 A state of charge estimation method for lithium-ion batteries based on fractional order adaptive extended kalman filterQiao Zhu, Mengen Xu, Weiqun Liu and Mengqian Zheng Energy, 2019, vol. 187, issue C Abstract: This paper focuses on the state of charge (SOC) estimation of a lithium-ion battery based on a fractional-order adaptive extended Kalman filter (FO-AEKF). First, a fractional order model (FOM) is introduced to describe the physical behavior of the battery which is superior than the integral-order model (IOM), because there are diffused and decentralized characteristics in battery inner parameters. Then, the parameters of the FOM are identified by a genetic algorithm which can realize optimal parameter identification. After that, the FO-AEKF algorithm is developed, which combines the advantages of the FOM and the adaptive strategy. Consequently, the FO-AEKF can quickly track the unknown and time-invariant (or slow time-varying) noise variance and then improve the accuracy of SOC estimation. Finally, two types of lithium-ion batteries and two dynamic operation conditions are given to show the efficiency of the FO-AEKF by comparing with the extended Kalman filter (EKF) for FOM and the adaptive extended Kalman filter (AEKF) for IOM. Keywords: Lithium-ion battery; State of charge estimation; Fractional order model; Genetic algorithm; Adaptive extended kalman filter (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:187:y:2019:i:c:s036054421931552x DOI: 10.1016/j.energy.2019.115880 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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