Name
Abstract | ||
|---|---|---|
Eco-driving assistance systems that encourage drivers to engage in fuel-saving behavior are effective at improving energy-efficiency, with recent research directed towards incorporating predictive models of energy losses in these systems to optimize recommendations. In this article, we evaluate a predictive eco-driving assistance system on three powertrains: a combustion engine-driven vehicle, a parallel hybrid electric vehicle, and a battery electric vehicle. In each case, energy consumption is found by applying a quasi-static model to driving simulator data for a simulated route including urban, rural, and highway sections. We find that both assisted and unassisted eco-driving has a beneficial effect in all cases, with the assistance system leading to reductions in energy usage of 6.1%, 15.9%, and 16.6% for the combustion engine, hybrid electric, and battery electric powertrains, respectively. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/THMS.2021.3086057 | IEEE Transactions on Human-Machine Systems |
Keywords | DocType | Volume |
Driver assistance,eco-driving,electric vehicles,fuel-efficient driving,hybrid vehicles | Journal | 51 |
Issue | ISSN | Citations |
4 | 2168-2291 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xingda Yan | 1 | 0 | 0.34 |
| Craig Kevin Allison | 2 | 0 | 0.34 |
| Fleming, J. | 3 | 12 | 3.22 |
| Neville A. Stanton | 4 | 3 | 3.15 |
| Roberto Lot | 5 | 1 | 4.10 |