Cover
Vol. 18 No. 1 (2022)

Published: June 30, 2022

Pages: 93-102

Original Article

Building A Control Unit of A Series-Parallel Hybrid Electric Vehicle by Using A Nonlinear Model Predictive Control (NMPC) Strategy

Maher Al-Flehawee 1 Auday Al-Mayyahi
1Department of Electrical Engineering, College of Engineering, University of Basrah, Basrah, Iraq
History
  • Received: February 21, 2022
  • Revised: March 23, 2022
  • Accepted: March 24, 2022
  • Available Online: March 31, 2022
DOI

https://doi.org/10.37917/ijeee.18.1.11

Abstract

Hybrid electric vehicles have received considerable attention because of their ability to improve fuel consumption compared to conventional vehicles. In this paper, a series-parallel hybrid electric vehicle is used because they combine the advantages of the other two configurations. In this paper, the control unit for a series-parallel hybrid electric vehicle is implemented using a Nonlinear Model Predictive Control (NMPC) strategy. The NMPC strategy needs to create a vehicle energy management optimization problem, which consists of the cost function and its constraints. The cost function describes the required control objectives, which are to improve fuel consumption and obtain a good dynamic response to the required speed while maintaining a stable value of the state of charge (SOC) for batteries. While the cost function is subject to the physical constraints and the mathematical prediction model that evaluate vehicle's behavior based on the current vehicle measurements. The optimization problem is solved at each sampling step using the (SQP) algorithm to obtain the optimum operating points of the vehicle's energy converters, which are represented by the torque of the vehicle components.

