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A fast algorithm for nonlinear model predictive control applied to HEV energy management systems

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Abstract

This paper presents a fast algorithm for nonlinear model predictive control. In real-time implementation, a nonlinear optimal problem is often rewritten as a nonlinear programming (NLP) problem using the Euler method, which is based on dividing the prediction horizon into N steps in a given time interval. However, real-time optimization is usually limited to slow processes, since the sampling time must be sufficient to support the task’s computational needs. In this study, by combining the Gauss pseudospectral method and model predictive control, a fast algorithm is proposed using fewer discrete points to transcribe an optimal control problem into an NLP problem while ensuring the same computational accuracy as traditional discretization methods. The approach is applied to the torque split control for hybrid electric vehicles (HEV) with a predefined torque demand, and its computational time is at least half that of the Euler method with the same accuracy.

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References

  1. Grancharova A, Johansen T A. Explicit nonlinear model predictive control: theory and applications. In: Theory and Applications. Berlin: Springer, 2012. 429: 39–69

    MathSciNet  MATH  Google Scholar 

  2. Kirches C. Fast Numerical Methods for Mixed-Integer Nonlinear Model-Predictive Control. Wiesbaden: Vieweg+ Teubner Verlag/Springer Fachmedien Wiesbaden, 2011

    Book  MATH  Google Scholar 

  3. De Haan D, Guay M. A real-time framework for model-predictive control of continuous-time nonlinear systems. IEEE Trans Autom Control, 2007, 52: 2047–2057

    Article  MathSciNet  Google Scholar 

  4. Betts J T. Survey of numerical methods for trajectory optimization. J Guid Control Dyna, 1998, 21: 193–207

    Article  MATH  Google Scholar 

  5. Benson D A, Huntington G T, Thorvaldsen T P, et al. Direct trajectory optimization and costate estimation via an orthogonal collocation method. J Guid Control Dyna, 2006, 29: 1435–1440

    Article  Google Scholar 

  6. Garg D, Patterson M A, Hager W W, et al. An overview of three pseudospectral methods for the numerical solution of optimal control problems. Adv Astronaut Sci, 2009, 135: 475–487

    Google Scholar 

  7. Benson D. A Gauss pseudospectral transcription for optimal control. Dissertation for Ph.D. Degree. Cambridge: Massachusetts Institute of Technology, 2005

    Google Scholar 

  8. Huntington G T. Advancement and analysis of a Gauss pseudospectral transcription for optimal control problems. Dissertation for Ph.D. Degree. Cambridge: Massachusetts Institute of Technology, 2007

    Google Scholar 

  9. Xu S, Li S E, Zhang X, et al. Fuel-optimal cruising strategy for road vehicles with step-gear mechanical transmission. IEEE Trans Intell Transport Syst, 2015, 16: 3496–3507

    Article  Google Scholar 

  10. Li S E, Xu S, Huang X, et al. Eco-departure of connected vehicles with V2X communication at signalized intersections. IEEE Trans Veh Tech, 2015, 64: 5439–5449

    Article  Google Scholar 

  11. Burden R L, Faires J D, Reynolds A C. Numerical Analysis. Boston: Wadsworth Publishing Company, 1978

    MATH  Google Scholar 

  12. Sezer V, Gokasan M, Bogosyan S. A novel ECMS and combined cost map approach for high-efficiency series hybrid electric vehicles. IEEE Trans Veh Tech, 2011, 60: 3557–3570

    Article  Google Scholar 

  13. Panday A, Bansal H O. A review of optimal energy management strategies for hybrid electric vehicle. Int J Veh Tech, 2014

    Google Scholar 

  14. Ambuhl D, Guzzella L. Predictive reference signal generator for hybrid electric vehicles. IEEE Trans Veh Tech, 2009, 58: 4730–4740

    Article  Google Scholar 

  15. Adhikari S. Real-time power management of parallel full hybrid electric vehicles. Dissertation for Ph.D. Degree. Melbourne: The University of Melbourne. 2010

    Google Scholar 

  16. Borhan H, Vahidi A, Phillips A M, et al. MPC-based energy management of a power-split hybrid electric vehicle. IEEE Trans Control Syst Tech, 2012, 20: 593–603

    Article  Google Scholar 

  17. Guo L L, Gao B Z, Chen H. On-line shift schedule optimization of 2-speed electric vehicle using moving horizon strategy. IEEE/ASME Trans Mech, 2016, 21: 2858–2869

    Article  Google Scholar 

  18. Borhan H A, Vahidi A, Phillips A M, et al. Predictive energy management of a power-split hybrid electric vehicle. In: Proceedings of IEEE American Control Conference, St. Louis, 2009. 3970–3976

    Google Scholar 

  19. Zeng X, Huang K, Meng F. Model predictive control for parallel hybrid electric vehicles with potential real-time capability. J Autom Safe Energy, 2012, 3: 165–172

    Google Scholar 

  20. Sciarretta A, de Nunzio G, Ojeda L L. Optimal ecodriving control: energy-efficient driving of road vehicles as an optimal control problem. IEEE Control Syst, 2015, 35: 71–90

    Article  MathSciNet  Google Scholar 

  21. Dib W, Chasse A, Moulin P, et al. Optimal energy management for an electric vehicle in eco-driving applications. Control Eng Pract, 2014, 29: 299–307

    Article  Google Scholar 

  22. Gill P E, Murray W, Saunders M A. SNOPT: an SQP algorithm for large-scale constrained optimization. SIAM Rev, 2005, 47: 99–131

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant Nos. 61520106008, 61522307, 61374046) and Graduate Innovation Fund of Jilin University (Grant No. 2016188).

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Authors and Affiliations

  1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, 130025, China

    Lulu Guo, Bingzhao Gao, Yong Li & Hong Chen

  2. Department of Control Science and Engineering, Jilin University (Campus NanLing), Changchun, 130025, China

    Lulu Guo & Hong Chen

  3. School of Mathematics, Jilin University, Changchun, 130012, China

    Yong Li

Authors
  1. Lulu Guo
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  2. Bingzhao Gao
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  3. Yong Li
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  4. Hong Chen
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Correspondence to Hong Chen.

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Guo, L., Gao, B., Li, Y. et al. A fast algorithm for nonlinear model predictive control applied to HEV energy management systems. Sci. China Inf. Sci. 60, 092201 (2017). https://doi.org/10.1007/s11432-016-0269-y

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  • DOI: https://doi.org/10.1007/s11432-016-0269-y

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