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On-Board Analysis of Degradation Mechanisms of Lithium-Ion Battery Using Differential Voltage Analysis

[+] Author Affiliations
Chao Hu, Mingyi Hong, Yifei Li, Ha-Lim Jeong

Iowa State University, Ames, IA

Paper No. DETC2016-59389, pp. V02AT03A044; 9 pages
doi:10.1115/DETC2016-59389
From:
  • ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2A: 42nd Design Automation Conference
  • Charlotte, North Carolina, USA, August 21–24, 2016
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5010-7
  • Copyright © 2016 by ASME

abstract

Reliability of lithium-ion (Li-ion) rechargeable batteries has been recognized as of high importance from a broad range of stakeholders, including manufacturers of battery-powered devices, regulatory agencies, researchers and the general public. Failures of Li-ion batteries could result in enormous economic losses and catastrophic events. To enable early identification and resolution of reliability issues and proactive prevention of failures, it is important to be able to diagnose, in a quantitative manner, degradation mechanisms of individual battery cells while the cells are in operation. This paper proposes a methodological framework for on-board quantitative analysis of degradation mechanisms of Li-ion battery using differential voltage analysis. In the framework, the task of on-board degradation analysis is decomposed into two phases: 1) offline high precision characterization of half-cell differential voltage (dV/dQ) behavior, which collects high precision voltage (V) and capacity (Q) data from positive and negative electrode half-cells; and 2) online (on-board) quantitative analysis of degradation mechanisms, which adopts recursive Bayesian filtering to online estimate degradation parameters based on measurement of full-cell dV/dQ curve. These degradation parameters quantify the degrees of degradation from the mechanisms. Simulation results obtained from LiCoO2/graphite Li-ion cells verify the effectiveness of the proposed framework in online estimation of the degradation parameters.

Copyright © 2016 by ASME

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