Journal
IEEE TRANSACTIONS ON POWER ELECTRONICS
Volume 37, Issue 1, Pages 971-989Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPEL.2021.3098445
Keywords
Circuit faults; Batteries; Voltage measurement; Integrated circuit modeling; Entropy; Temperature measurement; State of charge; Entropy; lithium-ion battery; multifault detection and isolation; short-circuit and connection fault; structural analysis
Categories
Funding
- National Natural Science Foundation of China [51875054, U1864212, 51775063]
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This article proposes an online multifault diagnosis strategy based on the fusion of model-based and entropy methods to effectively detect and isolate multiple types of faults in the lithium-ion battery system. The proposed method demonstrates good reliability and robustness.
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.
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