Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework

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 tinder operational conditions. Consequently, inference and estimation techniques need to he applied on indirect measurements, anticipated operational conditions, and historical data for which a Bayesian statistical approach is suitable. Models or 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 hounds. Results are shown on battery data.(1)