Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Introduction and Overview Kapitel lesen Erstes Kapitel lesen

2013 | OriginalPaper | Buchkapitel

8. Applications to Electric Machines

verfasst von : Qi He, Le Yi Wang, G. George Yin

Erschienen in: System Identification Using Regular and Quantized Observations

Verlag: Springer New York

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Electric machines are essential systems in electric vehicles and are widely used in other applications. In particular, permanent magnet direct current (PMDC) motors have been extensively employed in industrial applications such as electric vehicles and battery-powered devices such as wheelchairs, power tools, guided vehicles, welding equipment, X-ray and tomographic systems, and computer numerical control (CNC) machines. PMDC motors are physically smaller and lighter for a given power rating than induction motors. The unique features of PMDC motors, including their high torque production at lower speed and flexibility in design, make them preferred choices in automotive transmissions, gear systems, lower-power traction utility, and other fields [12, 18, 22, 47, 54]. For efficient torque/speed control, thermal management, motor-condition monitoring, and fault diagnosis of PMDC motors, it is essential that their characteristics be captured in real-time operations. This is a system identification problem that can be carried out by using standard identification methods [35, 48].

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Applications to Medical Signal Processing
Nächstes Kapitel Remarks and Conclusion
Fußnoten
1
In fact, in the actual implementation of the algorithm, the threshold is adaptively selected to improve estimation accuracy. This value is selected as an example that is close to the average of the periodic output values.
 
