Vehicle powertrain model to predict energy consumption for ecorouting purposes
| dc.contributor.author | Tamaro, Courtney Alex | en |
| dc.contributor.committeechair | Nelson, Douglas J. | en |
| dc.contributor.committeemember | Ferris, John B. | en |
| dc.contributor.committeemember | Ellis, Michael W. | en |
| dc.contributor.department | Mechanical Engineering | en |
| dc.date.accessioned | 2016-06-28T08:00:28Z | en |
| dc.date.available | 2016-06-28T08:00:28Z | en |
| dc.date.issued | 2016-06-27 | en |
| dc.description.abstract | The automotive industry is facing some of the most difficult design challenges in industry history. Developing innovative methods to reduce fossil fuel dependence is imperative for maintaining compliance with government regulations and consumer demand. In addition to powertrain design, route selection contributes to vehicle environmental impact. The objective of this thesis is to develop a methodology for evaluating the energy consumption of each route option for a specific vehicle. A 'backwards' energy tracking method determines tractive demand at the wheels from route requirements and vehicle characteristics. Next, this method tracks energy quantities at each powertrain component. Each component model is scalable such that different vehicle powertrains may be approximated. Using an 'ecorouting' process, the most ideal route is selected by weighting relative total energy consumption and travel time. Only limited powertrain characteristics are publicly available. As the future goal of this project is to apply the model to many vehicle powertrain types, the powertrain model must be reasonably accurate with minimal vehicle powertrain characteristics. Future work expands this model to constantly re-evaluate energy consumption with real-time traffic and terrain information. While ecorouting has been applied to conventional vehicles in many publications, electrified vehicles are less studied. Hybrid vehicles are particularly complicated to model due to additional components, systems, and operation modes. This methodology has been validated to represent conventional, battery electric, and parallel hybrid electric vehicles. A sensitivity study demonstrates that the model is capable of differentiating powertrains with different parameters and routes with different characteristics. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:8230 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/71635 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | powertrain modeling | en |
| dc.subject | ecorouting | en |
| dc.subject | scalable powertrain components | en |
| dc.subject | fuel economy | en |
| dc.subject | energy consumption | en |
| dc.subject | hybrid electric vehicle | en |
| dc.subject | plug-in | en |
| dc.subject | battery electric vehicle | en |
| dc.subject | environment | en |
| dc.subject | greenhouse gases | en |
| dc.subject | petroleum | en |
| dc.title | Vehicle powertrain model to predict energy consumption for ecorouting purposes | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Mechanical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |