Degradation mechanism analysis and State-of-Health estimation for lithium-ion batteries based on distribution of relaxation times

Qi Zhang, Dafan Wang*, Erik Schaltz, Daniel-Ioan Stroe, Alejandro Gismero, Bowen Yang

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

3 Citations (Scopus)

Abstract

Electrochemical Impedance Spectroscopy (EIS) is powerful tool to explain the degradation mechanisms estimate State-of-Health (SOH) of lithium-ion batteries. Recently, many online EIS measurement methods were proposed, which makes SOH estimations based on EIS data have more potential to achieve on-board applications. In this paper, the distribution of relaxation times (DRT) method is introduced to decouple the internal behaviors of lithium-ion batteries based on EIS data. And degradation mechanisms of all behaviors in both the cycling aging condition and the calendar aging condition are analyzed. In these analyses, similarities and differences of degradation mechanisms between these two aging conditions are discussed, which can provide the theoretical basis for developing battery degradation models. In SOH estimation section, correlation analyses between SOH and parameters extracted from DRT plots and EIS data are carried out and parameters showing linear relationship or piecewise linear relationship with SOH are selected from correlation analyses to achieve the highly accurate SOH estimation. Considering that State-of-Charge (SOC) of batteries may change when their EISs are
measured online, we try to set parameters from different SOC as inputs of the SOH estimation. In this case, the DRT-based SOH estimation method still has an enough accuracy. In the end, the advantage in terms of computational efficiency and estimation accuracy and the adaptability for different regression methods of the DRT-based SOH estimation method are discussed.
Original languageEnglish
Article number105386
JournalJournal of Energy Storage
Volume55
Number of pages21
ISSN2352-152X
DOIs
Publication statusPublished - 1 Nov 2022

Keywords

  • Lithium-ion battery
  • Distribution of relaxation time
  • Aging mechanism
  • State-of-health estimation

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