Transit electrification through charger deployment, fleet type selection, and charging schedule optimization

Transit electrification requires strategic planning of chargers, fleet selection, and charging schedules. This study presents an integrated optimization model considering these elements alongside operational factors, such as time-of-day electricity prices, enabling realistic assessment of charging costs. We address the computational complexity of electrifying large-scale transit networks through a heuristic algorithm that decomposes the problem into two sub-problems, where the first determines the optimal charger locations, and the second optimizes fleet selection and allocation to bus blocks and charging schedules. We extract network and operational characteristics, like layover times, from GTFS data. The heuristic algorithm produces solutions within 2.2% of the optimal on average for medium-sized networks, demonstrating its effectiveness in finding near-optimal solutions. Our analysis shows that although the charging rate affects the number and capacity of chargers, it has a lower effect on the distribution of battery types. The results reveal that higher electricity prices incentivize the adoption of larger battery capacities, as they enable buses to shift charging to lower-cost periods and reduce reliance on expensive peak-hour electricity. We examine the transit networks of 20 cities and districts and find that in Washington, Philadelphia, and Brooklyn, more than 70% of buses can be electrified solely with overnight charging without requiring recharging during the day. In contrast, Los Angeles, Houston, and Calgary require substantial en-route charging to achieve high electrification while completing routes without running out of charge.