Assessing Useful Remaining Life of Lithium (Li)-Ion Batteries After Deep Discharges
Small Business Information
GLOBAL TECHNOLOGY CONNECTION, INC.
2839 Paces Ferry Rd. Suite 1160, Atlanta, GA, 30339
AbstractThe objective of this program is to develop the necessary algorithms to determine the useful remaining life of Li-ion batteries after deep discharges below 2.0 volts/cell. This proposal presents the background and work necessary to develop and implement prototype remaining useful life models for JSF Li-ion batteries within the JSF Prognostic Health Maintenance (PHM) architecture under the sponsorship of the Navair JSF program office. In Phase I, data-driven prognostics architecture was proposed in consultation with US Navy to address self-learning prognostics, nonlinear modeling and uncertainty management. Preliminary remaining useful life (RUL) model parameters were determined and a strategy was developed for collecting the information required to update battery health status after deep and lengthy discharges during abnormal operating conditions, shipping or storage. A wavelet neural network based diagnostician and prognosticator were prototyped from simulated deep discharge data to assess the feasibility of detecting damaged Li-ion batteries and predicting the remaining useful life of Li-ion batteries. The goal of Phase II is to develop and implement prototype RUL models for deep discharged Li-ion cells within the JSF PHM architecture. This includes the development, documentation and verification of air vehicle, battery and Autolog parameters and interfaces necessary to update battery health status within the overall JSF architecture. An extensive cycle testing of representative cells or batteries will be conducted to develop and validate detailed RUL models to account for depth and duration of discharge below 2.0V/cell. Aggressive commercialization and technology transition plans (Phase III) will be pursued teaming with our industrial partners.
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