EconPapers    
Economics at your fingertips  
 

A coulombic efficiency-based model for prognostics and health estimation of lithium-ion batteries

Fangfang Yang, Xiangbao Song, Guangzhong Dong and Kwok-Leung Tsui

Energy, 2019, vol. 171, issue C, 1173-1182

Abstract: Coulombic efficiency, as an important battery parameter, is highly related to the loss of lithium inventory, which is the dominant aging factor for lithium-ion batteries. In this paper, a semi-empirical model is derived from this relationship to capture the capacity degradation of lithium-ion batteries. The coulombic efficiency-based model effectively captures the convex degradation trend of lithium iron phosphate batteries and presents better fitting performance than the existing square-root-of-time model. To evaluate the proposed model, a battery cycle life experiment was designed, in which the subjects were continuously cycled under a federal urban driving schedule to simulate real-life battery usage. To perform online battery health estimation and prognostics, a particle filtering framework incorporating the proposed model was constructed to update the model parameters regularly with periodically measured data. Remaining useful life of the battery was then predicted by extrapolating the models with renewed parameters. The experimental results indicated that the proposed prognostic method can provide higher prediction accuracy than the existing square-root-of-time model.

Keywords: Degradation modeling; Coulombic efficiency; Lithium iron phosphate batteries; Loss of lithium inventory; Remaining useful life prediction; Health estimation (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544219300854
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:171:y:2019:i:c:p:1173-1182

DOI: 10.1016/j.energy.2019.01.083

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 ().

 
Page updated 2025-03-19
Handle: RePEc:eee:energy:v:171:y:2019:i:c:p:1173-1182