Battery State-of-Health estimation based on multiple charge and discharge features

Brian Ospina Agudelo, Walter Zamboni, Fabio Postiglione and Eric Monmasson

Energy, 2023, vol. 263, issue PA

Abstract: In this work we propose and evaluate the performance of several linear multifeature models for battery State-of-Health estimation. The models combine high current incremental capacity and dynamic resistance features, which can be obtained during partial constant current charge and discharge, respectively. We construct the models by including fixed sets of features or by applying features selection procedures based on statistical criteria. The proposed models are fitted and evaluated with data from three publicly available battery datasets, including batteries cycled using driving, randomised and fast charging profiles. During the test process, we assess the estimation improvement introduced by each multifeature model by evaluating the reduction of the mean squared error in the State-of-Health estimation with respect to two reference single-feature models already used in recent literature. The collinearity is quantified through the variance inflation factor to indicate the prediction reliability of each model. As main result of this analysis, we propose a simple two-features model as the best compromise between estimation improvement with respect single feature models, and collinearity reduction.

Keywords: Battery SoH estimation; Multiple regression models; Battery capacity indicators; Features selection; Variance inflation factor (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222025233
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:263:y:2023:i:pa:s0360544222025233

DOI: 10.1016/j.energy.2022.125637

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 ().