Algorithm to Determine the Knee Point on Capacity Fade Curves of Lithium-Ion Cells

Weiping Diao, Saurabh Saxena, Bongtae Han and Michael Pecht
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Weiping Diao: Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA
Saurabh Saxena: Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA
Bongtae Han: Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA
Michael Pecht: Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA

Energies, 2019, vol. 12, issue 15, 1-9

Abstract: Lithium-ion batteries typically exhibit a transition to a more rapid capacity fade trend when subjected to extended charge–discharge cycles and storage conditions. The identification of the knee point can be valuable to identify the more severe degradation trend, and to provide guidance when scheduling battery replacements and planning secondary uses of the battery. However, a concise and repeatable determination of a knee point has not been documented. This paper provides a definition of the knee point which can be used as a degradation metric, and develops an algorithm to identify it. The algorithm is implemented on various data cases, and the results indicate that the approach provides repeatable knee point identification.

Keywords: lithium-ion batteries; capacity fade; algorithm; knee point identification; slope-changing ratio (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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