Method for estimating capacity and predicting remaining useful life of lithium-ion battery

• We develop an integrated method for the capacity estimation and RUL prediction.
• A state projection scheme is derived for capacity estimation.
• The Gauss–Hermite particle filter technique is used for the RUL prediction.
• Results with 10 years’ continuous cycling data verify the effectiveness of the method.

Reliability of lithium-ion (Li-ion) rechargeable batteries used in implantable medical devices has been recognized as of high importance from a broad range of stakeholders, including medical device manufacturers, regulatory agencies, physicians, and patients. To ensure Li-ion batteries in these devices operate reliably, it is important to be able to assess the capacity of Li-ion battery and predict the remaining useful life (RUL) throughout the whole life-time. This paper presents an integrated method for the capacity estimation and RUL prediction of Li-ion battery used in implantable medical devices. A state projection scheme from the author’s previous study is used for the capacity estimation. Then, based on the capacity estimates, the Gauss–Hermite particle filter technique is used to project the capacity fade to the end-of-service (EOS) value (or the failure limit) for the RUL prediction. Results of 10 years’ continuous cycling test on Li-ion prismatic cells in the lab suggest that the proposed method achieves good accuracy in the capacity estimation and captures the uncertainty in the RUL prediction. Post-explant weekly cycling data obtained from field cells with 4–7 implant years further verify the effectiveness of the proposed method in the capacity estimation.