| NSF Org: |
TI Translational Impacts |
| Recipient: |
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| Initial Amendment Date: | May 1, 2020 |
| Latest Amendment Date: | September 20, 2023 |
| Award Number: | 1951197 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Benaiah Schrag
bschrag@nsf.gov (703)292-8323 TI Translational Impacts TIP Directorate for Technology, Innovation, and Partnerships |
| Start Date: | May 1, 2020 |
| End Date: | January 31, 2026 (Estimated) |
| Total Intended Award Amount: | $750,000.00 |
| Total Awarded Amount to Date: | $1,599,526.00 |
| Funds Obligated to Date: |
FY 2021 = $199,881.00 FY 2023 = $649,645.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
903 GROGANS MILL DR CARY NC US 27519-7175 (919)985-4723 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
2 Davis Drive Durham NC US 27709-0003 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
STTR Phase II, SBIR Phase II |
| Primary Program Source: |
01002021DB NSF RESEARCH & RELATED ACTIVIT 01002122DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.084 |
ABSTRACT
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to develop a modeling, simulation and optimization software for fleet electrification projects. Electric vehicles (EVs) are expected to comprise 70% of all new buses and 15% of all commercial trucks by 2030. Electric vehicles are more expensive than diesel buses and need additional investments in charging infrastructure; furthermore, electrification is complex as several factors influence its design, cost and performance. The transition from diesel to electric buses could impose significant loads on the local electrical network, entailing significant upgrades to the electrical infrastructure at the facility and the utility grid. The proposed software will offer the electric vehicle industry a platform to analyze the battery, charging infrastructure, and energy infrastructure.
This Small Business Innovation Research (SBIR) Phase II project addresses the problem of planning and operating electric vehicle fleets, especially medium and heavy-duty fleets. The technology uses stochastic optimization and discrete event simulation to optimize fleet sizes to minimize costs and meet operational requirements. The proposed work will create a model of the joint transportation and energy processes (i.e., the driving and charging processes). The proposed software will enable real-time optimization of system operations.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Please report errors in award information by writing to: awardsearch@nsf.gov.