Keywords

References

  1. M.-K. Tran, M. Akinsanya, S. Panchal, R. Fraser, and M. Fowler, “Design of a Hybrid Electric Vehicle Powertrain for Performance Optimization Considering Various Powertrain Components and Configurations,” Vehicles, vol. 3, no. 1, pp. 20–32, 2020, doi: 10.3390/vehicles3010002.
  2. S. B. Kanap, “Modeling and Simulation with Optimal Gear Ratio for a Forward- Looking , Velocity-Driven , Power-Split Hybrid Electric Vehicle,” Western Michigan University, 2019.
  3. S. Borthakur and S. C. Subramanian, “Optimized Design and Analysis of a Series-Parallel Hybrid Electric Vehicle Powertrain for a Heavy Duty Truck,” IFAC- PapersOnLine, vol. 51, no. 1, pp. 184–189, 2018, doi: 10.1016/j.ifacol.2018.05.034.
  4. J. P. Torreglosa, P. Garcia-Triviño, D. Vera, and D. A. López-García, “Analyzing the improvements of energy management systems for hybrid electric vehicles using a systematic literature review: How far are these controls from rule-based controls used in commercial vehicles?,” Appl. Sci., vol. 10, no. 23, pp. 1–25, 2020.
  5. Y. Hu, W. Li, K. Xu, T. Zahid, F. Qin, and C. Li, “Energy management strategy for a hybrid electric vehicle based on deep reinforcement learning,” Appl. Sci., vol. 8, no. 2, 2018, doi: 10.3390/app8020187.
  6. A. M. Ali and D. Söffker, “Towards optimal power management of hybrid electric vehicles in real-time: A review on methods, challenges, and state-of-the-art solutions,” Energies, vol. 11, no. 3, pp. 1–24, 2018, doi: 10.3390/en11030476.
  7. J. Hu, X. Jiang, and L. Zheng, “Design and analysis of hybrid electric vehicle powertrain configurations considering energy transformation,” Int. J. Energy Res., vol. 42, no. 15, pp. 4719–4729, 2018, doi: 10.1002/er.4225.
  8. J. Yang and G. G. Zhu, “Model predictive control of a power split hybrid powertrain,” in 2016 American Control Conference (ACC), 2016, vol. 2016-July, pp. 617-622. IEEE.
  9. H. A. Borhan, A. Vahidi, A. M. Phillips, M. L. Kuang, and I. V. Kolmanovsky, “Predictive energy management of a power-split hybrid electric vehicle,” 2009 Am. Control Conf., pp. 3970-3976. IEEE, 2009.
  10. P. Poramapojana and B. Chen, “Minimizing HEV fuel Al-Flehawee & Al-Mayyahi consumption using model predictive control,” Proc. 2012 8th IEEE/ASME Int. Conf. Mechatron. Embed. Syst. Appl. MESA 2012, pp. 148–153, 2012.
  11. K. B. Wipke, M. R. Cuddy, and S. D. Burch, “ADVISOR 2.1: A user-friendly advanced powertrain simulation using a combined backward/forward approach,” IEEE Trans. Veh. Technol., vol. 48, no. 6, pp. 1751–1761, 1999.
  12. J. Liu, H. Peng, and Z. Filipi, “Modeling and analysis of the Toyota Hybrid System,” IEEE/ASME Int. Conf. Adv. Intell. Mechatronics, AIM, vol. 1, pp. 134–139, 2005.
  13. J. Liu and H. Peng, “Modeling and Control of a Power- Split Hybrid Vehicle,” IEEE Trans. Control Syst. Technol., vol. 16, no. 6, pp. 1242–1251, 2008.
  14. M. Joševski and D. Abel, “Energy management of parallel hybrid electric vehicles based on stochastic model predictive control,” IFAC Proc. Vol. 47(3), pp. 2132–2137, 2014.
  15. J. Liu and H. Peng, “Modeling and control of a power- split hybrid vehicle,” IEEE Trans. Control Syst. Technol., vol. 16, no. 6, pp. 1242–1251, 2008, doi: 10.1109/TCST.2008.919447.
  16. W. Wang, S. Jia, C. Xiang, K. Huang, and Y. Zhao, “Model predictive control-based controller design for a power-split hybrid electric vehicle,” in Proceedings of 2014 International Conference on Modelling, Identification and Control, 2015, pp. 219–224.
  17. Y. Li, H. He, J. Peng, and J. Wu, “Energy Management Strategy for a Series Hybrid Electric Vehicle Using Improved Deep Q-network Learning Algorithm with Prioritized Replay,” DEStech Trans. Environ. Energy Earth Sci., no. iceee, pp. 1–6, 2018, doi: 10.12783/dteees/iceee2018/27794.
  18. S. H. Brown, “Multiple Linear Regression Analysis : A Matrix Approach with MATLAB,” Alabama J. Math., pp. 1–3, 2009.
  19. H. A. F. Almurib, M. Askari, and M. Moghavvemi, “Hard constraints explicit model predictive control of an inverted pendulum,” Iraq J. Electr. Electron. Eng., vol. 6, no. 1, pp. 28–32, 2010, doi: 10.33762/eeej.2010.54340.
  20. F. Allgöwer, R. Findeisen, and Z. K. Nagy, “Nonlinear model predictive control: From theory to application,” J. Chinese Inst. Chem. Eng., vol. 35, no. 3, pp. 299–315, 2004.
  21. M. Keller et al., “Teaching Nonlinear Model Predictive Control with MATLAB/Simulink and an Internal Combustion Engine Test Bench,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 17190–17197, 2020.
  22. O. Mikuláš, “A Framework for Nonlinear Model Predictive Control,” Czech Technical University in Prague, 2016.
  23. A. Linder, R. Kanchan, R. Kennel, and P. Stolze, Model-Based Predictive Control of Electric Drives. Cuvillier Verlag Göttingen, 2010.
  24. M. A. Henson, “Nonlinear model predictive control: Current status and future directions,” Comput. Chem. Eng., vol. 23, no. 2, pp. 187–202, 1998.
  25. X. He and F. V. Lima, “A Modified SQP-based Model Predictive Control Algorithm: Application to Supercritical Coal-fired Power Plant Cycling,” Ind. Eng. Chem. Res., vol. 59, no. 35, pp. 15671–15681, 2020.
  26. A. Bemporad, N. Lawrence Ricker, and M. Morari, “Model Predictive Control Toolbox TM Reference How to Contact MathWorks - R2019b,” p. 994, 2019.
  27. J. Nocedal and S. J. Wright, Numerical optimization. Springer New York, 2006.
  28. S. Zhao, H. Wu, L. Shi, C. Huang, and X. Yang, “Research on Performance Seeking Control of Turbofan Engine at Cruise Status Based on Sequential Quadratic Programming Algorithm,” MATEC Web Conf., vol. 179, 2018.
  29. S. Sun, “Geometric Optimization of Radiative Enclosures Using Sequential Quadratic Programming Algorithm,” ES Energy Environ., pp. 57–68, 2019.
  30. P. E. Gill, W. Murray, and M. H. Wright, Practical Optimization, vol. PER-5, no. 7. ACADEMIC PRESS, INC., 1985.
  31. R. Tavakoli, “Active Set Algorithm for Large-Scale Continuous Knapsack Problems with Application to Topology Optimization Problems,” arXiv Prepr. arXiv1005.3144, 2010.
  32. A. B. Jadhav, “Optimal Energy Management for Forward-Looking Serial-Parallel Hybrid Electric Vehicle Using Rule-Based Control Strategy,” Western Michigan University, 2019.