United States Department of Transportation
i
Intelligent Wireless Charging for Electric Buses in Smart City
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2016-07-01
[PDF-2.72 MB]
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Edition:Final report, 6/1/14-2/29/16
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Abstract:According to the Environmental Protection Agency (EPA), 28% of all 2011 Greenhouse Gases (GHGs) for the US are from transportation related sources. These are the second largest sources of GHGs in the US after electricity. The US is also the second highest CO₂ emitter after China. These emissions are primarily from burning fossil fuels for transportation usage. While vehicles have become more environmentally friendly with lower emissions, there has still been a steady rise in GHGs from these modes of transportation. The EPA estimates that there has been an increase of 18 percent GHGs, which is most likely due to more vehicles on the road. One method for eliminating air emissions is through environmentally friendly transportation modes. Electric vehicles (EVs) are considered a prime candidate for lowering carbon and other environmentally unfriendly footprints. However, the leading issues with the adoption of EVs in today’s market are due to their limited driving range and lack of charging infrastructure along with the long durations of non-operation during recharging. Current technology is addressing both of these concerns through creating better, longer lasting batteries as well as other methods for charging both efficiently and quickly. The adoption of EVs by commercial fleets is an easier implementation strategy since fleets typically have predetermined routes and scheduling. The authors propose a feasibility study to determine primarily whether wireless charging at specifically designated bus stops throughout New York City can help to increase the feasibility of electric buses for city use, both from an operational and a financial standpoint. The authors have partnered with the Metropolitan Transit Authority (MTA) in order to obtain data about bus operations. The final deliverable is to provide a statistical model that can be utilized with bus data to determine the efficacy of wireless charging at bus stops. The authors' model can be adjusted to see how the placement of varying numbers of charging stations would change the outcome of buses being able to complete their routes. Using probabilistic modeling, the authors cluster bus trips to discover patterns of travel for buses, which include time spans that are spent at different stops. Using model selection, the authors choose the best model parameter, i.e. number of clusters equivalent to number of travel patterns. This probabilistic model allows them to simulate data since it is a generative model and can easily be applied to other bus lines. Also, this model allows the authors to simplify the optimization problem of finding good spots to install wireless charging pads that cater to as many bus trip types as possible with minimum disruption of the current schedule.
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