Optimizing In-Garage Charging Schedules to Maximize Electrified Mileage for Electric Bus Fleets

Many transit agencies across the US are working towards a zero-emission electric bus fleet in order to reduce petroleum use and carbon emissions. This thesis presents a data-driven approach to optimize short-term in-garage charging schedules of electric buses, aiming to enhance operational efficiency in public transportation systems. We estimate the energy required for each trip using historical data on temperature, ridership, and speeds. The proposed mixed-integer programming (MIP) model maximizes total electrified mileage while considering constraints related to charger configuration, block schedules, energy requirements, and battery capacity. To solve this complex problem in a reasonable timeframe, we further decompose the problem into two phases. The initial phase involves determining which blocks should be serviced by the same bus and establishing a schedule that covers each block exactly once. The subsequent phase focuses on identifying the optimal in-garage charging schedule and deciding which blocks should be electrified, considering the schedule from the first phase. The model’s effectiveness is demonstrated through a case study using real-world data from the Chicago Transit Authority (CTA). Future scenarios and sensitivity analyses, considering variations in available electric buses, charger configurations, and risk tolerance in estimated energy requirements for each block, offer comprehensive and valuable insights for the adoption of electric buses and chargers. Key findings include: (a) slow chargers may be more cost-effective than fast ones, given recent block schedules and cost estimates, (b) customizing charging strategies maximizes electrified distance but poses operational challenges, (c) agencies should assess the trade-offs between the electrifiable distances and the risk of running below specified state of charge (SOC) thresholds, (d) lower battery degradation may reduce the required number of buses for the same electrified mileage, and (e) seasonal analyses reveal that significantly more miles can be electrified during summer compared to winter due to the lower energy required for trips on warmer days.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Urban Studies and Planning

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Wu, Yen-Chu

Advisors dc:contributor.advisor

• Stewart, Anson F.
• Koutsopoulos, Haris N.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/156102

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/156102