The U.S. transportation sector has been heavily reliant on fossil fuels, which currently fuel more than 90 percent of the sector’s energy needs. As a result, the sector has become the largest source of greenhouse gas (GHG) emissions (e.g., carbon dioxide, CO2) in the U.S. (Sen et al., 2019). Measures must be undertaken to decarbonize all GHG emission sources while enhancing the accessibility, affordability, efficiency, and variety of clean transportation options generating an uptick in socioeconomic opportunities across the nation. The U.S. transportation sector is an ideal target to expedite the government’s decarbonization efforts.
Battery electric buses (BEBs) have a significant potential for reducing greenhouse gas and criteria air pollutant emissions from U.S. public transportation, with major implications for its sustainability, and public health (Ercan & Tatari, 2015). Therefore, transit authorities aim to replace their fossil-fueled fleets with zero-emission fleets, in line with the federal and local mandates to meet certain levels of zero-emission fleet ownership goals (The White House, 2021). While BEBs offer great potential to decarbonize the industry, their deployment is contingent upon various environmental, techno-economic, and spatiotemporal considerations throughout their life cycles (National Academy of Sciences, 2013). At this juncture, transit authorities face three major challenges in deploying BEBs.
First, cities’ distinct socioeconomic and geographical characteristics are critical in determining the type of BEB and infrastructure configurations and must therefore be taken into account when planning for deployment. These characteristics influence BEB energy and power requirements, the infrastructure configuration required to meet the operational needs of the given transit system, and the total cost of deployment and maintenance (Basma et al., 2020).
Second, batteries, as the sole energy and power source of BEBs, are at the core of planning for BEB deployment. Battery configuration is crucial to meet BEB’s energy requirement for the planned daily schedule and formulate an adequate charging strategy to optimize the charging load on the electrical grid. Unlike conventional fuels, electricity rates fluctuate depending on location, time of usage, and demand charges (Matisoff et al., 2020). These variations introduce additional complexity for public transit agencies when identifying the right BEB options and how and when to charge them. This requires an understanding of BEB energy consumption under specific driving conditions, e.g., driving patterns, weather, and ridership, as determinants for the load during operation.
Third, transit authorities lack the knowledge base regarding BEB technologies, including their battery requirements, infrastructural needs, their maintenance and repair, and potential end-of-life scenarios. BEBs are built on powertrains that are distinct from conventional transit buses and will require a new set of maintenance and repair skills. In addition, transit agencies may well be subject to different economic and/or regulatory constraints that influence their decision-making process with regard to transit bus fleet procurement and maintenance. Furthermore, once purchased, BEBs are expected to be in operation throughout their lifetime, and from the life cycle perspective, it is crucial to take into account the time dimension of bus procurement at the time of decision.
The multiplicity of variables, as well as data needs thereof, increases the complexity of planning and decision-making to transition to zero-emissions transit fleets. Additionally, a variety of uncertainties exist that must be incorporated into decisions regarding BEB deployment, especially when infrastructure is concerned, such as energy prices. This problem can be solved by developing analytical tools that are capable of integrating these significant considerations from the life cycle perspective to help transit authorities make the optimal choice of battery electric bus type and its infrastructure given their needs and conditions.