Low-Frequency Electrochemical Impedance Spectroscopy to Diagnose the Effect of the Aging Process on Mass Transfer in Cathode Particles
Bibliographic record
Abstract
Lithium ion batteries are widely used for small scale electronic applications and for large scale applications like electronic vehicles and grid energy storage. Hence, a key requirement to better predict battery performance is the development of advanced analysis techniques. Electrochemical impedance spectroscopy is a powerful tool to monitor the evolution of kinetic and mass transfer effects during cycling, as measurements can be performed without need to dissemble the battery cell. Moreover, measurements performed at very low frequencies (<1 mHz) can provide insightful information on mass transfer occurring at low rates, i.e. solid-state lithium ion diffusion. In the present study, we modelized impedances including finite-length internal diffusion for LiNixMnyCo1-x-yO2 of different ratios in NMC/graphite cells equipped with a gold wire reference electrode. This three-electrode Swagelok® type T-cell developed by Solchenbach and al. [1] allows for a reliable deconvolution of anode and cathode impedance. Thus, the evolution of the lithium ion diffusion coefficient into NMC secondary particles was monitored throughout the first charge and discharge of the battery cell. For this purpose, impedance analyses at frequencies from 200 kHz to 0.1 mHz were acquired at each ~5-10% state-of-charge gained (or lost during discharge). At first, the Warburg element of these spectra displayed a straight line of 45° corresponding to the semi-infinite diffusion. At lower frequencies the spectra subsequently showed a vertical line which corresponds to the diffusion layer thickness being much larger than the sphere radius. This indicated that the diffusion arrived at the very center of the particle and could not proceed any further (no dc current flowing). The electrical equivalent circuit including finite-length internal linear and spherical diffusion was used to fit the obtained spectra [2]. This model was previously applied by Rajabloo and al. [3] to LiFePO4 to develop a semi-empirical performance model and it was here modified to allow the diagnostic of NMC cathode material. This enabled estimation of the lithium ion diffusion coefficients at each state-of-charge. Therefore, we were able to observe the evolution of the solid-state diffusion coefficient during the very first charge/discharge cycle of the battery cell. This evolution was compared to those after a few charge/discharge cycles to determine the aging effect on mass transfer into cathode particles. We then accessed the characteristic behavior of the NMCs studied at various cycling conditions (i.e. cutoff potential and temperature). References: [1] S. Solchenbach, D. Pritzl, E. Kong, J. Landesfeind and H. A. Gasteiger, J. Electrochem. Soc., 163, A2265 (2016). [2] A. Lasia, Electrochemical impedance spectroscopy and its application, Eds. Springer, 112 (2014). [3] B. Rajabloo, A. Jokar, W. Wakem, M. Désilets and G. M. Brisard, J. Appl. Electrochem., 48(6), 663-674 (2018). Acknowledgements: This work is financially supported by the Fonds de recherche du Québec – Nature et technologies (FRQNT), the Ministry of International Relations and La Francophonie – Coopération bilatérale Québec-Bavière and Mitacs. Prof. Dr. Hubert A. Gasteiger, Dr. Bharatkumar Suthar and Dr. Daniel Pritzl are acknowledged for helpful discussions and insight during an internship at Technical University Munich (Chair of Technical Electrochemistry).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".