Skip to main content

Intelligent Vehicle Power Management: An Overview

  • Chapter

Part of the book series: Studies in Computational Intelligence ((SCI,volume 132))

Summary

This chapter overviews the progress of vehicle power management technologies that shape the modern automobile. Some of these technologies are still in the research stage. Four in-depth case studies provide readers with different perspectives on the vehicle power management problem and the possibilities that intelligent systems research community can contribute towards this important and challenging problem.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. http://www.epa.gov/otaq/emisslab/methods/uddscol.txt

  2. I. Arsie, M. Graziosi, C. Pianese, G. Rizzo, and M. Sorrentino, “Optimization of supervisory control strategy for parallel hybrid vehicle with provisional load estimate,” in Proc. 7th Int. Symp. Adv. Vehicle Control (AVEC), Arnhem, The Netherlands, Aug. 2004.

    Google Scholar 

  3. B. Badreddine and M.L. Kuang, “Fuzzy energy management for powersplit hybrid vehicles,” in Proc. of Global Powertrain Conference, Sept. 2004.

    Google Scholar 

  4. R. Bata, Y. Yacoub, W. Wang, D. Lyons, M. Gambino, and G. Rideout, “Heavy duty testing cycles: survey and comparison,” SAE Paper 942 263, 1994.

    Google Scholar 

  5. M. Back, M. Simons, F. Kirschaum, and V. Krebs, “Predictive control of drivetrains,” in Proc. IFAC 15th Triennial World Congress, Barcelona, Spain, 2002.

    Google Scholar 

  6. J. Bumby and I. Forster, “Optimization and control of a hybrid electric car,” Inst. Elect. Eng. Proc. D, vol. 134, no. 6, pp. 373–387, Nov. 1987.

    MATH  MathSciNet  Google Scholar 

  7. T.R. Carlson and R.C. Austin, “Development of speed correction cycles,” Report SR97-04-01, Sierra Research, Sacramento, CA, 1997.

    Google Scholar 

  8. J. Cloke, G. Harris, S. Latham, A. Quimby, E. Smith, and C. Baughan, “Reducing the environmental impact of driving: a review of training and in-vehicle technologies,” Report 384, Transport Res. Lab., Crowthorne, UK, 1999.

    Google Scholar 

  9. ZhiHang Chen, M. Abul Masrur, and Yi L. Murphey, “Intelligent vehicle power management using machine learning and fuzzy logic,” FUZZ 2008.

    Google Scholar 

  10. S. Delprat, J. Lauber, T.M. Guerra, and J. Rimaux, “Control of a parallel hybrid powertrain: optimal control,” IEEE Trans. Veh. Technol., vol. 53, no. 3, pp. 872–881, May 2004.

    Article  Google Scholar 

  11. A. Emadi, M. Ehsani, and J.M. Miller, Vehicular Electric Power Systems: Land, Sea, Air, and Space Vehicles. New York: Marcel Dekker, 2003.

    Google Scholar 

  12. E. Ericsson, “Variability in urban driving patterns,” Transport. Res. D, vol. 5, pp. 337–354, 2000.

    Article  Google Scholar 

  13. E. Ericsson, “Independent driving pattern factors and their influence on fuel-use and exhaust emission factors,” Transport. Res. D, vol. 6, pp. 325–341, 2001.

    Article  Google Scholar 

  14. F. Ferri, P. Pudil, M. hatef, and J. Kittler, “Comparative study of techniques for large scale feature selection,” in Pattern Recognition in Practice IV, E. Gelsema and L. Kanal, eds., pp. 403–413. Amsterdam: Elsevier, 1994.

    Google Scholar 

  15. Hamid Gharavi, K. Venkatesh Prasad, and Petros Ioannou, “Scanning advanced automobile technology,” Proc. IEEE, vol. 95, no. 2, Feb. 2007.

    Google Scholar 

  16. T. Hofman and R. van Druten, “Energy analysis of hybrid vehicle powertrains,” in Proc. IEEE Int. Symp. Veh. Power Propulsion, Paris, France, Oct. 2004.

    Google Scholar 

  17. B.A. Holmén and D.A. Niemeier, “Characterizing the effects of driver variability on real-world vehicle emissions,” Transport. Res. D, vol. 3, pp. 117–128, 1997.

    Article  Google Scholar 

  18. Jacob A. Crossman, Hong Guo, Yi Lu Murphey, and John Cardillo, “Automotive Signal Fault Diagnostics: Part I: signal fault analysis, feature extraction, and quasi optimal signal selection,” IEEE Transactions on Vehicular Technology, July 2003.

