SBIR-STTR Award

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.

Keywords:
Li-Ion Batteries, Modeling, Depth-Of-Discharge

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.

Keywords:
li-ion batteries, modeling, depth-of-discharge