ARTICLE | doi:10.20944/preprints202203.0119.v1
Subject: Engineering, Automotive Engineering Keywords: smart scheduling; smart reservations; reinforcement learning; electric vehicle charging; electric vehicle charging management platform; neural network; DQN reinforcement Learning algorithm
Online: 8 March 2022 (08:54:48 CET)
The widespread adoption of electromobility constitutes one of the measures designed to reduce air pollution caused by traditional fossil fuels. However, several factors are currently impending this process, ranging from insufficient charging infrastructure, battery capacity, long queueing and charging time, to psychological factors. On top of range anxiety, the frustration of the EV drivers is further fueled by the lack the uncertainty of finding an available charging point on their route. To address this issue, we propose a solution that comes to bypass the limitations of the Reserve now function of the OCPP standard, enabling drivers to make charging reservations for the upcoming days, especially when planning a longer trip. We created an algorithm that generates reservation intervals based on the charging station's reservation and transaction history. Subsequently, we ran a series of test cases that yielded promising results, with no overlapping reservations.