Extracting Quantitative Insights from electrochemical impedance spectra using Statistical Methods

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Nov 5, 2024
Pavle Boskoski Benjamin Königshofer Gjorgji Nusev Aljaž Ostrež Vanja Subotić

Abstract

Statistical analysis of electrochemical impedance spec- troscopy data provides a systematic way of detecting changes in electrochemical energy systems. Applying concepts of divergence measures directly on electrochemical impedance spectroscopy data, one can reliably detect and quantify statistically significant changes. The results is a set of high- lighted frequency bands where the measured impedance characteristics differ statistically significantly from a ref- erence curve. The approach is evaluated on a solid-oxide electrolyser cell operated under different conditions and proves to be sensitive to even the smallest changes. The complete numerical implementation and corresponding experimental data are available as supplementary material at https://portal.ijs.si/nextcloud/s/xTa2cmtfxXn2jSz

How to Cite

Boskoski, P., Königshofer, B., Nusev, G., Ostrež, A., & Subotić, V. (2024). Extracting Quantitative Insights from electrochemical impedance spectra using Statistical Methods. Annual Conference of the PHM Society, 16(1). https://doi.org/10.36001/phmconf.2024.v16i1.4057
Abstract 58 | PDF Downloads 37

##plugins.themes.bootstrap3.article.details##

Keywords

divergnece measures, solid-oxide fuel cells, electrochemical impedance spectroscopy, condition monitoring

References
Boškoski, P., Debenjak, A., & Boshkoska, B. M. (2017). Fast electrochemical impedance spectroscopy. Springer International Publishing. doi: 10.1007/978-3-319-53390 -2

Boškoski, P., & Debenjak, A. (2014, December). Optimal selection of proton exchange membrane fuel cell condition monitoring thresholds. Journal of Power Sources, 268, 692–699. doi: 10.1016/j.jpowsour.2014.06.110

Boškoski, P., Žnidariˇc, L., Gradišar, v., & SubotiÅLc, V. (2024, December). Probabilistic deconvolution for electrochemical impedance through variational bayesian inference. Journal of Power Sources, 622, 235359. Retrieved from http://dx.doi.org/10.1016/ j .jpowsour .2024 .235359 doi: 10 .1016/j .jpowsour.2024.235359

Ciucci, F. (2019, feb). Modeling electrochemical impedance spectroscopy. Current Opinion in Electrochemistry, 13, 132–139. doi: 10.1016/j.coelec.2018.12.003

Effat, M. B., & Ciucci, F. (2017, sep). Bayesian and hierarchical bayesian based regularization for deconvolving the distribution of relaxation times from electrochemical impedance spectroscopy data. Electrochimica Acta, 247, 1117–1129. doi: 10 .1016/j .electacta .2017 .07 .050

Fadaei, M., & Mohammadi, R. (2015, dec). A comprehensive simulation of gas concentration impedance for solid oxide fuel cell anodes. Energy Conversion and Management, 106, 93–100. doi: 10.1016/j.enconman .2015.08.073

Fang, Q., Blum, L., & Menzler, N. H. (2015). Performance and degradation of solid oxide electrolysis cells in stack. Journal of The Electrochemical Society, 162, F907-F912. doi: 10.1149/2.0941508jes

Kobayashi, K., & Suzuki, T. S. (2018, sep). Distribution of relaxation time analysis for non-ideal immittance spectrum: Discussion and progress. Journal of the Physical Society of Japan, 87(9), 094002. doi: 10.7566/jpsj.87.094002

Lasia, A. (2014). Electrochemical impedance spectroscopy and its applications. Springer New York. doi: 10.1007/ 978-1-4614-8933-7

Leonide, A., Sonn, V., Weber, A., & Ivers-Tiff.e, E. (2008). Evaluation and modeling of the cell resistance in anode-supported solid oxide fuel cells. Journal of The Electrochemical Society, 155, B36. doi: 10 .1149/ 1.2801372

Liu, J., & Ciucci, F. (2020, jan). The gaussian process distribution of relaxation times: A machine learning tool for the analysis and prediction of electrochemical impedance spectroscopy data. Electrochimica Acta, 331, 135316. doi: 10.1016/j.electacta.2019.135316

