Electrical engineering

Physics

Gene

Algorithm

Engineering

Residual

Voltage

Actuator

Fault detection and isolation

Artificial intelligence

Kalman filter

Power (physics)

Fault indicator

Control (management)

Chemistry

Reliability engineering

Electronic engineering

State of charge

Computer science

Battery (electricity)

Quantum mechanics

Control theory (sociology)

Biochemistry

Robustness (evolution)

IEEE Transactions on Power Electronics

Various faults in the lithium-ion battery system pose a threat to the performance and safety of the battery. However, early faults are difficult to detect, and false alarms occasionally occur due to similar features of the faults. In this article, an online multifault diagnosis strategy based on the fusion of model-based and entropy methods is proposed to detect and isolate multiple types of faults, including current, voltage, and temperature sensor faults, short-circuit faults, and connection faults. An interleaved voltage measurement topology is adopted to distinguish voltage sensor faults from battery short-circuit or connection faults. Based on the established comprehensive battery model, structural analysis is performed to develop diagnostic tests that are sensitive to different faults. Residual generation based on the extended Kalman filter and residual evaluation based on the statistical inference are conducted to detect and isolate sensor faults. Sample entropy is used to further distinguish between the short-circuit faults and connection faults. The effectiveness of the proposed diagnostic method is verified by multiple fault tests with different fault types and sizes. The results also show that the proposed method has good robustness to noise and inconsistencies in the state of charge and temperature.

Multi-fault Detection and Isolation for Lithium-Ion Battery Systems