Literatur
1.
K. Aström and B. Wittenmark, Adaptive Control, Addison-Wesley, 1989.
2.
R.R. Bahadur, Large deviation of the maximum likelihood estimate in the Markov chain case, in Recent Advances in Statistics, M.H. Rizvi, J.S. Rostag, and D. Siegmund eds., Academic Press, NY, 1983, 273–286.
3.
R.R. Bahadur, S.L. Zabell, and J.C. Gupta, Large deviations, tests and estimates, in Asymptotic Theory of Statistical Tests, I.M. Chakravarti, ed., Academic Press, NY, 1980, 33–64.
4.
C. Barbier, H. Meyer, B. Nogarede, S. Bensaoud, A battery state of charge indicator for electric vehicle, in Proc. International Conference of the Institution of Mechanical Engineers, Automotive Electronics, London, UK, May 17–19, 29–34, 1994.
5.
E. Barsoukov, J. Kim, C. Yoon, H. Lee, Universal battery parameterization to yield a non-linear equivalent circuit valid for battery simulation at arbitrary load, J. Power Sour., 83 (1999), 61–70.CrossRef
6.
B.S. Bhangu, P. Bentley, D.A. Stone, and C.M. Bingham, Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles, IEEE Trans. Veh. Tech., 54 (2005), 783–794.CrossRef
7.
W. Bryc and A. Dembo. Large deviations and strong mixing, Ann. Inst. H. Poincare Probab. Stat., 32 (1996), 549–569.MathSciNetMATH
8.
P.E. Caines, Linear Stochastic Systems, Wiley, New York, 1988.MATH
9.
H. Chan, D. Sutanto, A new battery model for use with battery energy storage systems and electric vehicle power systems, in Proceedings of the 2000 IEEE Power Engineering Society Winter Meeting, Singapore, 470–475, January 23–27, 2000.
10.
H.-F. Chen and L. Guo, Identification and Stochastic Adaptive Control, Birkhäuser, Boston, 1991.MATHCrossRef
11.
K.L. Chung, On a stochastic approximation method, Ann. Math. Statist., 25 (1954), 463–483.MATHCrossRef
12.
O. Craiu, A. Machedon, et al., 3D finite element thermal analysis of a small power PM DC motor, 12th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM), 2010.
13.
H.F. Dai, X.Z. Wei, Z.C. Sun, Online SOC estimation of high-power lithium-ion batteries used on HEVs, Proceedings of IEEE ICVES 2006, 342–347.
14.
A. Dembo and O. Zeitouni, Large Deviations Techniques and Applications, 2nd ed., Springer-Verlag, New York, 1998.MATHCrossRef
15.
P. Dupuis and H.J. Kushner, Stochastic approximation via large deviations: asymptotic properties, SIAM J. Control Optim., 23 (1985) 675–696.MathSciNetMATHCrossRef
16.
P. Dupuis and H.J. Kushner, Stochastic approximation and large deviations: upper bounds and w.p.1 convergence, SIAM J. Control Optim., 27 (1989) 1108–1135.
17.
W. Feller, An Introduction to Probability Theory and Its Applications, vol. I, 3rd ed. Wiley, New York, 1968.
18.
A.E. Fitzgerald, C. Kingsley Jr., and S.D. Umans, Electric Machinery, McGraw-Hill Science/Engineering/Math, 6th ed., 2002.
19.
J. Gärtner, On large deviations from the invariant measure, Theory Probab. Appl., 22 (1977), 24–39.MATHCrossRef
20.
R. Giglioli, P. Pelacchi, M. Raugi, and G. Zini, A state of charge observer for lead-acid batteries, Energia Elettrica 65 (1988), 27–33.
21.
W.B. Gu and C.Y. Wang, Thermal electrochemical modeling of battery systems, J. Electrochem. Soc., 147 (2000), 2910–2922.CrossRef
22.
B.S. Guru and H.R. Hiziroglu, Electric Machinery and Transformers, Oxford University Press, 2001.
23.
S. Haykin, Unsupervised Adaptive Filtering: Volume I Blind Source Separation, John Wiley Sons, Inc., USA, 2000.
24.
V. Johnson, A. Pesaran, and T. Sack, Temperature-dependent battery models for high-power lithium-ion batteries, Proceedings of the 17th Electric Vehicle Symposium, Montreal, Canada, October 2000.
25.
V. Johnson, M. Zolot, and A. Pesaran, Development and validation of a temperature-dependent resistance/capacitance battery model for ADVISOR, Proceedings of the 18th Electric Vehicle Symposium, Berlin, Germany, October 2001.
26.
