Electric vehicles (EVs) have been identified as one of the necessary solutions to reduce the carbon footprint of transportation, a large source of greenhouse gas (GHG). As adoption of EVs and infrastructures to support them grow, formidable hurdles to achieving equitable economic growth and reliable transportation and energy system via effective management of EVs have been discovered. This opens major opportunities and challenges for spatial computing research. Equitable distribution of EV infrastructure in a broad region presents complicated spatial computing challenges with a great social impact. Spatial computing informed adoption and management of EVs will be essential to achieving the maximum carbon reduction through EVs along with a reliable transition to a renewable energy future. On the road, EV drivers may benefit from spatial computing to choose routes that take into account public fast-charging stations as well as energy needs of the route, such as speed, weather (e.g. air-conditioning, heating, and elevation changes). This paper presents open research questions of spatial computing related to EV management.
|Original language||English (US)|
|Title of host publication||Proceedings of the 15th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2022|
|Editors||Andy Berres, Kuldeep Kurte, Haowen Xu|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||4|
|State||Published - Nov 1 2022|
|Event||15th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2022 - Seattle, United States|
Duration: Nov 1 2022 → …
|Name||Proceedings of the 15th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2022|
|Conference||15th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2022|
|Period||11/1/22 → …|
Bibliographical noteFunding Information:
This material is based upon work supported by the National Science Foundation under Grants No. 1901099, the USDOE Advanced Research Projects Agency-Energy ARPA-E under Award No. DEAR0000795. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. We also thank Kim Koffolt, the Spatial Computing Research Group, and the T. E. Murphy Engine Lab for valuable comments and refinements.
© 2022 ACM.
- charger placement
- climate change
- electric vehicles
- grid reliability
- range prediction
- renewable energy