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Simulation and control of hybrid electric vehicles

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Abstract

This research develops a typical model for a parallel hybrid electric vehicle. Model predictive controllers and simulations for this model have been built to verify the ability of the system to control the speeds and to handle the transitional period for the clutch engagement from the pure electrical driving to the hybrid driving. If the output constraints are the measured speeds and the unmeasured torques which are not strictly imposed and can be violated somewhat during the clutch engagements, a modified model predictive controller with soften output constraints has been developed. Simulations show that the new model predictive controller can control the speeds very well for rapid clutch engagements, which enhance the driving comfort and reduce the jerk on the parallel hybrid electric vehicles.

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References

  1. R. Langari and J. S. Won, “A driving situation awareness-based energy management strategy for parallel hybrid vehicles,” Proc. Future Transportation Technology Conference, California, vol. 1, pp. 95–102, 2003.

    Google Scholar 

  2. R. van der Graaf, D. B. Kok, and E. Spijker, “Integration of drive system, subsystems and auxiliary systems of a flywheel hybrid driveline with respect to design aspects and fuel economy,” Hybridantriebe: Tagung Garching, VDI-Verlag, Düsseldorf, vol. 1, pp. 459–467, 1999.

    Google Scholar 

  3. S. M. Lukic and A. Emadi, “Effects of drivetrain hybridization on fuel economy and dynamic performance of parallel hybrid electric vehicles,” IEEE Trans. on Vehicular Technology, vol. 53, no. 2, pp. 385–389, 2004.

    Article  Google Scholar 

  4. N. Jalil, N. A. Kheir, and M. Salman, “A rule-based energy management strategy for a series hybrid vehicle,” Proc. American Control Conference, Albuquerque, vol. 1, pp. 689–693, 1997.

    Google Scholar 

  5. N. J. Schouten, M. A. Salman, and N. A. Kheir, “Fuzzy logic control for parallel hybrid vehicles,” IEEE Trans. on Control Systems Technology, vol. 10, no. 3, pp. 460–468, 2002.

    Article  Google Scholar 

  6. T. S. Kim, C. Manzie, and R. Sharma, “Model predictive control of velocity and torque split in a parallel hybrid vehicle,” Proc. IEEE International Conference on Systems, San Antonio, vol. 13, no. 2, pp. 2014–2019, 2009.

    Google Scholar 

  7. G. Paganelli, S. Delprat, T. M. Guerra, J. Rimaux, and J. J. Santin, “Equivalent consumption minimization strategy for parallel hybrid powertrains,” Proc. IEEE Vehicular Technology Conference, Columbus, vol. 4, pp. 2076–2081, 2002.

    Google Scholar 

  8. A. Sciarretta, M. Back, and L. Guzzella, “Optimal control of parallelhybrid electric vehicles,” IEEE Trans. on Control Systems Technology, vol. 12, no. 3, pp. 352–363, 2004.

    Article  Google Scholar 

  9. S. J. Qin and T. J. Badgwell, “An overview of industrial model predictive control technology,” AIChE Symposium Series, vol. 316, pp. 232 256, 1997.

    Google Scholar 

  10. R. Findeisen, Z. Diehl, F. Nagy, H. Allgower, and J. Schloder, “Computational feasibility and performance of nonlinear model predictive control schemes,” Proc. International Conference on Chemical Process Control, Arizona, vol. 1, pp. 454–460, 2001.

    Google Scholar 

  11. J. Rawlings, “Tutorial overview of model predictive control,” IEEE Contr. Syst. Mag., vol. 20, no. 3, pp. 38–62, 2000.

    Article  MathSciNet  Google Scholar 

  12. M. Morari and J. Lee, “Model predictive control: past, present and future,” Comput. Chem. Eng., vol. 23, no. 4, pp. 667–682, 1999.

    Article  Google Scholar 

  13. D. Mayne and H. Michalska, “Robust receding horizon control of constrained nonlinear systems,” Proc. IEEE Conference on Decision and Control, vol. 38, pp. 1623–1633, 1993.

    MathSciNet  MATH  Google Scholar 

  14. H. Chen and F. Allgower, Nonlinear Model Predictive Control Schemes with Guaranteed Stability, Kluwer Academic Publishers, New York, 1998.

    Google Scholar 

  15. V. T. Minh and N. Afzulpurkar, “A comparative study on computational schemes for nonlinear model predictive control,” Asian Journal of Control, vol. 8, no. 4, pp. 324–331, 2006.

