Kouhestani, H. S., Liu, L., Wang, R., and Chandra, A. (2023).  Data-driven Prognosis of Failure Detection and Prediction of Lithium-ion Batteries.  Journal of Energy Storage, 70, 108045.

Abstract

Battery prognostics and health management predictive models are essential components of safety and reliability protocols in battery management system frameworks. Overall, developing a robust and efficient fault diagnostic battery model that aligns with the current literature is an essential step in ensuring the safety of battery function. For this purpose, a multi-physics, multi-scale deterministic data-driven prognosis (DDP) is proposed that only relies on in situ measurements of data and estimates the failure based on the curvature information extracted from the system. Unlike traditional applications that require explicit expression of conservation principle to represent the system's behavior, the proposed method devices a local conservation functional in the neighborhood of each data point which is represented as the minimization of curvature in the system. Pursuing such a deterministic approach, DDP eliminates the need for offline training regimen by considering only two consecutive time instances to make the prognostication that are sufficient to extract the behavioral pattern of the system. The developed framework is then employed to analyze the health of lithium ion batteries by monitoring the performance and detecting faults within the system's behavior. Based on the outcomes, the DDP exhibits promising results in detection of anomaly and prognostication of batteries' failure.