Skip to main content
SpringerLink
Log in

Wavelet Technology in Vehicle Power Management

  • Chapter
  • First Online:
Vehicle Power Management

Part of the book series: Power Systems ((POWSYS))

Abstract

The wavelet technology is introduced in this book for to the vehicle power management system by the authors for the first time. It can identify the high-frequency transients and real time power demand of the drive line. By using the wavelet transform, a proper power demand combination can be achieved for power sources in all types of hybrid vehicles. The objective of the wavelet-based power management strategy is to improve system efficiency and life expectancy of power sources (i.e., the fuel cell and battery), usually in the presence of various constraints due to drivability requirements and component characteristics. This chapter depicts the fundamental concepts of wavelets and filter banks for the design of the vehicle power management system. The feasibility analysis and detailed process of the wavelet-based vehicle power management strategy are given for various types of vehicle configurations. Additionally, the application of the wavelets for vehicle real-time environment is demonstrated.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Goupillaud P, Grossman A, Morlet J (1984) Cycle-octave and related transforms in seismic signal analysis. Geoexploration 23:85–102

    Article  Google Scholar 

  2. Stark HG (2005) Wavelets and signal processing-an application-based introduction. Springer, Berlin Heidelberg

    Google Scholar 

  3. Dym H, McKean H (1985) Fourier series and integrals. Academic Press, Orlando, FL

    MATH  Google Scholar 

  4. Bracewell RN (2000) The fourier transform and its applications, 3rd edn. McGraw-Hill, Boston

    Google Scholar 

  5. Gramatikov B, Georgiev I (1995) Wavelets as alternative to short-time fourier transform in signal-averaged electrocardiography. Med Biol Eng Comput 33:482–487

    Article  Google Scholar 

  6. Rioul O, Vetterli M (1991) Wavelets and signal processing. IEEE Signal Process Mag 8:11–38

    Article  Google Scholar 

  7. Antoine JP, Carrette P, Murenzi R et al (1993) Image analysis with two-dimensional continuous wavelet transform. Signal Process 31:241–272

    Article  MATH  Google Scholar 

  8. Zheng WM (1992) Admissibility conditions for symbolic sequences of the Lozi map. Chaos, Solitons and Fractals 2:461–470

    Article  MathSciNet  MATH  Google Scholar 

  9. Fukuda S, Hirosawa H (1999) Smoothing effect of wavelet-based speckle filtering: the Haar basis case. IEEE Trans Geosci Remote Sens 37:1168–1172

    Article  Google Scholar 

  10. Oppenheim AV, Schafer RW, Buck JR et al (1999) Discrete-time signal processing. Rrentice Hall, Upper Saddle River, NJ

    Google Scholar 

  11. Shannon CE (1949) Communication in the presence of noise. Proc Inst Radio Eng 37:10–21

    MathSciNet  Google Scholar 

  12. Honda L (1998) Abstraction of Shannon’s sampling theorem.ICICE Trans Fundam Electron, Commun Comput Sci E81-A:1187–1193

    Google Scholar 

  13. Vrhel MJ, Lee C, Unser M (1997) Fast continuous wavelet transform: a least-squares formulation. Signal Process 57:103–119

    Article  MATH  Google Scholar 

  14. Gosz J, Liu WK (1996) Admissible approximations for essential boundary conditions in the reproducing kernel particle method. Comput Mech 19:120–135

    Article  MATH  Google Scholar 

  15. Louis AK, Maass P, Rieder A (1997) Wavelets, theory and applications. Wiley, New York

    MATH  Google Scholar 

  16. Christensen O (1996) Frames containing a riesz basis and approximation of the frame coefficients using finite-dimensional methods. J Math Anal App 199:256

    Article  MATH  Google Scholar 

  17. Arfken G (1985) Mathematical methods for physicists, 3rd edn. Academic Press, Orlando, FL

    Google Scholar 

  18. Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11:674–693

    Article  MATH  Google Scholar 

  19. Meyer Y (1992) Wavelets and operators, volume 37 of cambridge studies in advanced mathematics. Cambridge University Press, Cambridge, MA

    Google Scholar 

  20. Jansen M, Oonincx P (2005) Second generation wavelets and applications. Springer, London

    Google Scholar 

  21. Vetterli M, Herley C (1992) Wavelets and filter banks: theory and design. IEEE Trans Signal Process 40:2207–2232

    Article  MATH  Google Scholar 

  22. Jin Q, Luo ZQ, Wong KM (1994) Optimum complete orthonormal basis for signal analysis and design. IEEE Trans Inf Theory 40:732–742

    Article  MATH  Google Scholar 

  23. Soman AK, Vaidyanathan PP (1993) On orthonormal wavelets and paraunitary filter banks. IEEE Trans Signal Process 41:1170–1183

    Article  MATH  Google Scholar 

  24. Rajqopal K, Babu JD, Venkataraman S (2007) Generalized adaptive IFIR filter bank structures. Signal Process 87:1575–1596

    Article  Google Scholar 

  25. Daubechies I (1988) Orthonormal bases of compactly supported wavelets. Commun Pure Appl Math XLI:909–996

    Article  MathSciNet  Google Scholar 

  26. Cariolaro G, Kraniauskas P, Vangelista L (2005) A novel general formulation of up/downsampling commutativity. IEEE Trans Signal Process 53:2124–2134

