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Model-Based Optimal Energy Management Strategies for Hybrid Electric Vehicles

  • Simona Onori
Chapter
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 455)

Abstract

Methods from optimal control theory have been used since the past decade to design model-based energy management strategies for hybrid electric vehicles (HEVs). These strategies are usually designed as solutions to a finite-time horizon, constrained optimal control problem that guarantees optimality upon perfect knowledge of the driving cycle. Properly adapted these strategies can be used for real-time implementation (without knowledge of the future driving mission) at the cost of either high (sometime prohibitive) computational burden or high memory requirement to store high-dimensional off-line generated look-up tables. These issues have motivated the research reported in this chapter. We propose to address the optimal energy management problem over an infinite time horizon by formulating the problem as a nonlinear, nonquadratic optimization problem. An analytical supervisory controller is designed that ensures stability, optimality with respect to fuel consumption, ease of implementation in real-time application, fast execution and low control parameter sensitivity. The approach generates a drive cycle independent control law without requiring discounted cost or shortest path stochastic dynamic programming introduced in the prior literature.

Keywords

Optimal Control Problem Energy Management Driving Cycle Drive Cycle Battery Energy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The author would like to deeply thank Roberto Mura for taking this research a step forward, Lorenzo Serrao for the enjoyable and productive discussions, Yann Guezennec, Giorgio Rizzoni, and Stephen Yuorkovich for the productive iterations.