    Article  MathSciNet  Google Scholar 

  16. V. T. Minh and N. Afzulpurkar, “Robust model predictive control for input saturated and soften state constraints,” Asian Journal of Control, vol. 7, no. 3, pp. 323–329, 2005.

    MathSciNet  Google Scholar 

  17. V. T. Minh and N. Afzulpurkar, “Robustness of model predictive control for ill-conditioned distillation process,” International Journal of Developments in Chemical Engineering and Mineral Processing, vol. 13, no. 3/4, pp. 331–316, 2005.

    Google Scholar 

  18. J. Fredriksson and B. Egbert, “nonlinear control applied to gearshifting in automated manual transmissions,” Proc. IEEE Conference on Decision and Control, Sydney, vol. 1, pp. 444–449, 2000.

    Google Scholar 

  19. B. K. Powell, K. E. Bailey, and S. R. Cikanek, “Dynamic modelling and control of hybrid electric vehicle powertrain systems,” IEEE Control Systems Magazine, vol. 18, no. 5, pp. 17–33, 1998.

    Article  Google Scholar 

  20. V. T. Minh, N. Afzulpurkar, and W. Muhamad, “Fault detection and control of process systems,” Mathematical Problems in Engineering, vol. 2007, Article ID 80321, 20 pages, 2007.

  21. A. Sciaretta and L. Guzzella, “Control of hybrid electric vehicles,” IEEE Control Systems Magazine, vol. 27, no. 2, pp. 60–70, 2007.

    Article  Google Scholar 

  22. G. Ripaccioli, D. Bernardini, S. Di Cairano, A. Bemporad, and V. Kolmanovsky, “A stochastic model predictive control approach for series hybrid electric vehicle power management,” Proc. American Control Conference, Baltimore, vol. 1, pp. 5844–5849, 2010.

    Google Scholar 

  23. D. H. Ji, J. H. Park, W. J. Yoo, and S. C. Won, “Robust memory state feedback model predictive control for discrete-time uncertain state delayed systems,” Applied Mathematics and Computation, vol. 215, no. 6, pp. 2035–2044, 2009.

    Article  MathSciNet  MATH  Google Scholar 

  24. S. M. Lee, S. C. Won, and J. H. Park, “A new robust model predictive control for uncertain systems with input constraints using relaxation matrices,” Journal of Optimization Theory and Application, vol. 138, no. 2, pp. 221–234, 2008.

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, Papua New Guinea University of Technology (UNITECH), Lae, Papua New Guinea

    Vu Trieu Minh & John Pumwa

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  1. Vu Trieu Minh
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  2. John Pumwa
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Correspondence to Vu Trieu Minh.

Additional information

Recommended by Editorial Board member Ju Hyun Park under the direction of Editor Young Il Lee.

This research was supported by Papua New Guinea University of Technology (UNITECH) Papua New Guinea.

Vu Trieu Minh is a visiting professor at the Department of Mechanical Engineering, Papua New Guinea University of Technology (UNITECH), Papua New Guinea. He obtained his B.E of Mechanical from Hanoi University of Technology (HUT) in 1983, an M.E. of Industrial System Engineering and a Ph.D. of Mechatronics from Asian Institute of Technology (AIT) in 1999 and 2004, respectively. He has previously worked in Vietnam, Thailand, Germany, Malaysia, and Papua New Guinea. He has authored over thirty research papers and textbooks in the fields of model-based control algorithms, dynamical systems, advanced process control, and automotive engineering. He is a senior member of IEEE/CSS.

John Pumwa is a professor and the head of Department of Mechanical Engineering, Papua New Guinea University of Technology (UNITECH), Papua New Guinwa. He obtained his B.E. in Mechanical Engineering from the PNG University of Technology (Unitech) in 1981, an MEng (Hons) in Mechanical Engineering from the University of Wollongong, N.S.W., Australia in 1991 and a Ph.D. in Interdisciplinary Engineering from Texas A&M University (TAMU), College Station, Texas, USA in 1997. He has been employed internationally by various learning institutions in Taejon, South Korea, Waco, Texas and Papua New Guinea. He has authored more than 25 conference and journal papers in the fields of engineering materials and dynamics. He is a Fellow of the American Society of Mechanical Engineers (ASME) and the Institution of Engineers Papua New Guinea. He is also a Chartered member and the country representative for the Institution of Mechanical Engineers (IMechE) UK.

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Minh, V.T., Pumwa, J. Simulation and control of hybrid electric vehicles. Int. J. Control Autom. Syst. 10, 308–316 (2012). https://doi.org/10.1007/s12555-012-0211-1

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  • DOI: https://doi.org/10.1007/s12555-012-0211-1

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