Charging, steady-state SoC and energy storage distributions for EV fleets

Design and Implementation of Low-Cost Electric Vehicles (EVs) Supercharger: A Comprehensive Review

“…Within this work, we want to bridge this gap by using a novel agent-based simulator, GAIA [37]; that allows us to model EVs' daily natural charging rhythm, referred to throughout this paper as the genuine charging pattern. The technology in the GAIA model is based on a steady-state SoC distribution [38] and a probabilistic decision-to-charge model that uses information-sharing [39] to simulate and analyze different charging strategies. Furthermore, there needs to be more studies that try to capture the combined probabilistic nature of both residential base load and EV load while analyzing the resulting grid impact.…”

“…The choice of charging is based on a discrete choice model that considers the present SoC and the remaining trip diary requirements while accounting for detours, waiting time, and parking restrictions. The initialization of agents and the decision to search for a charging option are based on the models for steady-state SoC distributions and the decision to charge introduced in [38].…”

Optimal placement and sizing of charging infrastructure for EVs under information-sharing

“…The idea of using information sharing in the context of an EV charging system is that the choice of well-informed users, will prevent queuing at the level of chargers and improve the overall system performance. More specifically, by informing users about waiting time at the chargers, these are able to use their battery flexibility (Hipolito et al, 2022) in combination with a geographically scattered charging infrastructure to circumvent queuing. It will cause the utilisation percentage at the stations to follow a largely uniform distribution pattern.…”

“…Sampling of driving range and initial State-of-Charge for all vehicles is based on their expected home-charging opportunities and the length of the trip. While in this paper, we did not use the steady-state approximation provided in Hipolito et al (2022), we project driving range by a shifted-mean approach as also described in Rich et al (2022). 5.…”

Shifting the focus

“…The definition of the initial SOC level of the individual EVs is highly influential to the results. Therefore, we rely on the steady-state SOC distribution proposed by Hipolito et al (2022) and calibrated to empirical data collected from 10,000 EVs in Denmark.…”