North American freight railroads uniquely combine safety, speed and efficiency in transporting large amounts of freight long distances over land. Prompted by railroad commitments to reduce emissions, and looming stricter in-use locomotive emissions regulations, US freight railroads face the challenge of reducing or eliminating the roughly 3 billion gallons of diesel fuel consumed each year. To achieve this, North American freight railroads are exploring alternative technologies, including modern options for electrification that leverage traditional traction power supply via overhead contact (catenary) systems (OCS), along with rapidly advancing battery technology.
Previous research by the University of Texas at Austin has investigated the overall economics of pure OCS and Battery Electric Locomotive (BEL) options. The resulting Cost Uncertainty and Risk of Rail Electrification wit New Technologies (CURRENT) model is also capable of evaluating the concept of intermittent electrification that uses batteries onboard electric locomotives to reduce the amount of OCS construction required on a given corridor.
Currently, application of the CURRENT model is subject to two main limitations. First, the model assumes battery performance and efficiency as a function of state of charge is static over time and not influenced by environmental temperature. Second, intermittent electrification case studies have used engineering judgement to identify the locations of OCS segments along a corridor. However, it is likely that OCS placement could be further optimized to reduce infrastructure costs and maximize the battery range and corresponding lengths of gaps in the OCS.
This project will advance the CURRENT model to address both of these limitations. The project team will identify and implement an appropriate battery degradation model to accurately capture the change in battery efficiency and storage capacity as a function of discharge cycles over time. This change in efficiency will alter the long-term economics of batteries within the CURRENT model. The project team will also develop an algorithm to optimize OCS placement on a given corridor subject to traffic, topography and onboard battery size. It is anticipated that a genetic algorithm framework will be coupled with the CURRENT model to identify the combination of segments that maximizes economics.