  High power operation in extreme fast charging significantly increases the
risk of internal faults in Electric Vehicle batteries which can lead to
accelerated battery failure. Early detection of these faults is crucial for
battery safety and widespread deployment of fast charging. In this setting, we
propose a real-time {detection} framework for battery voltage and thermal
faults. A major challenge in battery fault detection arises from the effect of
uncertainties originating from sensor inaccuracies, nominal aging, or
unmodelled dynamics. Inspired by physics-based learning, we explore a detection
paradigm that combines physics-based models, model-based detection observers,
and data-driven learning techniques to address this challenge. Specifically, we
construct the {detection} observers based on an experimentally identified
electrochemical-thermal model, and subsequently design the observer tuning
parameters following Lyapunov's stability theory. Furthermore, we utilize
Gaussian Process Regression technique to learn the model and measurement
uncertainties which in turn aid the {detection} observers in distinguishing
faults and uncertainties. Such uncertainty learning essentially helps
suppressing their effects, potentially enabling early detection of faults. We
perform simulation and experimental case studies on the proposed fault
{detection} scheme verifying the potential of physics-based learning in early
detection of battery faults.