I.-S. Kim, A technique for estimating the state of health of lithium batteries through a dual-sliding-mode observer, IEEE Trans. Power Elec., 25 (2010), 1013–1022.CrossRef
27.
28.
P.R. Kumar and P. Varaiya, Stochastic Systems: Estimation, Identification and Adaptive Control, Prentice-Hall, Englewood Cliffs, NJ, 1986.MATH
29.
H.J. Kushner, Asymptotic behavior of stochastic approximation and large deviation, IEEE Trans. Automat. Control, 29 (1984), 984–990.MathSciNetMATHCrossRef
30.
H.J. Kushner and H. Huang, Rates of convergence for stochastic approximation type algorithms, SIAM J. Control Optim., 17 (1979) 607–617.MathSciNetMATHCrossRef
31.
H.J. Kushner and G. Yin, Stochastic Approximation Algorithms and Applications, 2nd ed., Springer-Verlag, New York, 2003.MATH
32.
Z.Y. Liu and C.R. Lu, Limit Theory for Mixing Dependent Random Variables, Science Press, Kluwer Academic, New York, Dordrecht, 1996.
33.
L. Liu, L.Y. Wang, Z. Chen, C. Wang, F. Lin, and H. Wang, Integrated system identification and state-of-charge estimation of battery systems, IEEE Transactions on Energy Conversion, (2013), 1–12.
34.
D. Linden and T. Reddy, Handbook of Batteries, 3rd ed., McGraw Hill, 2001.
35.
L. Ljung, System Identification: Theory for the User, Prentice-Hall, Englewood Cliffs, NJ, 1987.MATH
36.
L. Ljung, H. Hjalmarsson, and H. Ohlasson, Four encounters with systems identification, Euro. J. Control, 17 (2011) 449–471.MATHCrossRef
37.
L. Ljung and T. Söderström, Theory and Practice of Recursive Identification, MIT Press, Cambridge, MA, 1983.MATH
38.
M. Milanese and A. Vicino, Optimal estimation theory for dynamic systems with set membership uncertainty: an overview, Automatica, 27 (1991), 997–1009.MathSciNetMATHCrossRef
39.
40.
K. Ogata, Discrete-Time Control Systems, Prentice-Hall, Inc. Upper Saddle River, NJ, 1987.
41.
B.T. Polyak, New method of stochastic approximation type, Automation Remote Control 7 (1991), 937–946.
42.
G. Plett, Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs. Part 1. Background, J. Power Sour., 134 (2004), 252–261.
43.
M.A. Roscher, J. Assfalg, and O.S. Bohlen, Detection of utilizable capacity deterioration in battery systems, IEEE Trans. Vehicular Tech., 60 (2011), 98–103.CrossRef
44.
S. Rodrigues, N. Munichandraiah, and A. Shukla, A review of state-of-charge indication of batteries by means of AC impedance measurements, J. Power Sour., 87 (2000), 12–20.CrossRef
45.
D. Ruppert, Stochastic approximation, in Handbook in Sequential Analysis, B.K. Ghosh and P.K. Sen, eds., Marcel Dekker, New York, 1991, 503–529.
46.
M. Sitterly, L.Y. Wang, G. Yin, and C. Wang, Enhanced identification of battery models for real-time battery management, IEEE Transactions on Sustainable Energy, 2 (2011), 300–308.CrossRef
47.
A.M. Sharaf, E. Elbakush, and I.H. Atlas, A predictive dynamic controller for PMDC motor drives, Fifth International Conference on Industrial Automation, Montréal, Quebec, Canada, June 11–13, 2007.
48.
T. Soderstom and P. Stoica, System Identification, Prentice-Hall, 1989.
49.
V. Solo, Robust identification and large deviations, in Proc. 35th IEEE CDC, Kobe Japan, Dec. 1996, 4202–4203.
50.
V. Solo, More on robust identification and large deviations, in Proc. IFAC99, Beijing, P.R. China, June, 1999, 451–455.
51.
V. Solo and X. Kong, Adaptive Signal Processing Algorithms, Prentice-Hall, Englewood Cliffs, NJ, 1995.
52.
K. Takano, K. Nozaki, Y. Saito, A. Negishi, K. Kato, and Y. Yamaguchi, Simulation study of electrical dynamic characteristics of lithium-ion battery, J. Power Sour., 90 (2000), 214–223.CrossRef
53.
O. Tremblay and L.A. Dessaint, Experimental validation of a battery dynamic model for EV applications, World Electric Vehicle Journal, vol. 3, at 2009 AVERE, EVS24 Stavanger, Norway, May 13–16, 2009.
54.
V.I. Utkin and H.-C. Chang, Sliding mode control on electromechanical systems, Mathmatical Problems in Engineering, 8 (2002), 451–473.MathSciNetMATHCrossRef
