A Simulated Annealing Approach to the Scheduling of Battery-Electric Bus Charging

With an increasing adoption of battery-electric bus (BEB) fleets, developing a reliable charging schedule is vital to a successful migration from their fossil fuel counterparts. In this paper, a simulated annealing (SA) implementation is developed for a charge scheduling framework for a fixed-schedule fleet of BEBs that utilizes a proportional battery dynamics model, accounts for multiple charger types, allows partial charging, and further considers the total energy consumed by the schedule as well as peak power use. Two generation mechanisms are implemented for the SA algorithm, denoted as the “quick” and “heuristic” implementations, respectively. The model validity is demonstrated by utilizing a set of routes sampled from the Utah Transit Authority (UTA) and comparing the results against two other models: the BPAP and the Qin-Modified. The results presented show that both SA techniques offer a means of generating operationally feasible schedules quickly while minimizing the cost of operation and considering battery health.

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Publication Details

Journal
Future Transportation
Published
2024-09-09
DOI
https://doi.org/10.3390/futuretransp4030049
Citations
2
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
0.41
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article

A Simulated Annealing Approach to the Scheduling of Battery-Electric Bus Charging

Alexander Brown, Greg N. Droge
2 citations
Future Transportation
Advanced Battery Technologies Research
0.41
article

A Simulated Annealing Approach to the Scheduling of Battery-Electric Bus Charging

Alexander Brown, Greg N. Droge
article en
2 citations

Abstract

With an increasing adoption of battery-electric bus (BEB) fleets, developing a reliable charging schedule is vital to a successful migration from their fossil fuel counterparts. In this paper, a simulated annealing (SA) implementation is developed for a charge scheduling framework for a fixed-schedule fleet of BEBs that utilizes a proportional battery dynamics model, accounts for multiple charger types, allows partial charging, and further considers the total energy consumed by the schedule as well as peak power use. Two generation mechanisms are implemented for the SA algorithm, denoted as the “quick” and “heuristic” implementations, respectively. The model validity is demonstrated by utilizing a set of routes sampled from the Utah Transit Authority (UTA) and comparing the results against two other models: the BPAP and the Qin-Modified. The results presented show that both SA techniques offer a means of generating operationally feasible schedules quickly while minimizing the cost of operation and considering battery health.

Future TransportationVol. 4(3)
Utah State University (US)
Openalex Percentile: Top 40%
Advanced Battery Technologies Research
0.41
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