Cover
Vol. 21 No. 3 (2021)

Published: October 31, 2021

Pages: 34-41

Original Article

The Effectiveness of Merge the (A-ECMS) with Heuristics Rule-Based Control Strategy for Energy Management in a Parallel HEVs

Mohammed Khudher Raheem 1 Abdul Baki K. Ali 2 Basil Sh. Munahi 1
1 Department of Mechanical Engineering, College of Engineering, University of Basrah, Basrah, Iraq
2 Energy Consultant for the Ministry of Oil of Iraq, Ministry of Oil, Baghdad, Iraq
History
  • Received: May 11, 2020
  • Revised: July 20, 2021
  • Accepted: August 2, 2021
  • Available Online: October 5, 2021
DOI

https://doi.org/10.33971/bjes.21.3.4

Abstract

The hybrid electric vehicle (HEV) is considered an effective technique to reduce fuel consumption and exhaust emissions. The effectiveness of the HEVs in reducing fuel consumption and exhaust emissions is required an accurate division of the total power demand between energy sources. This aim is reached by an accurate design of energy management strategy (EMS) in the HEVs. Dynamic programming is an effective strategy to found the optimal solution for energy management. This technique requires the driving cycle to be known previously, wherefore it's not suitable to implement in real-time. The Equivalent Consumption Minimization Strategy (ECMS) is an effective technique that can be implemented in real-time. This strategy is used to estimate and adapt the equivalent factor (EF) in real-time, which is used to convert the electric energy from the battery to equivalent fuel cost. The value of the (EF) varies with the driving cycle, therefore, the (EF) is suitable for a certain driving cycle and may lead to weak performance to another. This work proposed a technique based on the battery state of charge feedback called adaptive prediction (AP) to estimate and adapt the equivalent factor in real-time. The best-obtained results are ranged between (11.1 to 32.889) % for several different driving cycles.