Maradesa, A., Py, B., Wan, T. H., Effat, M. B., & Ciucci, F. (2023, March). Selecting the regularization parameter in the distribution of relaxation times. Journal of The Electrochemical Society, 170(3), 030502. Retrieved from http://dx.doi.org/10.1149/ 1945-7111/acbca4 doi: 10.1149/1945-7111/ acbca4

Monje, C. A., Chen, Y., Vinagre, B. M., Xue, D., & Feliu- Batlle, V. (2010). Fractional-order systems and controls: fundamentals and applications. In (chap. Chapter 13: Numerical Issues and MATLAB Implementations for Fractional-order Control Systems). Springer Science & Business Media.

Nusev, G., JuriˇciÅLc, .., Gaberšˇcek, M., Moškon, J., & Boškoski, P. (2021). Fast impedance measurement of li-ion battery using discrete random binary excitation and wavelet transform. IEEE Access, 9, 46152–46165. doi: 10.1109/access.2021.3058368

Overschee, P. V., & Moor, B. D. (1993). Subspace algorithms for the stochastic identification problem. Automatica, 29(3), 649 - 660.

Papurello, D., Menichini, D., & Lanzini, A. (2017). Distributed relaxation times technique for the determination of fuel cell losses with an equivalent circuit model to identify physicochemical processes. Electrochimica Acta, 258, 98 - 109. doi: 10.1016/j.electacta.2017.10 .052

Pardo, L. (2005). Statistical Inference Based on Divergence Measures. Abingdon: CRC Press. Petrovcˇicˇ, J., Cˇ erne, S., & Dolanc, G. (2020). Power supply unit with diagnostic capabilities (Tech. Rep.). EU Horizon 2020. doi: 10.3030/735533

Riedel, M., Heddrich, M. P., & Friedrich, K. A. (2019, 2). Analysis of pressurized operation of 10 layer solid oxide electrolysis stacks. International Journal of Hydrogen Energy, 44, 4570-4581. doi: 10.1016/j.ijhydene .2018.12.168

Sadli, I., Urbain, M., Hinaje, M., Martin, J.-P., Ra.l, S., & Davat, B. (2010, dec). Contributions of fractional differentiation to the modelling of electric double layer capacitance. Energy Conversion and Management, 51(12), 2993–2999. doi: 10.1016/j.enconman.2010 .06.045

Sonn, V., Leonide, A., & Ivers-Tiff.e, E. (2008). Combined deconvolution and cnls fitting approach applied on the impedance response of technical ni/8ysz cermet electrodes. Journal of The Electrochemical Society, 155, B675. doi: 10.1149/1.2908860

Stepanˇciˇc, M., JuriˇciÅLc, .., & Boškoski, P. (2019, sep). Fault detection of fuel cell systems based on statistical assessment of impedance data. Energy Conversion and Management, 195, 76–85. doi: 10.1016/j.enconman .2019.05.004




SubotiÅLc, V., K.nigshofer, B., .ani JuriˇciÅLc, Kusnezoff, M., Schr.ttner, H., Hochenauer, C., & Boškoski, P. (2020). Detailed insight into processes of reversible solid oxide cells and stacks using drt analysis. Energy Conversion and Management, 226, 113509. doi: 10.1016/ j.enconman.2020.113509

Wan, T. H., Saccoccio, M., Chen, C., & Ciucci, F. (2015). Influence of the discretization methods on the distribution of relaxation times deconvolution: Implementing radial basis functions with drttools. Electrochimica Acta, 184, 483 - 499. doi: 10.1016/j.electacta.2015.09.097

Wasserman, L. (2006). All of nonparametric statistics. Springer New York. doi: 10.1007/0-387-30623-4

Yang, B., Wang, J., Zhang, M., Shu, H., Yu, T., Zhang, X., . . . Sun, L. (2020). A state-of-the-art survey of solid oxide fuel cell parameter identification: Modelling, methodology, and perspectives. Energy Conversion and Management, 213, 112856. doi: 10.1016/ j.enconman.2020.112856
Section
Technical Research Papers