besides, we obtained the quantity distribution of BEV access to the power grid, parking time distribution, parking duration distribution and initial state of charge (SOC) distribution at the beginning of charging by analyzing the real-world driving data. These distribution curves were used to obtain the driving and charging habits of BEV drivers. By comparing the optimized orderly charging strategy with the random charging, in the case of meeting the user’s demand for charging power, the peak and valley difference and the equivalent load fluctuation of the power grid were significantly reduced by 22 and 22.7%, respectively. It greatly improves the security and economy of the grid.

Keywords: Battery electric vehicle; Driving data; Orderly charging; Genetic algorithm (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:193:y:2020:i:c:s0360544219325010

DOI: 10.1016/j.energy.2019.116806

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