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dc.contributor.advisorSchwartz, Daniel T.
dc.contributor.authorEggleton, Erica E.
dc.date.accessioned2022-07-14T22:07:00Z
dc.date.available2022-07-14T22:07:00Z
dc.date.submitted2022
dc.identifier.otherEggleton_washington_0250E_24424.pdf
dc.identifier.urihttp://hdl.handle.net/1773/48861
dc.descriptionThesis (Ph.D.)--University of Washington, 2022
dc.description.abstractThe demand for electrified transportation, including battery-electric buses, is increasing as fleets aim to meet lower carbon emissions goals. However, there exist knowledge gaps, which include but are not limited to charging infrastructure placement and demand, battery sizing for optimal vehicle range, and the effect of load demands on battery lifetime and performance, that impede the adoption of these technologies at the desired rate. Solving these problems requires information and models across scales, from the vehicle systems to the battery packs, cells, and materials. This work creates tools, data analysis, and datasets across these scales. First, a vehicle dynamics model is used to estimate the power and energy required to transport a battery-electric bus across a given route, using King County Metro bus routes as a case-study. The load demand for the Battery Energy Storage System (BESS) on-board is estimated, and routes are compared based on load profiles and distributions. The effect of route characteristics such as elevation, frequency of stops, and ridership mass on the battery load are also analyzed. Select load profiles are then translated to the cell-level and used as charging protocols for commercial Li-ion cells. The resulting voltage and current profiles allow us to calibrate a simple battery model that can estimate these profiles for other routes. Lastly, we emphasize the importance of data sharing to improve battery models. We introduce the framework for a standardized data-sharing protocol with suggested metadata. An example dataset utilizes the proposed format and demonstrates the importance of replicates in open datasets.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.rightsCC BY
dc.subjectBatteries
dc.subjectElectric Bus
dc.subjectElectric Vehicle
dc.subjectEnergy Storage
dc.subjectLithium-ion
dc.subjectVehicle Dynamics
dc.subjectChemical engineering
dc.subjectTransportation
dc.subjectEnergy
dc.subject.otherChemical engineering
dc.titleMultiscale Data, Analytics, and Tools for Transportation Electrification
dc.typeThesis
dc.embargo.termsOpen Access


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