Prognostics of lithium-ion batteries using a deterministic Bayesian approach

Lithium-ion batteries are popular for a wide variety of applications owing to their high energy/power density, long cycle life, and low self-discharge rate. A battery management system (BMS) can ensure the reliability and safety of batteries. As an important part of a BMS, prognostics and health management (PHM) can predict the failure time of batteries. This paper presents a new approach for battery prognostics based on a deterministic Bayesian approach. This approach can provide a probability density function (PDF) for the failure cycle. Based on the experiments, the battery capacity data collected under charge-discharge cycling conditions was used to validate the developed algorithm. The prediction results are updated over time as more data become available, which leads to an increase in prognostic accuracy. The prediction results provide a guideline for maintenance and replacement of batteries in electric vehicles (EVs).