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Modeling of Lithium-Ion Cell Performance

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0147-11-C-7664
Agency Tracking Number: B10B-004-0014
Amount: $149,459.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: MDA10-T004
Solicitation Number: 2010.B
Solicitation Year: 2010
Award Year: 2011
Award Start Date (Proposal Award Date): 2011-07-28
Award End Date (Contract End Date): N/A
Small Business Information
12744 San Fernando Road, Building 3, Sylmar, CA, -
DUNS: 071628460
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Mikito Nagata
 Program Manager
 (818) 833-2015
Business Contact
 Bryan McKinney
Title: Program Manager
Phone: (818) 833-4276
Research Institution
 University of South Carolina
 Ralph White
 College of Engineering and Com
columbia, SC, 29208-
 (803) 777-3270
 Nonprofit college or university
Quallion LLC is pleased to submit this proposal in response to the solicitation for"Modeling of Lithium-ion Cell Performance."This proposal focuses on the area of interest relating to"accelerated life testing for LEO because the processes that occur at the anode and cathode at high DoD"s (60+%) are poorly understood as to how they relate to low DoD"s (30% and lower)."Quallion has developed a reference electrode technology for acquiring detailed information on the life cycling of Li-ion cells. This technology coupled with the modeling capabilities of Dr. Ralph White and University of South Carolina can identify the key relationships of various DoD in cycling on Li-ion cell performance. The capability of predictive modeling of the effects of different DoDs could significantly reduce development time for introduction of lithium-ion batteries into MDA applications. Phase I will establish methods of incorporating reference electrode into cells, collect initial cycling data, and evaluate the methods of integrating data into the model. The modeling effort will evaluate two methods of predicting capacity fade. Phase II will develop long-term cycling data on test regimes and further develop the model capability for prediction of cycle life under different test regimes and DoDs.

* Information listed above is at the time of submission. *

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