A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries

Rui Xiong, Jinpeng Tian, Hao Mu and Chun Wang

Applied Energy, 2017, vol. 207, issue C, 372-383

Abstract: Degradation is a complex and intricate process which relates strongly to the state of health (SoH) of a lithium-ion battery. Due to the ambiguous mechanism and sensitivity to the objective factors of lithium-ion batteries, it is difficult to recognize the degradation state and monitor the SoH of a battery. A recognition method for the degradation state to estimate the remaining capacity online has been presented. First, through the analysis of the results of electrochemical impedance spectroscopy (EIS) tests at different SoHs, the degradation level can be detected by the EIS measurement. Second, according to the fractional order theory, an online parameter identification approach with the fractional order impedance model has been proposed for the degradation analysis. Third, the correlation between variation of parameters and degradation level is discussed and the SEI (Solid Electrolyte Interphase) resistance is extracted to predict the remaining capacity by selecting an appropriate fitting function. Finally, the effectiveness of the presented method is validated by the test data, and the estimation error of the remaining capacity can be guaranteed within 3%.

Keywords: Lithium-ion battery; Degradation recognition; Remaining capacity; Polarization resistance; Electrochemical impedance spectroscopy (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations: View citations in EconPapers (63)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261917306645
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:207:y:2017:i:c:p:372-383

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2017.05.124

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().