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A model for state-of-health estimation of lithium ion batteries based on charging profiles
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Chemical Engineering.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Chemical Engineering.ORCID iD: 0000-0001-6801-9208
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Chemical Engineering.
2019 (English)In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 177, p. 57-65Article in journal (Refereed) Published
Abstract [en]

Using an equivalent circuit model to characterize the constant-current part of a charging/discharging profile, a model is developed to estimate the state-of-health of lithium ion batteries. The model is an incremental capacity analysis-based model, which applies a capacity model to define the dependence of the state of charge on the open circuit voltage as the battery ages. It can be learning-free, with the parameters subject to certain constraints, and is able to give efficient and reliable estimates of the state-of-health for various lithium ion batteries at any aging status. When applied to a fresh LiFePO 4 cell, the state-of-health estimated by this model (learning-unrequired or learning-required)shows a close correspondence to the available measured data, with an absolute difference of 0.31% or 0.12% at most, even for significant temperature fluctuation. In addition, NASA battery datasets are employed to demonstrate the versatility and applicability of the model to different chemistries and cell designs.

Place, publisher, year, edition, pages
Elsevier Ltd , 2019. Vol. 177, p. 57-65
Keywords [en]
Equivalent circuit model, Incremental capacity analysis, Lithium ion battery, State of health
National Category
Chemical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-252451DOI: 10.1016/j.energy.2019.04.070ISI: 000471360100005Scopus ID: 2-s2.0-85064732053OAI: oai:DiVA.org:kth-252451DiVA, id: diva2:1337472
Note

QC 20190715

Available from: 2019-07-15 Created: 2019-07-15 Last updated: 2019-07-29Bibliographically approved

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Bian, XiaoleiLiu, LongchengYan, Jinying

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  • apa
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