    Google Scholar 

  19. S.-I. Jeon, S.-T. Jo, Y.-I. Park, and J.-M. Lee, “Multi-mode driving control of a parallel hybrid electric vehicle using driving pattern recognition,” J. Dyn. Syst. Meas. Control., vol. 124, pp. 141–149, Mar. 2002.

    Article  Google Scholar 

  20. V.H. Johnson, K.B. Wipke, and D.J. Rausen, “HEV control strategy for real-time optimization of fuel economy and emissions,” SAE Paper-01-1543, 2000.

    Google Scholar 

  21. K. Ehlers, H.D. Hartmann, and E. Meissner, “42 V – An indication for changing requirements on the vehicle electrical system,” J. Power Sources, vol. 95, pp. 43–57, 2001.

    Article  Google Scholar 

  22. M. Koot, J.T.B.A. Kessels, B. de Jager, W.P.M.H. Heemels, P.P.J. van den Bosch, and M. Steinbuch, “Energy management strategies for vehicular electric power systems,” IEEE Trans. Veh. Technol., vol. 54, no. 3, pp. 771–782, May 2005.

    Article  Google Scholar 

  23. J.G. Kassakian, J.M. Miller, and N. Traub, “Automotive electronics power up,” IEEE Spectr., vol. 37, no. 5, pp. 34–39, May 2000.

    Article  Google Scholar 

  24. C. Kim, E. NamGoong, S. Lee, T. Kim, and H. Kim, “Fuel economy optimization for parallel hybrid vehicles with CVT,” SAE Paper-01-1148, 1999.

    Google Scholar 

  25. M.W.Th. Koot, “Energy management for vehicular electric power systems,” Ph.D. thesis, Library Technische Universiteit Eindhoven, 2006, ISBN-10: 90-386-2868-4.

    Google Scholar 

  26. I. Kolmanovsky, I. Siverguina, and B. Lygoe, “Optimization of powertrain operating policy for feasibility assessment and calibration: stochastic dynamic programming approach,” in Proc. Amer. Contr. Conf ., vol. 2, Anchorage, AK, May 2002, pp. 1425–1430.

    Google Scholar 

  27. R. Langari and Jong-Seob Won, “Intelligent energy management agent for a parallel hybrid vehicle-part I: system architecture and design of the driving situation identification process,” IEEE Trans. Veh. Technol., vol. 54, no. 3, pp. 925–934, 2005.

    Article  Google Scholar 

  28. C.-C. Lin, Z. Filipi, L. Louca, H. Peng, D. Assanis and J. Stein, “Modelling and control of a medium-duty hybrid electric truck,” Int. J. Heavy Veh. Syst., vol. 11, nos. 3/4, pp. 349–370, 2004.

    Article  Google Scholar 

  29. C.-C. Lin, H. Peng, J.W. Grizzle, and J.-M. Kang, “Power management strategy for a parallel hybrid electric truck,” IEEE Trans. Contr. Syst. Technol., vol. 11, no. 6, pp. 839–849, Nov. 2003.

    Article  Google Scholar 

  30. C.-C. Lin, H. Peng, and J.W. Grizzle, “A stochastic control strategy for hybrid electric vehicles,” in Proc. Amer. Contr. Conf ., Boston, MI, Jun. 2004, pp. 4710–4715.

    Google Scholar 

  31. D.C. LeBlanc, F.M. Saunders, M.D. Meyer, and R. Guensler, “Driving pattern variability and impacts on vehicle carbon monoxide emissions,” in Transport. Res. Rec., Transportation Research Board, National Research Council, 1995, pp. 45–52.

    Google Scholar 

  32. Yi L. Murphey, ZhiHang Chen, Leo Kiliaris, Jungme Park, Ming Kuang, Abul Masrur, and Anthony Phillips, “Neural learning of predicting driving environment,” IJCNN 2008.

    Google Scholar 

  33. Jorge Moreno, Micah E. Ortúzar, and Juan W. Dixon, “Energy-management system for a hybrid electric vehicle, using ultracapacitors and neural networks,” IEEE Trans. Ind. Electron., vol. 53, no. 2, Apr. 2006.

    Google Scholar 

  34. Yi Lu Murphey and Hong Guo, “Automatic feature selection – a hybrid statistical approach,” in International Conference on Pattern Recognition, Barcelona, Spain, Sept. 3–8, 2000.

    Google Scholar 

  35. P. Nicastri and H. Huang, “42 V PowerNet: providing the vehicle electric power for the 21st century,” in Proc. SAE Future Transportation Technol. Conf ., Costa Mesa, CA, Aug. 2000, SAE Paper 2000-01-3050.

