Data-Driven Planning for Short-Haul Heavy-Duty Electric Vehicles: Insights from Naturalistic Driving Patterns
dc.contributor.author | Bragg, Haden Stuart | en |
dc.contributor.committeechair | Perez, Miguel A. | en |
dc.contributor.committeemember | Doerzaph, Zachary R. | en |
dc.contributor.committeemember | Hanowski, Richard J. | en |
dc.contributor.department | Biomedical Engineering and Mechanics | en |
dc.date.accessioned | 2025-06-06T17:49:48Z | en |
dc.date.available | 2025-06-06T17:49:48Z | en |
dc.date.issued | 2025-05-09 | en |
dc.description.abstract | Severe emissions from the transportation sector in the United States has encouraged growth in the use of electric vehicles. The adoption of heavy-duty battery electric vehicles (HDBEVs), however, has been delayed by uncertainties in their range and operational capabilities. These factors, coupled with a high initial cost of ownership, have presented a significant barrier to fleet operators considering electrifying their fleet. To address these concerns, having access to a variety of empirical data is crucial for fleet operators to make informed decisions about the viability of HDBEVs. Naturalistic driving data for diesel Class 8 trucks, which is collected on the basis that drivers should ideally behave as they would under current driving conditions, can be leveraged to model the behavior of HDBEVs under comparable conditions. This thesis investigates the use of telemetry data from two naturalistic driving studies for Class 8 vehicles, along with data for the current electric vehicle charging infrastructure, to assess the viability of using HDBEVs to fulfill routes for fleet operations. It also presents a tool that (1) can be used by individuals to evaluate which HDBEVs would be viable replacements for diesel vehicles on a workday shift basis and (2) where charging infrastructure would be necessary to support them. Previous efforts to plan for HDBEV charging infrastructure improvements in the United States have largely ignored the existing charging infrastructure, which can provide a solid foundation for implementing HDBEV charging solutions. This work provides a framework for evaluating the current charging infrastructure, predicting the performance of HDBEVs with real-world driving data, and identifying gaps in the charging network that should be addressed for the efficient and balanced implementation of HDBEVs in fleet applications. | en |
dc.description.abstractgeneral | As more electric vehicles reach consumers, commercial fleet operations have also started to use heavy-duty battery electric vehicles (HDBEVs). However, concerns about HDBEV’s range and performance have prevented their widespread use. Fleets hoping to use HDBEVs need to have reliable data about whether these vehicles will perform well on their routes and whether they will be able to charge them outside of their base location. One source of valuable information to determine this can come from naturalistic driving data for diesel trucks because such data captures how those vehicles actually performed under normal driving conditions. This data can be used to estimate how an HDBEV would perform under the same conditions as the current nonelectric fleet and where it would need to be charged. Previous work done to plan charging stations that can support HDBEVs have not fully considered the existing charging infrastructure, which could be converted to provide solutions for charging HDBEVs off-site. This thesis explores how naturalistic driving data for heavy-duty vehicles and data about the current charging infrastructure can be used to assess the charging outlook on actual fleet routes and how HDBEVs would perform on these routes, while providing a tool that fleet operators can use to gather and benefit from this information. | en |
dc.description.degree | Master of Science | en |
dc.format.medium | ETD | en |
dc.format.mimetype | application/pdf | en |
dc.identifier.uri | https://hdl.handle.net/10919/135102 | en |
dc.language.iso | en | en |
dc.publisher | Virginia Tech | en |
dc.rights | In Copyright | en |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
dc.subject | Electric vehicles | en |
dc.subject | Heavy-duty vehicles | en |
dc.subject | Charging stations | en |
dc.subject | Infrastructure | en |
dc.subject | Naturalistic driving | en |
dc.title | Data-Driven Planning for Short-Haul Heavy-Duty Electric Vehicles: Insights from Naturalistic Driving Patterns | en |
dc.type | Thesis | en |
dc.type.dcmitype | Text | en |
thesis.degree.discipline | Engineering Mechanics | en |
thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
thesis.degree.level | masters | en |
thesis.degree.name | Master of Science | en |