  Lithium-ion battery (Li-ion) is becoming the dominant energy storage solution
in many applications such as hybrid electric and electric vehicles, due to its
higher energy density and longer life cycle. For these applications, the
battery should perform reliably and pose no safety threats. However, the
performance of Li-ion batteries can be affected by abnormal thermal behaviors,
defined as faults. It is essential to develop reliable thermal management
system to accurately predict and monitor thermal behaviors of Li-ion battery.
Using the first-principle models of batteries, this work presents a stochastic
fault detection and diagnosis (FDD) algorithm to identify two particular faults
in the Li-ion battery cells, using easily measured quantities such as
temperatures. Models of Li-ion battery are typically derived from the
underlying physical phenomena. To make model tractable and useful, it is common
to make simplifications during model development, which may consequently
introduce mismatch between models and battery cells. Further, FDD algorithms
can be affected by uncertainty, which may originate from either intrinsic time
varying phenomena or model calibration with noisy data. A two-step FDD
algorithm is developed in this work to correct model of Li-ion battery cells
and to identify faulty operations from a normal operating condition. An
iterative optimization problem is proposed to correct the model by
incorporating the errors between measured quantities and model predictions,
which is followed by an optimization-based FDD to provide a probabilistic
description of the occurrence of possible faults, while taking the uncertainty
into account. The two-step stochastic FDD algorithm in this work is shown to be
efficient in terms of fault detection rate for both individual and simultaneous
faults in Li-ion batteries, as compared to Monte Carlo (MC) simulations.