55.
S.R. Venkatesh and M.A. Dahleh, Identification in the presence of classes of unmodelled dynamics and noise, IEEE Trans. Automatic Control, 42 (1997), 1620–1635.MathSciNetMATHCrossRef
56.
A. Widodo, M.-C. Shim, W. Caesarendra, and B.-S. Yang, Intelligent prognostics for battery health monitoring based on sample entropy, Expert Systems Appl., 38 (2011) 11763–11769.CrossRef
57.
L.Y. Wang and G. Yin, Persistent identification of systems with unmodeled dynamics and exogenous disturbances, IEEE Trans. Automat. Control, 45 (2000), 1246–1256.MathSciNetMATHCrossRef
58.
L.Y. Wang and G. Yin, Asymptotically efficient parameter estimation using quantized output observations, Automatica, 43 (2007), 1178–1191.MathSciNetMATHCrossRef
59.
L.Y. Wang and G. Yin, Quantized identification with dependent noise and Fisher information ratio of communication channels, IEEE Trans. Automat. Control, 53 (2010), 674–690.MathSciNetCrossRef
60.
L.Y. Wang, G. Yin, and J.F. Zhang, Joint identification of plant rational models and noise distribution functions using binary-valued observations, Automatica, 42 (2006), 535–547.MathSciNetMATHCrossRef
61.
L.Y. Wang, G. Yin, J.F. Zhang, and Y.L. Zhao, Space and time complexities and sensor threshold selection in quantized identification, Automatica, 44 (2008), 3014–3024.MathSciNetMATHCrossRef
62.
L.Y. Wang, G. Yin, J.-F. Zhang, and Y.L. Zhao, System Identification with Quantized Observations: Theory and Applications, Birkhäuser, Boston, 2010.CrossRef
63.
L.Y. Wang, J.-F. Zhang, and G. Yin, System identification using binary sensors, IEEE Trans. Automat. Control, 48 (2003), 1892–1907.MathSciNetCrossRef
64.
65.
B. Widrow, J. Glover, J. McCool, J. Kaunitz, C. Williams, R. Hearn, J. Zeidler, E. Dong and R. Goolin, Adaptive noise cancelling: principles and applications, IEEE Proc., 63 (1975), 1692–1716.CrossRef
66.
G. Yin, S. Kan, L.Y. Wang, and C.Z. Xu, Identification of systems with regime switching and unmodelled dynamics, IEEE Trans. Automat. Control, 54 (2009), 34–47.MathSciNetCrossRef
67.
G. Yin, V. Krishnamurthy, and C. Ion, Regime switching stochastic approximation algorithms with application to adaptive discrete stochastic optimization, SIAM J. Optim., 14 (2004), 1187–1215.MathSciNetMATHCrossRef
68.
G. Yin, L.Y. Wang, and S. Kan, Tracking and identification of regime-switching systems using binary sensors, Automatica, 45 (2009), 944–955.MathSciNetMATHCrossRef
69.
G. Yin and Q. Zhang, Discrete-Time Markov Chains: Two-Time-Scale Methods and Applications, Springer, New York, 2005.MATH
70.
G. Yin and C. Zhu, Hybrid Switching Diffusions: Properties and Applications, Springer, New York, 2010.MATHCrossRef
71.
G. Zames, On the metric complexity of causal linear systems: ε-entropy and ε-dimension for continuous time, IEEE Trans. Automatic Control, 24 (1979), 222–230.MathSciNetMATHCrossRef
72.
H. Zheng, H. Wang, L.Y. Wang, and G. Yin, Time-shared channel identification for adaptive noise cancellation in breath sound extraction, J. Control Theory Appl., 2 (2004), 209–221.MATHCrossRef
73.
H. Zheng, H. Wang, L.Y. Wang, and G. Yin, Cyclic system reconfiguration and time-split signal separation with applications to lung sound pattern analysis, IEEE Trans. Signal Processing, 55 (2007), 2897–2913.MathSciNetCrossRef
74.
X.Y. Zhou and G. Yin, Markowitz mean-variance portfolio selection with regime switching: a continuous-time model, SIAM J. Control Optim., 42 (2003), 1466–1482.MathSciNetMATHCrossRef
75.
Metadaten
Titel
Applications to Electric Machines
verfasst von
Qi He
Le Yi Wang
G. George Yin
Copyright-Jahr
2013
Verlag
Springer New York
Buch
System Identification Using Regular and Quantized Observations

Print ISBN: 978-1-4614-6291-0

Electronic ISBN: 978-1-4614-6292-7

Copyright-Jahr: 2013

DOI
https://doi.org/10.1007/978-1-4614-6292-7_8

Neuer Inhalt

    Bildnachweise