Analysis of discrete spectra of electrochemical noise of lithium power sources
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The Fourier, Daubechies, and Chebyshev transforms are used to analyze discrete spectra of electrochemical noise of lithium power sources under the open-circuit conditions. In the absence of trend of open-circuit voltage, all three approaches lead to similar estimates of intensity of discrete spectra of electrochemical noise of lithium power sources. A trend of open-circuit voltage has different effects on the Fourier, Daubechies, and Chebyshev spectra. The Fourier spectrum is most sensitive to a trend of open-circuit voltage; the Chebyshev spectrum is most resistant to the trend. The Daubechies spectrum occupies an intermediate position between the Fourier spectrum and the Chebyshev spectrum in the resistance to the trend of open-circuit voltage.
KeywordsLithium power sources Electrochemical noise Trend of open-circuit voltage Fourier discrete noise spectra Daubechies discrete noise spectra Chebyshev discrete noise spectra
This work was partially supported by the Russian Foundation for Basic Research, project no. 16-29-09375.
- 4.Giriga S, Mudali UK, Raju VR, Raj B (2005) Electrochemical noise technique for corrosion assessment-a review. Corros Rev 23(2–3):107–170Google Scholar
- 5.Huet F (2006) Electrochemical noise technique. In: Marcus P, Mansfeld F (eds) Analytical methods in corrosion science and engineering. Taylor & Francis Group, CRC Press, Boca Raton, p 508Google Scholar
- 15.Homborg AM, Tinga T, Zhang X, Van Westing EPM, Oonincx PJ, De Wit JHW, Mol JMC (2012) Time–frequency methods for trend removal in electrochemical noise data. Electrochim Acta 70:199–209Google Scholar
- 17.Vaseghi S (1996) Advanced signal processing and digital noise reduction. Wiley and B.G. Teubner, N.Y. Chapter 8Google Scholar
- 18.Naidu PS (1996) Modern spectrum analysis of time series. CRC Press, New YorkGoogle Scholar
- 19.Rao SS (2017) A course in time series analysis, technical report. Texas A&M University, College StationGoogle Scholar
- 22.Mallat S (1999) A wavelet tour of signal processing. Academic, New YorkGoogle Scholar
- 24.Gogin N, Hirversalo M (2007) On generating function of discrete Chebyshev polynomials. Technical Report No. 819. Turku Centre for Computer Science, TurkuGoogle Scholar
- 27.Grafov B, Klyuev A, Davydov A, Lukovtsev V (2017) Chebyshev’s noise spectroscopy for testing electrochemical systems. Bulg Chem Commun 49:102–105Google Scholar