    Google Scholar 

  36. Guobing Ou, Yi L. Murphey, and Lee Feldkamp, “Multiclass pattern classification using neural networks,” in International Conference on Pattern Recognition, Cambridge, UK, 2004.

    Google Scholar 

  37. Guobin Ou and Yi Lu Murphey, “Multi-class pattern classification using neural networks,” J. Pattern Recognit., vol. 40, no 1, pp. 4–18, Jan. 2007.

    Article  MATH  Google Scholar 

  38. G. Paganelli, G. Ercole, A. Brahma, Y. Guezennec, and G. Rizzoni, “General supervisory control policy for the energy optimization of charge-sustaining hybrid electric vehicles,” JSAE Rev., vol. 22, no. 4, pp. 511–518, Apr. 2001.

    Article  Google Scholar 

  39. T. Preben, “Positive side effects of an economical driving style: safety, emissions, noise, costs,” in Proc. ECODRIVE 7th Conf ., Sept. 16–17, 1999.

    Google Scholar 

  40. Danil V. Prokhorov, “Toyota Prius HEV neurocontrol,” in Proceedings of International Joint Conference on Neural Networks, Orlando, FL, USA, Aug. 12–17, 2007.

    Google Scholar 

  41. Danil V. Prokhorov, “Approximating optimal controls with recurrent neural networks for automotive systems,” in Proceedings of the 2006 IEEE International Symposium on Intelligent Control, Munich, Germany, Oct. 4–6, 2006.

    Google Scholar 

  42. Fazal U. Syed, Dimitar Filev, and Hao Ying, “Fuzzy rule-based driver advisory system for fuel economy improvement in a hybrid electric vehicle,” in Annual Meeting of the NAFIPS, June 24–27, 2007, pp. 178–183.

    Google Scholar 

  43. A. Sciarretta, L. Guzzella, and M. Back, “A real-time optimal control strategy for parallel hybrid vehicles with on-board estimation of the control parameters,” in Proc. IFAC Symp. Adv. Automotive Contr., Salerno, Italy, Apr. 19–23, 2004.

    Google Scholar 

  44. Sierra Research,“ SCF Improvement – Cycle Development,” Sierra Report No. SR2003-06-02, 2003.

    Google Scholar 

  45. F. Syed, M.L. Kuang, J. Czubay, M. Smith, and H. Ying, “Fuzzy control to improve high-voltage battery power and engine speed control in a hybrid electric vehicle,” in Soft Computing for Real World Applications, NAFIPS, Ann Arbor, MI, June 22–25, 2005.

    Google Scholar 

  46. N.J. Schouten, M.A. Salman, and N.A. Kheir, “Fuzzy logic control for parallel hybrid vehicles,” IEEE Trans. Contr. Syst. Technol., vol. 10, no. 3, pp. 460–468, May 2002.

    Article  Google Scholar 

  47. E.D. Tate and S.P. Boyd, “Finding ultimate limits of performance for hybrid electric vehicles,” SAE Paper-01-3099, 2000.

    Google Scholar 

  48. Highway Capacity Manual 2000, Transportation Res. Board, Washington, DC, 2000.

    Google Scholar 

  49. I. De Vlieger, D. De Keukeleere, and J. Kretzschmar, “Environmental effects of driving behaviors and congestion related to passenger cars,” Atmos. Environ., vol. 34, pp. 4649–4655, 2000.

    Article  Google Scholar 

  50. I. De Vlieger, “Influence of driving behavior on fuel consumption,” in ECODRIVE 7th Conf ., Sept. 16–17, 1999.

    Google Scholar 

  51. J.-S. Won, R. Langari, and M. Ehsani, “Energy management strategy for a parallel hybrid vehicle,” in Proc. Int. Mechan. Eng. Congress and Exposition (IMECE ’02), New Orleans, LA, Nov. 2002, pp. IMECE2002–33 460.

    Google Scholar 

  52. Jong-Seob Won and R. Langari, “Intelligent energy management agent for a parallel hybrid vehicle-part II: torque distribution, charge sustenance strategies, and performance results,” IEEE Trans. Veh. Technol., vol. 54, no. 3, pp. 935–953, 2005.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Murphey, Y.L. (2008). Intelligent Vehicle Power Management: An Overview. In: Prokhorov, D. (eds) Computational Intelligence in Automotive Applications. Studies in Computational Intelligence, vol 132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79257-4_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-79257-4_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-79256-7

  • Online ISBN: 978-3-540-79257-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics