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Fault Diagnosis of Lithium Battery based on Fuzzy Bayesian Network

Volume 14, Number 10, October 2018, pp. 2302-2311
DOI: 10.23940/ijpe.18.10.p6.23022311

Ran Li, Sibo Li, and Yongqin Zhou

Department of Electrical Engineering and Automation, Harbin University of Science and Technology, Harbin, 150080, China

(Submitted on May 22, 2018; Revised on July 14, 2018; Accepted on August 18, 2018)

Abstract:

With the development of battery technology, lithium batteries are widely applied to electrical vehicles. The generation of the lithium battery fault has certain complexity and uncertainty, and the quantity of lithium batteries's real-time data test point is low. In addition, the test data is incomplete. Therefore, a fault diagnosis method for lithium batteries is presented based on a fuzzy Bayesian network, and a fault diagnosis model is established combined with fuzzy mathematics and the Bayesian network. The data is fuzzified by fuzzy mathematics to obtain the membership of fault symptoms. The demand of date and computation complexity is reduced by the Leaky Noisy-OR Bayesian network model. If the amount of fault nodes is large, the demand of conditional probability is reduced greatly, from 2n to 2n, by applying the Bayesian network constructed by the model presented above. This method requires less diagnosis time and sample demand, and it has high quality of diagnosis as well as many other advantages. The fault diagnosis of lithium batteries is supported by this method.

 

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