    Article  MathSciNet  Google Scholar 

  27. Muthuvel A, Makur A (2001) Eigenstructure approach for characterization of FIR PR filterbanks with order one polyphase. IEEE Trans Signal Process 49:2283–2291

    Article  Google Scholar 

  28. Vaidyanathan PP, Nquyen TQ, Doqanata Z et al (1989) Improved technique for design of perfect reconstruction FIR QMF banks with lossless polyphase matrices. IEEE Trans Acoust Speech Signal Process 37:1042–1058

    Article  Google Scholar 

  29. Vetterli M, Le Gall D (1989) Perfect reconstruction FIR filter banks: some properties and factorizations. IEEE Trans Acoust Speech Signal Process 37:1057–1071

    Article  Google Scholar 

  30. Lu HC, Tzeng ST (2001) Adaptive lifting schemes with perfect reconstruction. Int J Syst Sci 32:25–32

    MathSciNet  MATH  Google Scholar 

  31. Sinap A, van Assche W (1996) Orthogonal matrix polynomials and applications. J Comput Appl Math 66:27–52

    Article  MathSciNet  MATH  Google Scholar 

  32. Vaidyanathan PP, Hoang PQ (1988) Lattice structures for optimal design and robust implementation of two-band perfect reconstruction QMF banks. IEEE Trans Acoust Speech Signal Process 36:81–94

    Article  Google Scholar 

  33. Nagai T, Fuchie T, Ikehara M (1997) Design of linear phase M-channel perfect reconstruction FIR filter banks. IEEE Trans Signal Process 45:2380–2387

    Article  Google Scholar 

  34. Vandiyanathan PP (1987) Theory and design of M-channel maximally decimated quadrature mirror filters with arbitrary m, having the perfect-reconstruction property. IEEE Trans Acoust Speech Signal Process 35:476–492

    Article  Google Scholar 

  35. Nguyen TQ, Vaidyananthan PP (1990) Structures for M-Channel perfect- reconstruction FIR QMF banks which yield linear-phase analysis filters. IEEE Trans Acoust Speech Signal Process 38:433–446

    Article  MATH  Google Scholar 

  36. Yan X, Hou M, Sun L et al (2007) The study on transient characteristic of proton exchange membrane fuel cell stack during dynamic loading. J Power Sources 163:966–970

    Article  Google Scholar 

  37. Liu X, Hui SYR (2005) An analysis of a double-layer electromagnetic shield for a universal contactless battery charging platform. 36th IEEE Power Electron Specialists Conference 2005, pp 1767–1772

    Google Scholar 

  38. Jossen A (2006) Fundamentals of battery dynamics. J Power Sources 154:530–538

    Article  Google Scholar 

  39. Ribeiro PF (1994) Wavelet transform: an advanced tool for analyzing non-stationary harmonic distortion in power systems. Proc IEEE ICHPS VI:21–23

    Google Scholar 

  40. Robertson D, Camps Q, Mayer J et al (1996) Wavelets and electromagnetic power system transients. IEEE Trans Power Delivery 11:1050–1058

    Article  Google Scholar 

  41. Galli AW (1997) Analysis of electrical transients in power systems via a novel wavelet recursive method. PhD Dissertation, Purdue University, Purdue

    Google Scholar 

  42. Heydt GT, Galli AW (1997) Transient power quality problems analyzed using wavelets. IEEE Trans Power Delivery 12:908–915

    Article  Google Scholar 

  43. Meliopoulos APS, Lee CH (1997) Wavelet-based transient analysis, Proceedings of North American power symposium, pp 339–346

    Google Scholar 

  44. Strzelecki RM, Benysek G (2008) Energy storage systems. Springer, London

    Google Scholar 

  45. http://www.act.jp/eng/index.htm. Accessed 1 Feb 2009

  46. Zhang X, Mi CC, Masrur A, Daniszewski D et al (2008) Wavelet based power management of hybrid electric vehicles with multiple onboard power sources. J Power Sources 185:1533–1543

    Article  Google Scholar 

  47. Walter GG, Shen XP (2001) Wavelets and other orthogonal systems. CRC Press, Boca Raton, FL

    MATH  Google Scholar 

  48. Ben-Aris J, Rao KR (1993) A novel approach for template matching by nonorthogonal image expansion. IEEE Trans Circuits Syst Video Technol 3:71–84

    Article  Google Scholar 

  49. Wang X (2006) Moving window-based double Haar wavelet transform for image processing. IEEE Trans Image Process 15:2771–2779

    Article  Google Scholar 

  50. Zhang X, Mi CC, Masrur A et al (2008) Wavelet-transform-based power management of hybrid vehicles with multiple on-board energy sources including fuel cell, battery and ultracapacitor. J Power Sources 185:1533–1543

    Article  Google Scholar 

  51. Suter BW (1998) Multirate and wavelet signal processing. Academic Press, New York

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Michigan-Dearborn, 4901 Evergreen Rd., Dearborn, MI, USA

    Xi Zhang & Chris Mi

Authors
  1. Xi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chris Mi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chris Mi .

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag London Limited

About this chapter

Cite this chapter

Zhang, X., Mi, C. (2011). Wavelet Technology in Vehicle Power Management. In: Vehicle Power Management. Power Systems. Springer, London. https://doi.org/10.1007/978-0-85729-736-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-0-85729-736-5_5

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-735-8

  • Online ISBN: 978-0-85729-736-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation