Battery-assisted Electric Vehicle Charging: Data Driven Performance Analysis
Junade Ali, Vladimir Dyo, Sijing Zhang

TL;DR
This study evaluates how battery-assisted EV charging can replace high-speed chargers at large scales, showing near-parity performance in domestic and non-domestic settings through data-driven simulations.
Contribution
It introduces a discrete-event EV charge model and assesses battery-assisted charging performance across extensive real-world data, filling a gap in large-scale impact analysis.
Findings
Domestic battery-assisted charging achieves 98% parity with high-speed chargers.
Non-domestic settings reach up to 99% parity with 10 batteries and higher grid capacity.
Battery-assisted charging significantly reduces reliance on high-speed chargers at scale.
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 first 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…
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