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

TL;DR

This paper introduces a mixed integer linear programming framework for efficient scheduling of electric bus charging, considering route constraints, battery health, and station resources, validated with real transit data.

Contribution

It presents a novel Position Allocation Problem model for BEB charging scheduling that incorporates battery dynamics and route constraints, improving upon heuristic methods.

Findings

Slow chargers are preferred and effectively utilized.

The model ensures all buses maintain required charge levels.

It reduces charger count and operational costs.

Abstract

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) in a modified form known as the Position Allocation Problem (PAP). 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 \A buses and \N 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 slow chargers are more readily selected and the charging and spatiotemporal constraints are met while considering the battery dynamics and minimizing both the charger count and consumption cost.