Model-Based Multi-Fault Diagnosis for Lithium-Ion Battery Systems

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.