Name
Abstract | ||
|---|---|---|
Thanks to their affordability and practicality, electric bicycles (e-bikes) are becoming popular, especially in urban areas. They are a zero-emission and zero-carbon alternative to cars and therefore have a high potential to mitigate climate change. However, range anxiety can be a significant barrier to the adoption of electric vehicles. To address this challenge, in this paper we focus on how an e-bike's remaining range can be accurately predicted. Using real data from the University of Waterloo WeBike field trial, combined with OpenStreetMap data, we evaluate two range prediction methods that take riding behaviour and route characteristics into account. Surprisingly, we find that predicting range for a particular cyclist based on his or her past energy consumption works as well as more complex methods that include additional information such as the route being travelled. Our findings also reveal which additional hardware and sensors e-bike manufacturers should provide in the future to make it easier to implement on-board range prediction. To the best of our knowledge, this is the first study of range prediction specifically for e-bikes. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2934328.2934349 | e-Energy |
Field | DocType | Citations |
Climate change,Range anxiety,Transport engineering,Engineering,Field trial,Energy consumption | Conference | 2 |
PageRank | References | Authors |
0.46 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lukas Gebhard | 1 | 2 | 0.46 |
| Lukasz Golab | 2 | 1263 | 80.95 |
| Srinivasan Keshav | 3 | 3778 | 761.32 |
| Hermann de Meer | 4 | 1575 | 143.10 |