  This contribution presents a diagnosis scheme for batteries to detect and
isolate internal faults in the form of small parameter changes. This scheme is
based on an electrochemical reduced-order model of the battery, which allows
the inclusion of physically meaningful faults that might affect the battery
performance. The sensitivity properties of the model are analyzed. The model is
then used to compute residuals based on an unscented Kalman filter. Primary
residuals and a limiting covariance matrix are obtained thanks to the local
approach, allowing for fault detection and isolation by chi-squared statistical
tests. Results show that faults resulting in limited 0.15% capacity and 0.004%
power fade can be effectively detected by the local approach. The algorithm is
also able to correctly isolate faults related with sensitive parameters,
whereas parameters with low sensitivity or linearly correlated are more
difficult to precise.
