 Fault diagnosis of voltage sensor and current sensor for lithium-ion battery pack using hybrid system modeling and unscented particle filterChangwen Zheng, Ziqiang Chen and Deyang Huang Energy, 2020, vol. 191, issue C Abstract: Fault diagnosis is very critical for battery management systems. This paper proposes a fault diagnosis method for voltage sensor and current sensor in Lithium-ion battery pack system using hybrid system modeling and unscented particle filter. Stochastic hybrid automata model the battery pack system as a hybrid system to process simultaneously the continuous variables including state of charge and voltages, and discrete dynamics including faulty modes and normal modes. The unscented particle filter algorithm, which is responsible for the computation of the hybrid system states or modes, is also used to estimate both discrete states and continuous states and output diagnosis results. By using a serial-parallel configuration battery pack, the experimental validation is conducted in different fault scenarios of voltage sensor and current sensor. The results indicate that the method proposed in this paper not only has effective state tracking ability but also achieves accurate diagnosis to the Lithium-ion battery system sensor faults. Keywords: Sensor fault diagnosis; Lithium-ion battery pack; Hybrid systems; Unscented particle filter; Battery management system (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:191:y:2020:i:c:s0360544219321991 DOI: 10.1016/j.energy.2019.116504 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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