A mathematical approach to survey electrochemical impedance spectroscopy for aging in lithium-ion batteries

Chen, Bor-Rong; Police, Yugandhar R.; Li, Meng; Chinnam, Paramesh R.; Tanim, Tanvir R.; Dufek, Eric J.

doi:10.3389/fenrg.2023.1132876

A mathematical approach to survey electrochemical impedance spectroscopy for aging in lithium-ion batteries

Journal Article · Mon Feb 27 23:00:00 EST 2023 · Frontiers in Energy Research

DOI:https://doi.org/10.3389/fenrg.2023.1132876· OSTI ID:1960083

Chen, Bor-Rong; Police, Yugandhar R.; Li, Meng; Chinnam, Paramesh R.; Tanim, Tanvir R.; Dufek, Eric J.

Electrochemical impedance spectroscopy (EIS) is a valuable technique to detect the health status and aging phenomena in lithium-ion batteries (LiB). Equivalent circuit modeling (ECM) is conventionally used when interpreting EIS data and gaining physical insights into the aging mechanisms. However, performing ECM is resource intensive and expert-level of knowledge is usually required to select suitable models and fitting parameters. This article presents a quick and user-friendly data analysis algorithm as an alternative to ECM by mathematically fitting geometric features in Nyquist plots and obtaining the growth trends of the features. The evolving trends in the Nyquist plots, such as chord lengths of the arcs and interception points, are consistent with the growth of resistance components obtained using ECM with R ² values from 0.67 to 0.99, and therefore can be used as indicators of battery aging. Our results show that the quick-fitting approach is suitable for analyzing a series of EIS data acquired during battery cycling and identifying the underlying aging mechanisms.

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Sponsoring Organization:: USDOE

OSTI ID:: 1960083

Journal Information:: Frontiers in Energy Research, Journal Name: Frontiers in Energy Research Vol. 11; ISSN 2296-598X

Publisher:: Frontiers Media SACopyright Statement

Country of Publication:: Switzerland

Language:: English

References (39)

A Comprehensive Understanding of the Aging Effects of Extreme Fast Charging on High Ni NMC Cathode Tanim, Tanvir R.; Yang, Zhenzhen; Finegan, Donal P. Advanced Energy Materials, Vol. 12, Issue 22 https://doi.org/10.1002/aenm.202103712	journal	April 2022
Electrochemical Impedance Spectroscopy for All‐Solid‐State Batteries: Theory, Methods and Future Outlook Vadhva, Pooja; Hu, Ji; Johnson, Michael J. ChemElectroChem, Vol. 8, Issue 11 https://doi.org/10.1002/celc.202100108	journal	May 2021
Analysis of Lithium-Ion Battery Models Based on Electrochemical Impedance Spectroscopy Westerhoff, Uwe; Kurbach, Kerstin; Lienesch, Frank Energy Technology, Vol. 4, Issue 12 https://doi.org/10.1002/ente.201600154	journal	August 2016
Exploring the use of machine learning for interpreting electrochemical impedance spectroscopy data: evaluation of the training dataset size Bongiorno, V.; Gibbon, S.; Michailidou, E. Corrosion Science, Vol. 198 https://doi.org/10.1016/j.corsci.2022.110119	journal	April 2022
How the distribution of relaxation times enhances complex equivalent circuit models for fuel cells Dierickx, Sebastian; Weber, André; Ivers-Tiffée, Ellen Electrochimica Acta, Vol. 355 https://doi.org/10.1016/j.electacta.2020.136764	journal	September 2020
Towards robust autonomous impedance spectroscopy analysis: A calibrated hierarchical Bayesian approach for electrochemical impedance spectroscopy (EIS) inversion Huang, Jake; Papac, Meagan; O’Hayre, Ryan Electrochimica Acta, Vol. 367 https://doi.org/10.1016/j.electacta.2020.137493	journal	January 2021
State of charge prediction of EV Li-ion batteries using EIS: A machine learning approach Babaeiyazdi, Iman; Rezaei-Zare, Afshin; Shokrzadeh, Shahab Energy, Vol. 223 https://doi.org/10.1016/j.energy.2021.120116	journal	May 2021
Extended cycle life implications of fast charging for lithium-ion battery cathode Tanim, Tanvir R.; Yang, Zhenzhen; Colclasure, Andrew M. Energy Storage Materials, Vol. 41 https://doi.org/10.1016/j.ensm.2021.07.001	journal	October 2021
Rapid failure mode classification and quantification in batteries: A deep learning modeling framework Kim, Sangwook; Yi, Zonggen; Chen, Bor-Rong Energy Storage Materials, Vol. 45 https://doi.org/10.1016/j.ensm.2021.07.016	journal	March 2022
Advanced diagnostics to evaluate heterogeneity in lithium-ion battery modules Tanim, Tanvir R.; Dufek, Eric J.; Walker, Lee K. eTransportation, Vol. 3 https://doi.org/10.1016/j.etran.2020.100045	journal	February 2020
Equivalent circuit model recognition of electrochemical impedance spectroscopy via machine learning Zhu, Shan; Sun, Xinyang; Gao, Xiaoyang Journal of Electroanalytical Chemistry, Vol. 855 https://doi.org/10.1016/j.jelechem.2019.113627	journal	December 2019
A review of conduction phenomena in Li-ion batteries Park, Myounggu; Zhang, Xiangchun; Chung, Myoungdo Journal of Power Sources, Vol. 195, Issue 24 https://doi.org/10.1016/j.jpowsour.2010.06.060	journal	December 2010
Synthesize battery degradation modes via a diagnostic and prognostic model Dubarry, Matthieu; Truchot, Cyril; Liaw, Bor Yann Journal of Power Sources, Vol. 219 https://doi.org/10.1016/j.jpowsour.2012.07.016	journal	December 2012
Original implementation of Electrochemical Impedance Spectroscopy (EIS) in symmetric cells: Evaluation of post-mortem protocols applied to characterize electrode materials for Li-ion batteries Gordon, Isabel Jiménez; Genies, Sylvie; Si Larbi, Gregory Journal of Power Sources, Vol. 307 https://doi.org/10.1016/j.jpowsour.2016.01.036	journal	March 2016
Degradation diagnostics for lithium ion cells Birkl, Christoph R.; Roberts, Matthew R.; McTurk, Euan Journal of Power Sources, Vol. 341 https://doi.org/10.1016/j.jpowsour.2016.12.011	journal	February 2017
State of health battery estimator enabling degradation diagnosis: Model and algorithm description Dubarry, Matthieu; Berecibar, M.; Devie, A. Journal of Power Sources, Vol. 360 https://doi.org/10.1016/j.jpowsour.2017.05.121	journal	August 2017
Recent progress in the use of electrochemical impedance spectroscopy for the measurement, monitoring, diagnosis and optimization of proton exchange membrane fuel cell performance Tang, Zhepeng; Huang, Qiu-An; Wang, Yan-Jie Journal of Power Sources, Vol. 468 https://doi.org/10.1016/j.jpowsour.2020.228361	journal	August 2020
Application of electrochemical impedance spectroscopy to commercial Li-ion cells: A review Meddings, Nina; Heinrich, Marco; Overney, Frédéric Journal of Power Sources, Vol. 480 https://doi.org/10.1016/j.jpowsour.2020.228742	journal	December 2020
Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis Dubarry, Matthieu; Beck, David Journal of Power Sources, Vol. 479 https://doi.org/10.1016/j.jpowsour.2020.228806	journal	December 2020
