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Differential Expansion and Voltage Model for Li-ion Batteries at Practical Charging Rates
Author(s) -
Peyman Mohtat,
Suhak Lee,
Valentin Sulzer,
Jason B. Siegel,
Anna G. Stefanopoulou
Publication year - 2020
Publication title -
journal of the electrochemical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.258
H-Index - 271
eISSN - 1945-7111
pISSN - 0013-4651
DOI - 10.1149/1945-7111/aba5d1
Subject(s) - unobservable , observable , voltage , graphite , materials science , electrode , work (physics) , smoothing , differential (mechanical device) , computer science , mechanics , physics , biological system , chemistry , analytical chemistry (journal) , electrical engineering , mathematics , thermodynamics , composite material , engineering , econometrics , chromatography , quantum mechanics , computer vision , biology
Differential voltage analysis (DVA) is a conventional approach for estimating capacity degradation in batteries. During charging, a graphite electrode goes through several phase transitions observed as plateaus in the voltage response. The transitions between these plateaus emerge as observable peaks in the differential voltage. The DVA method utilizes these peaks for estimating cell degradation. Unfortunately, at higher C-rates (above C/2) the peaks flatten and become unobservable. In this work, we show that, unlike the differential voltage, the peaks in the 2nd derivative of the expansion with respect to capacity remain observable up to 1C and thus make possible diagnostic algorithms at these charging rates. To understand why that is the case, we have developed an electrochemical and expansion model suitable for model-based estimation. In particular, we demonstrate that the single particle modeling methodology is not able to capture the peak smoothing effect, therefore a multi-particle approach for the graphite electrode is needed. Additionally, model parameters are identified using experimental data from a graphite/NMC pouch cell. The proposed model produces an excellent fit for the observed electric and mechanical swelling response of the cells and could enable physics-based data-driven degradation studies at practical charging rates.

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