State of health prediction of lithium-ion batteries based on autoregression with exogenous variables model

Zhelin Huang, Fan Xu and Fangfang Yang

Energy, 2023, vol. 262, issue PB

Abstract: The gradually decreasing capacity of lithium-ion batteries can serve as a health indicator for tracking their degradation. Therefore, it is important to predict the capacity of future cycles to assess the health condition of lithium-ion batteries. According to electrochemical theory and the characteristics of the data curves, this paper proposes several ideas for feature extraction. A novel fusion prognostic framework is proposed, in which a data-driven time series prediction model is adopted and combined with extracted features for lithium-ion battery capacity prediction. The proposed method is based on an autoregression with an exogenous-variable model that can self-adaptively update at each cycle and then predict the state of health in the next cycle and cycles in the near future. Under the assumption that the historical capacity data is available, the experimental results showed that by using the proposed autoregression with exogenous variables model, the root mean square error, mean absolute error, and mean absolute percentage error of the prediction results were 0.000963, 0.000562, and 0.000584, respectively, which indicated that the prediction results were precise.

Keywords: AREV model; Feature extraction; State of health prediction; Lithium-ion battery; Data-driven method (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222023799
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:energy:v:262:y:2023:i:pb:s0360544222023799

DOI: 10.1016/j.energy.2022.125497

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

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