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A state of health estimation method for lithium-ion batteries based on voltage relaxation model

Fang, Q.; Wei, X.; Lu, T.; Dai, H.; Zhu, Jiangong

Abstract:
The state of health estimation for lithium-ion battery is a key function of the battery management system. Unlike the traditional state of health estimation methods under dynamic conditions, the relaxation process is studied and utilized to estimate the state of health in this research. A reasonable and accurate voltage relaxation model is established based on the linear relationship between time coefficient and open circuit time for a Li$_{1}$(NiCoAl)$_{1}$O$_{2}$-Li$_{1}$(NiCoMn)$_{1}$O$_{2}$/graphite battery. The accuracy and effectiveness of the model is verified under different states of charge and states of health. Through systematic experiments under different states of charge and states of health, it is found that the model parameters monotonically increase with the aging of the battery. Three different capacity estimation methods are proposed based on the relationship between model parameters and residual capacity, namely the α-based, β-based, and parameter–fusion methods. The validation of the three methods is verified with high accuracy. The results indicate that the capacity estimation error under most of the aging states is less than 1%. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000095684
Veröffentlicht am 26.06.2019
Originalveröffentlichung
DOI: 10.3390/en12071349
Scopus
Zitationen: 1
Coverbild
Zugehörige Institution(en) am KIT Institut für Angewandte Materialien - Energiespeichersysteme (IAM-ESS)
Publikationstyp Zeitschriftenaufsatz
Jahr 2019
Sprache Englisch
Identifikator ISSN: 1996-1073
KITopen-ID: 1000095684
Erschienen in Energies
Band 12
Heft 7
Seiten Art.-Nr.: 1349
Schlagworte voltage relaxation model; capacity estimation; lithium-ion battery; battery management system
Nachgewiesen in Scopus
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