Data-driven optimized layout of battery electric vehicle charging infrastructure

Ye Tao, Miaohua Huang and Lan Yang

Energy, 2018, vol. 150, issue C, 735-744

Abstract: The work established a mathematical model to optimize the layout of charging infrastructure based on the real-world driving data of 196 battery electric vehicles in Wuhan. Two hundred and thirty-three candidate locations of the charging site were designated by analyzing these data. The mathematical model was implemented, using genetic algorithm with Matlab software. The life of power battery of battery electric vehicle was shortened under over discharge (state of charge below 20%). The optimization target was to reduce the amount of over discharge for a lower over discharge rate. Compared with the current charging points, the layout calculated by our model remarkably decreased the over discharge rate of the electric vehicles from 68.1% to 39.6% and 15.3% for slow and fast charging, respectively. Besides, we discussed the relationship among over discharge rate and the number of charging points, budget costs, as well as rated range. Moreover, the work have studied the connection of the number increase of charging points and retention rate. When the number of charging points increased from 60 to 220, the retention rate was 97% for slow charging and 95% for fast charging.

Keywords: Battery electric vehicle; Genetic algorithm; Driving data; Over discharge rate (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:150:y:2018:i:c:p:735-744

DOI: 10.1016/j.energy.2018.03.018

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