Model-Based Multi-Fault Diagnosis for Lithium-Ion Battery
Systems 2022-01-7034
Accurate fault diagnosis is critical to the safe and efficient operation of
lithium-ion battery systems. However, various faults in battery systems are
difficult to detect and isolate due to their similar features. This paper
proposes a model-based multi-fault diagnosis method to detect and isolate the
current, voltage, and temperature sensor faults, short circuit faults, and
connection faults in the lithium-ion battery systems. An electro-thermal model
with fault information is established and used to construct the structural
model. Structural analysis theory is applied to design diagnostic tests
sensitive to multiple faults. To improve the accuracy and robustness of residual
generation, the adaptive extended Kalman filter is introduced to battery state
estimation. The multi-fault detection and isolation are implemented using
residual evaluation based on the cumulative sum algorithm. Furthermore, a fault
indicator used to distinguish short circuit and connection faults is presented
with low computational complexity. The diagnostic results of various fault tests
show that the proposed diagnostic method can accurately detect and isolate
multiple faults without changing the voltage measurement topology of the battery
pack.