Abstract
The HIVE™ platform is a mobility services simulation platform developed to provide insight on the energy, infrastructure, service, and economic outcomes of various mobility as a service (MaaS) options. The HIVE platform takes a set of spatiotemporal travel origin-destination pairs and simulates the operation of a predefined mobility service fleet, incorporating request pooling, and various operational and charging behaviors. Hive specializes at modeling fleets of automated electric vehicles (AEVs) and can be used to site and size direct current fast charge (DCFC) stations and measure grid impacts of large-scale AEV fleets serving real-world MaaS trip demand (similar to taxis, Uber, Lyft, etc.). Potential outcomes from a Hive simulation include level of service, total vehicle miles traveled (VMT), deadheading (zero passenger) miles, simultaneous and total energy loads, average occupancy, and more. Hive is developed to generalize to new regions and can be customized to handle many scenarios and operating conditions.
- Developers:
-
Rames, Clement [1] ; Borlaug, Brennan [1] ; Moniot, Matthew [1] ; Reinicke, Nicholas [1] ; Grushka, Thomas [1] ; Holden, Jacob [1] ; Wood, Eric [1] ; Kontou, Eleftheria [1] ; Fitzgerald, Robert [1] ; Hoshiko, Joshua [1]
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Release Date:
- 2022-09-23
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
Python
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOE Laboratory Directed Research and Development (LDRD) ProgramPrimary Award/Contract Number:AC36-08GO28308
- Code ID:
- 93368
- Site Accession Number:
- NREL SWR-19-36
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Country of Origin:
- United States
Citation Formats
Rames, Clement, Borlaug, Brennan, Moniot, Matthew, Reinicke, Nicholas, Grushka, Thomas, Holden, Jacob, Wood, Eric, Kontou, Eleftheria, Fitzgerald, Robert, and Hoshiko, Joshua.
HIVE™ [SWR-19-36].
Computer Software.
https://github.com/NREL/hive.
USDOE Laboratory Directed Research and Development (LDRD) Program.
23 Sep. 2022.
Web.
doi:10.11578/dc.20221031.1.
Rames, Clement, Borlaug, Brennan, Moniot, Matthew, Reinicke, Nicholas, Grushka, Thomas, Holden, Jacob, Wood, Eric, Kontou, Eleftheria, Fitzgerald, Robert, & Hoshiko, Joshua.
(2022, September 23).
HIVE™ [SWR-19-36].
[Computer software].
https://github.com/NREL/hive.
https://doi.org/10.11578/dc.20221031.1.
Rames, Clement, Borlaug, Brennan, Moniot, Matthew, Reinicke, Nicholas, Grushka, Thomas, Holden, Jacob, Wood, Eric, Kontou, Eleftheria, Fitzgerald, Robert, and Hoshiko, Joshua.
"HIVE™ [SWR-19-36]." Computer software.
September 23, 2022.
https://github.com/NREL/hive.
https://doi.org/10.11578/dc.20221031.1.
@misc{
doecode_93368,
title = {HIVE™ [SWR-19-36]},
author = {Rames, Clement and Borlaug, Brennan and Moniot, Matthew and Reinicke, Nicholas and Grushka, Thomas and Holden, Jacob and Wood, Eric and Kontou, Eleftheria and Fitzgerald, Robert and Hoshiko, Joshua},
abstractNote = {The HIVE™ platform is a mobility services simulation platform developed to provide insight on the energy, infrastructure, service, and economic outcomes of various mobility as a service (MaaS) options. The HIVE platform takes a set of spatiotemporal travel origin-destination pairs and simulates the operation of a predefined mobility service fleet, incorporating request pooling, and various operational and charging behaviors. Hive specializes at modeling fleets of automated electric vehicles (AEVs) and can be used to site and size direct current fast charge (DCFC) stations and measure grid impacts of large-scale AEV fleets serving real-world MaaS trip demand (similar to taxis, Uber, Lyft, etc.). Potential outcomes from a Hive simulation include level of service, total vehicle miles traveled (VMT), deadheading (zero passenger) miles, simultaneous and total energy loads, average occupancy, and more. Hive is developed to generalize to new regions and can be customized to handle many scenarios and operating conditions.},
doi = {10.11578/dc.20221031.1},
url = {https://doi.org/10.11578/dc.20221031.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20221031.1}},
year = {2022},
month = {sep}
}