Abstract
Vehicle electrification plays a central role in reducing global energy use and greenhouse gas emissions. Predicting electric vehicle (EV) energy use for future transportation networks is critical for the planning, design, and operations of sustainable transportation systems. However, there is currently a lack of EV energy modeling approaches that are fully-scalable to large transportation network applications and consider actual on-road vehicle operating conditions. Such an approach is required for the accurate assessment of EV energy impact under various transportation scenarios. Here we present a simulation-based quasi-statistical approach to estimate EV energy consumption under various on-road vehicle operating conditions. In this approach, a Bayesian Network method is used to integrate outputs from full-system vehicle simulation tools for specific makes and models of EVs under a wide-variety of on-road operating conditions. These outputs are used to develop inference models that greatly improve computational efficiency, while maintaining most of the prediction accuracy of the complete system models. This approach is both highly scalable and transferable for analyzing the energy impact of EV fleet deployment in different regions, can facilitate the estimation of network-level EV energy consumption, and can be incorporated into a wide-variety of transportation planning models. In our case study of Atlanta, GA, the results indicate that if 6.2% of urban travel distances and 4.9% of rural travel distances were to be driven by EVs, regional fuel savings would be around 4.0% for a typical travel day in 2024.
| Original language | English |
|---|---|
| Article number | 115095 |
| Journal | Applied Energy |
| Volume | 269 |
| DOIs | |
| State | Published - Jul 1 2020 |
| Externally published | Yes |
Funding
This work was supported by the National Center for Sustainable Transportation, a National University Transportation Center sponsored by the U.S. Department of Transportation (DOT 69A3551747114). The contents of this paper reflect the view of the authors, who are responsible for the facts and accuracy of the data presented herein. The contents do not necessarily reflect the official view or policies of the Department of Transportation. This paper does not constitute a standard, specification, or regulation. The research team would like to acknowledge the Atlanta Regional Commission for providing the regional models and data for this project, as well as Argonne National Laboratory the use of Autonomie. The research team also acknowledges Oak Ridge National Laboratory and US Department of Energy for their support. The software that produces this work can be freely accessed at https://github.com/arielgatech/EV_energy_model, under the GPL-3.0 license. This work was supported by the National Center for Sustainable Transportation, a National University Transportation Center sponsored by the U.S. Department of Transportation (DOT 69A3551747114 ). The contents of this paper reflect the view of the authors, who are responsible for the facts and accuracy of the data presented herein. The contents do not necessarily reflect the official view or policies of the Department of Transportation . This paper does not constitute a standard, specification, or regulation. The research team would like to acknowledge the Atlanta Regional Commission for providing the regional models and data for this project, as well as Argonne National Laboratory the use of Autonomie. The research team also acknowledges Oak Ridge National Laboratory and US Department of Energy for their support.
| Funders | Funder number |
|---|---|
| Atlanta Regional Commission | |
| National Center for Sustainable Transportation | |
| National University Transportation Center | |
| US Department of Energy | |
| U.S. Department of Transportation | DOT 69A3551747114 |
| Argonne National Laboratory | |
| Oak Ridge National Laboratory |
Keywords
- Bayesian Network
- Electric vehicle
- Regional-scale energy prediction
- Transportation network
- Vehicle drivetrain simulation
