| NSF Org: |
TI Translational Impacts |
| Recipient: |
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| Initial Amendment Date: | July 12, 2022 |
| Latest Amendment Date: | July 6, 2023 |
| Award Number: | 2213918 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Samir M. Iqbal
smiqbal@nsf.gov (703)292-7529 TI Translational Impacts TIP Directorate for Technology, Innovation, and Partnerships |
| Start Date: | July 15, 2022 |
| End Date: | June 30, 2026 (Estimated) |
| Total Intended Award Amount: | $250,000.00 |
| Total Awarded Amount to Date: | $266,000.00 |
| Funds Obligated to Date: |
FY 2023 = $16,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
801 UNIVERSITY BLVD TUSCALOOSA AL US 35401 (205)348-5152 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
801 University Blvd. Tuscaloosa AL US 35478-0104 |
| 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): | PFI-Partnrships for Innovation |
| Primary Program Source: |
01002324DB 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 Partnerships for Innovation - Technology Translation (PFI-TT) project is to develop a battery health monitoring technology and a prototype system that can detect performance deterioration and possible battery failures more quickly and accurately than current technologies. Fast and accurate health monitoring of batteries impacts a wide range of applications and products and may have significant impact on the safety of materials and personnel. Applications and products such as electric and hybrid-electric vehicles, electric aircrafts, electric boats, green homes and buildings, off-power-grid homes and buildings, backup battery systems for data centers and computing infrastructure, communication systems, hospitals and grid or micro-grid scale energy storage are potential direct beneficiaries of this technology. The resiliency of power availability increasingly impacts daily lives and security. Fast and accurate state-of-health degradation evaluation technology is also important for repurposing batteries for second-life use.
The proposed project seeks to develop a battery health monitoring technology. The technology employs new battery health indicators, advanced algorithms such as Artificial Neural Networks, and measurement methods that are suitable for online applications and simultaneous multi-battery component monitoring with reduced cost and size. A proof-of-concept prototype will be developed during this project to demonstrate the competitive advantages of the technology such as accuracy, speed, cost, and size. The project seeks to evaluate and demonstrate the commercial potential and viability of the technology using many parameters. The proof-of-concept prototype will also be used to demonstrate the technology to potential industry collaborators and partners.
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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