Physics-based prognostics of implantable-grade lithium-ion battery for remaining useful life prediction

Abstract Accurately predicting the remaining useful life (RUL) of a lithium-ion battery is essential for health management of both the battery and its host device. We propose a physics-based prognostics approach for prediction of the capacity and RUL of an implantable-grade lithium-ion battery by simultaneously considering multiple degradation mechanisms, including the losses of active materials of the positive and negative electrodes and the loss of lithium inventory. Unlike traditional capacity-based prognostics that exclusively relies on the empirical capacity fade trend, the proposed approach leverages a half-cell model to 1) estimate degradation parameters from voltage and capacity measurements to quantify the degradation mechanisms and 2) predict the capacity fade trend based on the estimated parameters. We compare the performance of the proposed physics-based approach with that of the traditional capacity-based approach on eight implantable-grade lithium-ion cells that have been subjected to continuous charge–discharge cycling over 1.5 years at high temperature. The proposed approach achieves a more accurate RUL prediction than the traditional capacity-based approach. The results show that the proposed physics-based approach, which extrapolates the degradation parameters, can provide a more accurate and conservative RUL prediction when compared to extrapolating just the capacity.