State-of-life prognosis and diagnosis of lithium-ion batteries by data-driven particle filters

The aim of this study is that of presenting a new diagnostic and prognostic method aimed at automatically detecting deviations from the expected degradation dynamics of the batteries due to changes in the operating conditions, or, possibly, anomalous behaviors, and predicting their remaining useful life (RUL) in terms of their state-of-life (SOL), without needing to derive any complex physics-based models and/or gather huge amounts of experimental data to cover all possible operative/fault conditions. The proposed method in fact exploits the real time framework offered by particle filtering and resorts to neural networks in order to build a suitable parametric measurement equation, which provides the algorithm with the capability of automatically adjusting to different battery's dynamic behaviors. The results of this study demonstrate the satisfactory performances of the algorithm in terms of adaptability and diagnostic sensibility, with reference to suitably identified case studies based on actual Lithium-Ion battery capacity data taken from the prognostics data repository of the NASA Ames Research Center database and of the CALCE Battery Group.