Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework

TLDR

The study investigates assessing remaining useful life for complex systems with inaccessible internal states. The authors employ a Bayesian framework that integrates equivalent circuit electrochemical models with statistical state‑transition, aging, and measurement fidelity models, and evaluate relevance vector machines and particle filters for life prediction and uncertainty estimation. The approach is validated on battery data, demonstrating its feasibility.

Abstract

This paper explores how the remaining useful life (RUL) can be assessed for complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. Consequently, inference and estimation techniques need to be applied on indirect measurements, anticipated operational conditions, and historical data for which a Bayesian statistical approach is suitable. Models of electrochemical processes in the form of equivalent electric circuit parameters were combined with statistical models of state transitions, aging processes, and measurement fidelity in a formal framework. Relevance vector machines (RVMs) and several different particle filters (PFs) are examined for remaining life prediction and for providing uncertainty bounds. Results are shown on battery data.