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• W2954242549 abstract "Abstract The lithium-ion battery is the critical component in the microgrid energy storage systems. Affected by factors such as abuse operation and aging, voltage fault including over-voltage and under-voltage may occur to battery, which implies more serious faults including short-circuit, thermal runaway and so on. Detecting the voltage fault accurately is critical for enhancing the safety of battery pack. Therefore, this paper presents a voltage fault detection method for lithium-ion battery pack using local outlier factor (LOF). The proposed method systematically incorporates a model-based system identification algorithm into an outlier detection algorithm. Firstly, the model parameters are identified to represent the battery dynamics, which indicate the fault condition of the battery. By doing this, the fault detection problem is converted into detecting abnormal parameter in the model parameter set. Then, the local outlier factor is utilized to describe the degree of abnormality of parameter by evaluating the local deviation of the observed data with respect to its neighbors. Finally, an outlier filter based on Grubbs criterion is utilized to detect the fault battery cell using calculated local outlier factors. The simulations and experimental results show that the proposed method can detect the fault accurately, which verify the effectiveness of the proposed method." @default.
• W2954242549 created "2019-07-12" @default.
• W2954242549 creator A5023704628 @default.
• W2954242549 creator A5040644418 @default.
• W2954242549 creator A5059567942 @default.
• W2954242549 creator A5071343023 @default.
• W2954242549 date "2019-11-01" @default.
• W2954242549 modified "2023-10-03" @default.
• W2954242549 title "Voltage fault detection for lithium-ion battery pack using local outlier factor" @default.
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• W2954242549 doi "https://doi.org/10.1016/j.measurement.2019.06.052" @default.
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