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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Process and Energy Systems Engineering
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1347442

A Position Allocation Approach to the Scheduling of Battery-Electric Bus Charging

Provisionally accepted
  • Utah State University, Logan, United States

The final, formatted version of the article will be published soon.

    Robust charging schedules in a growing market of Battery-Electric Bus (BEB) fleets are a critical component to successful adoption. In this paper, a BEB charging scheduling framework that considers spatiotemporal schedule constraints, route schedules, fast and slow charging, and battery dynamics is modeled as a Mixed Integer Linear Program (MILP). The MILP is modeled after the Berth Allocation Problem (BAP), a method of optimally assigning vessels to be serviced, in a modified form known as the Position Allocation Problem (PAP), which assigns Electric Vehicels (EVs) to be charged. Linear battery dynamics are included to model the charging of buses while at the station. To model the BEB discharges over their respective routes, it is assumed each BEB has an average kWh charge loss while on route. The optimization coordinates BEB charging to ensure that each vehicle remains above a specified State-Of-Charge (SOC). The model also minimizes the total number of chargers utilized and prioritizes slow charging for battery health. The model validity is demonstrated with a set of routes sampled from the Utah Transit Authority (UTA) for 35 buses and 338 visits to the charging station. The model is also compared to a heuristic algorithm based on charge thresholds referred to as the Qin-Modified method. The results presented show that the MILP framework encourages battery health by assigning BEBs slow chargers more readily than the Qin-Modified. The MILP utilizied one fast charger and six slow chargers whereas the Qin-Modified utilized four fast chargers and six slow. Moreover, the MILP was able to maintain a specified minimum SOC of 25% throughout the day and meeting a reqired minimum SOC at the end of the working day of 70% whereas the Qin-Modifed was unable to keep the SOC above 0% without any constraints applied. Furthermore, it is shown that the spatiotemporal constraints are met while considering the battery dynamics while minimizing both the charger count and consumption cost.

    Keywords: Berth Allocation Problem (BAP), Position Allocation Problem (PAP), Mixed integer linear program (MILP), Battery electric bus (BEB), Scheduling

    Received: 30 Nov 2023; Accepted: 30 Oct 2024.

    Copyright: © 2024 Brown, Droge and Gunther. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Alexander Brown, Utah State University, Logan, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.