Battery-assisted Electric Vehicle Charging: Data Driven Performance Analysis

Junade Ali, Vladimir Dyo, Sijing Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As the number of electric vehicles rapidly increases, their peak demand on the grid becomes one of the major challenges. A battery-assisted charging concept has emerged recently, which allows to accumulate energy during off-peak hours and in-between charging sessions to boost-charge the vehicle at a higher rate than available from the grid. While prior research focused on the design and implementation aspects of battery-assisted charging, its impact at large geographical scales remains largely unexplored. In this paper we analyse to which extent the battery-assisted charging can replace high-speed chargers using a dataset of over 3 million EV charging sessions in both domestic and public setting in the UK. We rst develop a discrete-event EV charge model that takes into account battery capacity, grid supply capacity and power output among other parameters. We then run simulations to evaluate the battery-assisted charging performance in terms of delivered energy, charging time and parity with conventional high-speed chargers. The results indicate that in domestic settings battery-assisted charging provides 98% performance parity of high-speed chargers from a standard 3 kW grid connection with a single battery pack. For non-domestic settings, the battery-assisted chargers can provide 92% and 99% performance parity of high-speed chargers with 10 battery packs using 3 kW and 7 kW grid supply respectively.
Original languageEnglish
Title of host publication2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)
PublisherIEEE
Number of pages5
DOIs
Publication statusPublished - 10 Nov 2020

Keywords

  • Electric Vehicles
  • Electric Vehicles Charging

Cite this