References

  1. 1.
    Argonne National Laboratory: Powertrain system analysis toolkit (PSAT) documentation. DuPage County, IL, http://web.anl.gov/techtransfer/pdf/PSAT.pdf
  2. 2.
    Bernstein DS (1993) Non quadratic cost and nonlinear feedback control. Int J Robust Nonlinear Control 3:211–229CrossRefMATHGoogle Scholar
  3. 3.
    Bertsekas DP (1995) Dynamic programming and optimal control. Athena Scientific, BelmontMATHGoogle Scholar
  4. 4.
    Bianchi D, Rolando L, Serrao L, Onori S, Rizzoni G, Al-Khayat N, Hsieh TM, Kang P (2011) Layered control strategies for hybrid electric vehicles based on optimal control. Int J Electric Hybrid Veh 3:191–217CrossRefGoogle Scholar
  5. 5.
    Biasini R, Onori S, Rizzoni G (2013) A rule-based energy management strategy for hybrid medium duty truck. Int J Powertrains 2(2/3):232–261CrossRefGoogle Scholar
  6. 6.
    Brahma A, Guezennec Y, Rizzoni G (2000) Optimal energy management in series hybrid electric vehicles. In: Proceedings of the 2000 American control conference, vol 1, issue 6, pp 60–64.Google Scholar
  7. 7.
    Chan CC (2002) The state of the art of electric and hybrid vehicles. Proc IEEE 90(2):247–275CrossRefGoogle Scholar
  8. 8.
    Cipollone R, Sciarretta A (2006) Analysis of the potential performance of a combined hybrid vehicle with optimal supervisory control. In: Proceedings of the 2006 IEEE international conference on control applications, pp 2802–2807.Google Scholar
  9. 9.
    Ebbesen S, Elbert P, Guzzella L (2012) Battery state-of-health perceptive energy management for hybrid electric vehicles. IEEE Trans Veh Technol 61(7):2893–2900CrossRefGoogle Scholar
  10. 10.
    Guzzella L, Sciarretta A (2007) Vehicle propulsion systems: introduction to modeling and optimization, 2nd edn. Springer, BerlinGoogle Scholar
  11. 11.
    Haddad WM, Chellaboina V (2008) Nonlinear dynamical systems and control: a lyapunov-based approach. Princeton University Press, NJGoogle Scholar
  12. 12.
    Johnson VH, Wipke KB, Rausen DJ (2000) HEV control strategy for real-time optimization of fuel economy and emissions. SAE Paper number No. 2000–01-1543.Google Scholar
  13. 13.
    Kim N, Cha S, Peng H (2011) Optimal control of hybrid electric vehicles based on Pontryagin’s minimum principle. IEEE Trans Control Systems Technol 19(5):1279–1287CrossRefGoogle Scholar
  14. 14.
    Kim N, Rousseau A (2012) Sufficient conditions of optimal control based on Pontryagin’s minimum principle for use in hybrid electric vehicles. Proc Inst Mech Eng, Part D: J Automobile Eng 226:1160–1170CrossRefGoogle Scholar
  15. 15.
    Koot M, Kessels J, de Jager B, Heemels W, van den Bosh PPJ, Steinbuch M (2005) Energy management strategies for vehicular electric systems. IEEE Trans Veh Technol 54(3):771–782CrossRefGoogle Scholar
  16. 16.
    Lin CC, Peng H, Grizzle JW, Kang JM (2003) Power management strategy for a parallel hybrid electric truck. IEEE Trans Control Syst Technol 11(6):839–849CrossRefGoogle Scholar
  17. 17.
    Mi C, Masrur MA, Gao DW (2011) Hybrid electric vehicles: principles and applications with practical perspectives. Wiley, New YorkCrossRefGoogle Scholar
  18. 18.
    Miller JM (2003) Propulsion systems for hybrid vehicles. The Institution of Electrical Engineers, LondonGoogle Scholar
  19. 19.
    Mura R, Utkin V, Onori S (2013) Ecasting the HEV energy management problem into an infinite-time optimization problem including stability. In: 52nd IEEE CDC.Google Scholar
  20. 20.
    Onori S, Serrao L, Rizzoni G (2010) Adaptive equivalent consumption minimization strategy for hybrid electric vehicles. In: Proceedings of the 2010 ASME DSCC, pp 499–505.Google Scholar
  21. 21.
    Paganelli G, Ercole G, Brahma A, Guezennec Y, Rizzoni G (2001) General supervisory control policy for the energy optimization of charge-sustaining hybrid electric vehicles. JSAE Rev 22:511–518CrossRefGoogle Scholar
  22. 22.
    Pontryagin L, Boltyanskii VG, Gamkrelidze RV, Mishchenko EF (1962) The mathematical theory of optimal processes. Wiley, NJMATHGoogle Scholar
  23. 23.
    Rizzoni G, Peng H (2013) Hybrid and electric vehicles: the role of dynamics and control. In: ASME dynamic systems and control magazine, pp 10–17.Google Scholar
  24. 24.
    Sampathnarayanan B (2013) Analysis and design of stable and optimal energy management strategies for hybrid electric vehicles. Ph.D. Dissertation The Ohio State University.Google Scholar
  25. 25.
    Sampathnarayanan B, Onori S, Yurkovich S (2012) An optimal regulation strategy for energy management of hybrid electric vehicles. In: 51st IEEE CDC.Google Scholar
  26. 26.
    Sciarretta A, Back M, Guzzella L (2004) Optimal control of parallel hybrid electric vehicles. IEEE Trans Control Syst Technol 12:352–363CrossRefGoogle Scholar
  27. 27.
    Sciarretta A, Guzzella L (2007) Control of hybrid electric vehicles. IEEE Control Syst Mag 27:60–70CrossRefGoogle Scholar
  28. 28.
    Serrao L, Onori S, Rizzoni G (2011) A comparative analysis of energy management strategies for hybrid electric vehicles. ASME JDSMC 133(3):1–9Google Scholar
  29. 29.
    Serrao L, Onori S, Rizzoni G (2009) ECMS as a realization of Pontryagin’s minimum principle for HEV control. In: Proceedings of the 2009 American control conference, pp 3964–3969.Google Scholar
  30. 30.
    Serrao L, Onori S, Sciarretta A, Guezennec Y, Rizzoni G (2011) Optimal energy management of hybrid electric vehicles including battery aging. In: Proceedings of the 2011 American control conference, pp 2125–2130.Google Scholar
  31. 31.
    Sundstrom O, Guzzella L (2009) TA generic dynamic programming Matlab function. In: Control applications (CCA) intelligent control (ISIC), 2009 IEEE, pp 1625–1630.Google Scholar
  32. 32.
    Sundstrom O, Guzzella L, Soltic P (2008) Optimal hybridization in two parallel hybrid electric vehicles using dynamic programming. In: Proceedings of the 17th IFAC world congress.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Automotive Engineering DepartmentClemson UniversityClemsonUSA

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