  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 battery model that
aligns with the current literature is a useful 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, 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. By
eliminating the need for offline training, the method can predict the onset of
instability for a variety of systems over a prediction horizon. The prediction
horizon to prognosticate the instability, alternatively, is considered as the
remaining useful life (RUL) metric. The framework is then employed to analyze
the health status of Li-ion batteries. Based on the results, it has
demonstrated that the DDP technique can accurately predict the onset of failure
of Li-ion batteries.