Keywords

References

  1. S. H. Mohr, J. Wang, G. Ellem, J. Ward, and D. Giurco, “Projection of world fossil fuels by country”, Fuel, Vol. 141, pp. 120-135, 2015.
  2. C. Musardo, G. Rizzoni, Y. Guezennec, and B. Staccia, “A-ECMS: An adaptive algorithm for hybrid electric vehicle energy management”, European Journal of Control, Vol. 11, Issues 4-5, pp. 509-524, 2005.
  3. W. Enang, “Robust Real-Time Control of a Parallel Hybrid Electric Vehicle”, Ph.D. thesis, Department of Mechanical Engineering, University of Bath, 2017.
  4. P. Leduc, B. Dubar, A. Ranini, and G. Monnier, “Downsizing of gasoline engine: an efficient way to reduce CO 2 emissions”, Oil & Gas Science and Technology, Vol. 58, No. 1, pp. 115-127, 2003.
  5. H. Yeo and H. Kim, “Hardware-in-the-loop simulation of regenerative braking for a hybrid electric vehicle”, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, Vol. 216, Issue 11, pp. 855-864, 2002.
  6. J. C. Guan and B. C. Chen, “Adaptive Power Management Strategy for a Four-Mode Hybrid Electric Vehicle”, Energy Procedia, Vol. 105, pp. 2403-2408, 2017.
  7. H. Lee, C. Song, N. Kim, and S. W. Cha, “Comparative Analysis of Energy Management Strategies for HEV: Dynamic Programming and Reinforcement Learning”, IEEE Access, Vol. 8, pp. 67112-67123, 2020.
  8. W. Enang and C. Bannister, “Robust proportional ECMS control of a parallel hybrid electric vehicle”, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, Vol. 231, Issue 1, pp. 99-119, 2017.
  9. J. T. B. A. Kessels, M. W. T. Koot, P. P. J. Van Den Bosch, and D. B. Kok, “Online energy management for hybrid electric vehicles”, IEEE Transactions on Vehicular Technology, Vol. 57, Issue 6, pp. 3428-3440, 2008.
  10. S. Onori, L. Serrao, and G. Rizzoni, “Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles”, Proceedings of the ASME 2010 Dynamic Systems and Control Conference, Cambridge, Massachusetts, USA, Vol. 1, pp. 499-505, September 1215, 2010. https://doi.org/10.1115/DSCC2010-4211
  11. A. Chasse, A. Sciarretta, and J. Chauvin, “Online optimal control of a parallel hybrid with costate adaptation rule”, IFAC Proceedings Volumes, Vol. 43, Issue 7, pp. 99-104, 2010. https://doi.org/10.3182/20100712-3-DE-2013.00134
  12. Q. Jiang, F. Ossart, C. Marchand, “Real-time HEV energy management strategies”, Symposium de Genie Electrique, Grenoble, France, 7-9 Jun 2016. https://hal.archives-ouvertes.fr/hal-01361674
  13. M. Montazeri-Gh and Z. Pourbafarani, “Near-Optimal SOC Trajectory for Traffic-Based Adaptive PHEV Control Strategy”, IEEE Transactions on Vehicular Technology, Vol. 66, No. 11, pp. 9753-9760, 2017.
  14. S. Wang, X. Huang, J. M. Lopez, X. Xu, and P. Dong, “Fuzzy Adaptive-Equivalent Consumption Minimization Strategy for a Parallel Hybrid Electric Vehicle”, IEEE Access, Vol. 7, pp. 133290-133303, 2019.
  15. T. Deng, P. Tang, and J. L. Luo, “A novel real-time energy management strategy for plug-in hybrid electric vehicles based on equivalence factor dynamic optimization method”, International Journal of Energy Research, Vol. 45, pp. 626-641, 2021. https://doi.org/10.1002/er.5794
  16. J. S. Won, R. Langari, and M. Ehsani, “An energy management and charge sustaining strategy for a parallel hybrid vehicle with CVT”, IEEE Transactions on Control Systems Technology, Vol. 13, No. 2, pp. 313-320, 2005.
  17. 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”, IFAC Proceedings Volumes, Vol. 37, Issue 22, pp. 489-494, 2004. https://doi.org/10.1016/S1474-6670(17)30391-9
  18. S. Jeon, S. Jo, Y. Park, and J. Lee, “Multi-mode driving control of a parallel hybrid electric vehicle using driving pattern recognition”, ASME, Journal of Dynamic System, Measurement, and Control, Vol. 124, No. 1, pp. 141-149, 2002. https://doi.org/10.1115/1.1434264
  19. M. Koot, J. T. B. A. Kessels, B. De Jager, W. Heemels, P. P. J. Van den Bosch, and M. Steinbuch, “Energy management strategies for vehicular electric power systems”, IEEE Transactions on Vehicular Technology, Vol. 54, No. 3, pp. 771-782, 2005.
  20. G. Paganelli, M. Tateno, A. Brahma, G. Rizzoni, and Y. Guezennec, “Control development for a hybrid-electric sport-utility vehicle: strategy, implementation and field test results”, Proceedings of the 2001 American Control Conference. (Cat. No. 01CH37148), Vol. 6, pp. 50645069, 2001. https://doi.org/10.1109/ACC.2001.945787
  21. F. Zhang, J. Xi, and R. Langari, “An adaptive equivalent consumption minimization strategy for parallel hybrid electric vehicle based on Fuzzy PI”, 2016 IEEE Intelligent Vehicles Symposium (IV), pp. 460-465, 2016.
  22. B. K. A. Brooker, K. Haraldsson, T. Hendricks, V. Johnson, K. Kelly, C. D. T. Markel, M. O’Keefe, S. Sprik, K. Wipke, M. Zolot, and D. R. Bharathan, S. Burch, M. Cuddy, “Advanced Vehicle Simulator program (Advisor)”. http://adv-vehicle-sim.sourceforge.net/advisor_doc.html
  23. J. V. Mierlo, P. V. Bossche, and G. Maggetto, “Models of energy sources for EV and HEV: fuel cells, batteries, ultracapacitors, flywheels and engine-generators”, Journal of Power Sources, Vol. 128, Issue 1, pp. 76-89, 2004.
  24. J. Almgren and G. Elingsbo, “Route Based Optimal Control Strategy for Plug-in Hybrid Electric Vehicles”, M.Sc. thesis, Electrical Engineering Department, College of Engineering, Linköping University, 2017. https://liu.diva-portal.org/smash/get/diva2:1113426/FULLTEXT01.pdf
  25. Y. Yang, H. Pei, X. Hu, Y. Liu, C. Hou, and D. Cao, “Fuel economy optimization of power split hybrid vehicles: A rapid dynamic programming approach”, Energy, Vol. 166, pp. 929-938, 2019.
  26. W. Enang, C. Bannister, C. Brace, and C. Vagg, “Modelling and heuristic control of a parallel hybrid electric vehicle”, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, Vol. 229, Issue 11, pp. 1494-1513, 2015.
  27. H. S. Najem, Q. A. Jawad, A. K. Ali, and B. S. Munahi, “Developing a General Methodology and Construct a Driving Cycle with an Economic Performance Evaluation for Basrah City”, International Journal of Engineering & Technology, Vol. 7, No. 4.19, pp. 939-944, 2018.
  28. R. Wang and S. M. Lukic, “Dynamic programming technique in hybrid electric vehicle optimization”, 2012 IEEE International Electric Vehicle Conference, pp. 1-8, 2012. https://doi.org/10.1109/IEVC.2012.6183284
  29. L. Tang, G. Rizzoni, and M. Lukas, “Comparison of dynamic programming-based energy management strategies including battery life optimization”, 2016 International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC), pp. 1-6, 2016.
  30. G. Li and D. Görges, “Energy management strategy for parallel hybrid electric vehicles based on approximate dynamic programming and velocity forecast”, Journal of the Franklin Institute, Vol. 356, Issue 16, pp. 9502-9523, 2019. https://doi.org/10.1016/j.jfranklin.2019.09.011
  31. Qahtan A. Jawad, “Control Strategies for Power Management in Hybrid Electric Vehicle”, Ph.D. thesis, Mechanical Engineering Department, College of Engineering, University of Basrah, September 2019.