On the use of electrochemical impedance spectroscopy to characterize and model the aging phenomena of lithium-ion batteries: a critical review Iurilli, Pietro; Brivio, Claudio; Wood, Vanessa Journal of Power Sources, Vol. 505 https://doi.org/10.1016/j.jpowsour.2021.229860	journal	September 2021
A machine learning framework for early detection of lithium plating combining multiple physics-based electrochemical signatures Chen, Bor-Rong; Kunz, M. Ross; Tanim, Tanvir R. Cell Reports Physical Science, Vol. 2, Issue 3 https://doi.org/10.1016/j.xcrp.2021.100352	journal	March 2021
Impedance Analysis of Electrochemical Systems Vivier, Vincent; Orazem, Mark E. Chemical Reviews, Vol. 122, Issue 12 https://doi.org/10.1021/acs.chemrev.1c00876	journal	June 2022
Fast-Charging Aging Considerations: Incorporation and Alignment of Cell Design and Material Degradation Pathways Chinnam, Parameswara R.; Colclasure, Andrew M.; Chen, Bor-Rong ACS Applied Energy Materials, Vol. 4, Issue 9 https://doi.org/10.1021/acsaem.1c01398	journal	September 2021
Using In Situ High-Energy X-ray Diffraction to Quantify Electrode Behavior of Li-Ion Batteries from Extreme Fast Charging Paul, Partha P.; Cao, Chuntian; Thampy, Vivek ACS Applied Energy Materials, Vol. 4, Issue 10 https://doi.org/10.1021/acsaem.1c02348	journal	October 2021
Electrochemical Impedance Spectroscopy of Metal Oxide Electrodes for Energy Applications Bredar, Alexandria R. C.; Chown, Amanda L.; Burton, Andricus R. ACS Applied Energy Materials, Vol. 3, Issue 1 https://doi.org/10.1021/acsaem.9b01965	journal	December 2019
Interfacial Processes and Influence of Composite Cathode Microstructure Controlling the Performance of All-Solid-State Lithium Batteries Zhang, Wenbo; Weber, Dominik A.; Weigand, Harald ACS Applied Materials & Interfaces, Vol. 9, Issue 21 https://doi.org/10.1021/acsami.7b01137	journal	May 2017
Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning Zhang, Yunwei; Tang, Qiaochu; Zhang, Yao Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-15235-7	journal	April 2020
Impedance-based forecasting of lithium-ion battery performance amid uneven usage Jones, Penelope K.; Stimming, Ulrich; Lee, Alpha A. Nature Communications, Vol. 13, Issue 1 https://doi.org/10.1038/s41467-022-32422-w	journal	August 2022
Data-driven prediction of battery cycle life before capacity degradation Severson, Kristen A.; Attia, Peter M.; Jin, Norman Nature Energy, Vol. 4, Issue 5 https://doi.org/10.1038/s41560-019-0356-8	journal	March 2019
SciPy 1.0: fundamental algorithms for scientific computing in Python Virtanen, Pauli; Gommers, Ralf; Oliphant, Travis E. Nature Methods https://doi.org/10.1038/s41592-019-0686-2	journal	February 2020
Electrochemical impedance spectroscopy Wang, Shangshang; Zhang, Jianbo; Gharbi, Oumaïma Nature Reviews Methods Primers, Vol. 1, Issue 1 https://doi.org/10.1038/s43586-021-00039-w	journal	June 2021
Quantification of heterogeneous, irreversible lithium plating in extreme fast charging of lithium-ion batteries Paul, Partha P.; Thampy, Vivek; Cao, Chuntian Energy & Environmental Science, Vol. 14, Issue 9 https://doi.org/10.1039/D1EE01216A	journal	January 2021
A Scalable Active Battery Management System With Embedded Real-Time Electrochemical Impedance Spectroscopy Din, Eric; Schaef, Christopher; Moffat, Keith IEEE Transactions on Power Electronics, Vol. 32, Issue 7 https://doi.org/10.1109/TPEL.2016.2607519	journal	July 2017
The Effect of Electrode-Electrolyte Interface on the Electrochemical Impedance Spectra for Positive Electrode in Li-Ion Battery Tatara, Ryoichi; Karayaylali, Pinar; Yu, Yang Journal of The Electrochemical Society, Vol. 166, Issue 3 https://doi.org/10.1149/2.0121903jes	journal	November 2018
Analysis of Thousands of Electrochemical Impedance Spectra of Lithium-Ion Cells through a Machine Learning Inverse Model Buteau, Sam; Dahn, J. R. Journal of The Electrochemical Society, Vol. 166, Issue 8 https://doi.org/10.1149/2.1051908jes	journal	January 2019
Challenges and needs for system-level electrochemical lithium-ion battery management and diagnostics Tanim, Tanvir R.; Dufek, Eric J.; Sazhin, Sergiy V. MRS Bulletin, Vol. 46, Issue 5 https://doi.org/10.1557/s43577-021-00101-8	journal	May 2021
impedance.py: A Python package for electrochemical impedance analysis Murbach, Matthew; Gerwe, Brian; Dawson-Elli, Neal Journal of Open Source Software, Vol. 5, Issue 52 https://doi.org/10.21105/joss.02349	journal	August 2020
EIS Study on the Electrode-Separator Interface Lamination Frankenberger, Martin; Singh, Madhav; Dinter, Alexander Batteries, Vol. 5, Issue 4 https://doi.org/10.3390/batteries5040071	journal	November 2019
Modeling and Applications of Electrochemical Impedance Spectroscopy (EIS) for Lithium-ion Batteries Choi, Woosung; Shin, Heon-Cheol; Kim, Ji Man Journal of Electrochemical Science and Technology, Vol. 11, Issue 1 https://doi.org/10.33961/jecst.2019.00528	journal	February 2020

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