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Fast Electrochemical Impedance Spectroscopy: As a Statistical Condition Monitoring Tool 1st ed. 2017 [Pehme köide]

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  • Formaat: Paperback / softback, 83 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 11 Illustrations, color; 32 Illustrations, black and white; XIII, 83 p. 43 illus., 11 illus. in color., 1 Paperback / softback
  • Sari: SpringerBriefs in Applied Sciences and Technology
  • Ilmumisaeg: 18-May-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319533894
  • ISBN-13: 9783319533896
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Fast Electrochemical Impedance Spectroscopy: As a Statistical Condition Monitoring Tool 1st ed. 2017Suurem pilt
  • Formaat: Paperback / softback, 83 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 11 Illustrations, color; 32 Illustrations, black and white; XIII, 83 p. 43 illus., 11 illus. in color., 1 Paperback / softback
  • Sari: SpringerBriefs in Applied Sciences and Technology
  • Ilmumisaeg: 18-May-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319533894
  • ISBN-13: 9783319533896
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319533896.html
Märksõnad:
This book offers a review of electrochemical impedance spectroscopy (EIS) and its application in online condition monitoring of electrochemical devices, focusing on the practicalities of performing fast and accurate EIS. The first part of the book addresses the theoretical aspects of the fast EIS technique, including stochastic excitation signals, time-frequency signal processing, and statistical analysis of impedance measurements. The second part presents an application of the fast EIS technique for condition monitoring and evaluates the performance of the proposed fast EIS methodology in three different types of electrochemical devices: a Li-ion battery, a Li-S cell, and a polymer electrolyte membrane (PEM) fuel cell.





Uniquely, in addition to theoretical aspects the book provides practical guidelines for implementation, commissioning, and exploitation of EIS for condition monitoring of electrochemical devices, making it a valuable resource for practicing engineers as well as researchers.
1 Introduction
1(8)
1.1 EIS as a Tool for Condition Monitoring
3(1)
1.2 Book Structure
4(5)
References
5(4)
2 Fast Electrochemical Impedance Spectroscopy
9(14)
2.1 Discrete Random Binary Sequence
10(3)
2.2 Frequency Analysis
13(1)
2.3 Time-Frequency Analysis
13(6)
2.3.1 Short-Time Fourier Transform
14(1)
2.3.2 Wavelet Transform
14(1)
2.3.3 The Morlet Wavelet
15(1)
2.3.4 Cone of Influence
16(1)
2.3.5 Computationally Efficient CWT with the Morlet Wavelet
17(1)
2.3.6 The Lognormal Wavelet
18(1)
2.4 Impedance Through Complex Wavelet Coefficients
19(1)
2.5 Parameter Selection
19(2)
2.5.1 Selection of the DRBS Bandwidth
19(1)
2.5.2 Influence of the Morlet Wavelet Central Frequency
20(1)
2.6 Accuracy of the Amplitude and Phase Estimates from Wavelet Coefficients
21(1)
2.7 Summary
21(2)
References
21(2)
3 Statistical Properties
23(8)
3.1 The Concept of Complex Circular Random Variable
23(2)
3.1.1 Circularity of Spectral Components
24(1)
3.1.2 Circularity of DRBS Spectral Components
24(1)
3.2 Statistical Properties of Measured Impedance
25(5)
3.2.1 Probability Distribution of the Measured Impedance
26(1)
3.2.2 Probability Distribution of Impedance Components
27(2)
3.2.3 Probability Distribution of the Impedance Amplitude
29(1)
3.2.4 Parameter Estimation
30(1)
3.3 Summary
30(1)
References
30(1)
4 Test Cases
31(12)
4.1 RC Circuits
31(5)
4.1.1 Measurement Equipment
32(1)
4.1.2 First-Order RC Circuit
33(2)
4.1.3 Cascaded RC Circuit
35(1)
4.2 Li-Ion Battery
36(2)
4.3 Li-S Cell
38(1)
4.4 PEM Fuel Cells
39(1)
4.5 Discussion
40(3)
References
41(2)
5 Statistical Condition Monitoring Tool
43(14)
5.1 Optimal Alarm Thresholds Based on the Probability of False Alarm
44(1)
5.2 Single Frequency Based Condition Indicator
45(1)
5.3 Dependence Among Complex Random Variables as a Condition Indicator
46(5)
5.3.1 Basics of Copula Functions
47(2)
5.3.2 Estimation of the Parameter θ
49(1)
5.3.3 Higher-Dimensional Copulas
50(1)
5.4 Copula Based Condition Indicator
51(3)
5.4.1 Selection of the Appropriate Frequencies
52(1)
5.4.2 Estimating Copula Parameters
52(1)
5.4.3 Copula Output as an Aggregated Condition Indicator
53(1)
5.5 Summary
54(3)
References
54(3)
6 Condition Monitoring of PEM Fuel Cells
57(8)
6.1 Experimental Setup
57(1)
6.2 Experimental Profile
58(1)
6.3 Time Evolution of Particular Impedance Components at a Single Frequency
59(1)
6.4 Time Evolution of the Condition Indicator at a Single Frequency
59(3)
6.5 Time Evolution of the Aggregated Condition Indicator
62(1)
6.6 Summary
63(2)
7 Hardware Components for Condition Monitoring of PEM Fuel Cells
65(14)
7.1 DC-DC Converter
65(6)
7.1.1 Microcontroller Circuitry
68(1)
7.1.2 Power Output Stage
68(3)
7.2 Fuel Cell Voltage Monitor
71(5)
7.2.1 Resolving the High Common-Mode Voltage Potential Issue
73(1)
7.2.2 FCVM Measurement Modes
74(1)
7.2.3 DC-DC Converter and FCVM as a Condition Monitoring System
75(1)
7.3 Summary
76(3)
References
76(3)
8 Conclusion
79(2)
Appendix A Listings 81
Pavle Bokoski and Andrej Debenjak are researchers at Joef Stefan Institute. Their main research focusis in the area of electrochemical energy systems. Among them, the authors have more than 10 years ofexperience in the field of signal processing and condition monitoring of mechanical and electrochemicalenergy systems. They are experts in the field of condition monitoring of fuel cells and have authored morethan 60 peer-reviewed publications. Biljana Mileva Boshkoska is an assistant professor in the Faculty of Information Studies in Novo Mesto.Her main research focus are methods for decision support systems and statistical information fusiontechniques. The developed techniques have been readily implemented in the field of maintenance decisionsupport.