Skip to main content
SpringerLink
Log in

Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution

  • Research Article
  • Published:
Control Theory and Technology Aims and scope Submit manuscript

Abstract

In this paper, we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles (HEVs) on a road with slope. Moreover, it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything (V2X), including geographic information, vehicle-to-infrastructure (V2I) information and vehicle-to-vehicle (V2V) information. The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator, where the running route is generated based on real-world data with slope and intersection position. The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time. To show the HEV powertrain characteristics, a case study is given with the speed planning and energy management strategy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

References

  1. Zhou, Q., Zhao, D., Shuai, B., Li, Y., Williams, H., & Xu, H. (2021). Knowledge implementation and transfer with an adaptive learning network for real-time power management of the plug-in hybrid vehicle. IEEE Transactions on Neural Networks and Learning Systems, 32(12), 5298–5308. https://doi.org/10.1109/TNNLS.2021.3093429

    Article  Google Scholar 

  2. Xu, F., & Shen, T. (2021). Decentralized optimal merging control with optimization of energy consumption for connected hybrid electric vehicles. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2021.3054903

    Article  Google Scholar 

  3. Zhuang, W., Li, S., Zhang, X., et al. (2020). A survey of powertrain configuration studies on hybrid electric vehicles. Applied Energy, 262, 114553.

    Article  Google Scholar 

  4. Wang, S., Chen, K., Zhao, F., & Hao, H. (2019). Technology pathways for complying with corporate average fuel consumption regulations up to 2030: A case study of China. Applied Energy, 241, 257–277.

    Article  Google Scholar 

  5. Zhang, J., Shen, T., & Kako, J. (2020). Short-term optimal energy management of power-split hybrid electric vehicles under velocity tracking control. IEEE Transactions on Vehicular Technology, 69(1), 182–193.

    Article  Google Scholar 

  6. Asaei, B. (2010). A fuzzy-genetic algorithm approach for finding a new HEV control strategy idea. 1st Power Electronic and Drive Systems and Technologies Conference, pp. 224 – 229. Tehran, Iran.

  7. Wu, J., Zhang, C. H., & Cui, N. X. (2008). PSO algorithm-based parameter optimization for HEV powertrain and its control strategy. International Journal of Automotive Technology, 9(1), 53–59.

    Article  Google Scholar 

  8. Lin, C. C., Peng, H., Grizzle, J. W., & Kang, J.-M. (2003). Power management strategy for a parallel hybrid electric truck. IEEE Transactions on Control Systems Technology, 11(6), 839–849.

    Article  Google Scholar 

  9. Luján, J. M., Guardiola, C., Pla, B., & Reig, A. (2018). Analytical optimal solution to the energy management problem in series hybrid electric vehicles. IEEE Transactions on Vehicular Technology, 67(8): 6803 – 6813.

  10. Larsson, V., Johannesson, L., & Egardt, B. (2014). Analytic solutions to the dynamic programming subproblem in hybrid vehicle energy management. IEEE Transactions on Vehicular Technology, 64(4), 1458–1467.

    Article  Google Scholar 

  11. Serrao, L., Onori, S., & Rizzoni, G. (2009). ECMS as a realization of Pontryagin’s minimum principle for HEV control. American Control Conference, pp. 3964-3969. St. Louis, MO, USA.

  12. Kim, N., Cha, S., & Peng, H. (2011). Optimal equivalent fuel consumption for hybrid electric vehicles. IEEE Transactions on Control Systems Technology, 20(3), 817–825.

    Google Scholar 

  13. Rezaei, A., Burl, J. B., Solouk, A., Zhou, B., et al. (2017). Catch energy saving opportunity (CESO), an instantaneous optimal energy management strategy for series hybrid electric vehicles. Applied Energy, 208, 655–665.

    Article  Google Scholar 

  14. Xie, S., Hu, X., Qi, S., & Lang, K. (2018). An artificial neural network-enhanced energy management strategy for plug-in hybrid electric vehicles. Energy, 163, 837–848.

    Article  Google Scholar 

  15. Zhang, J., & Shen, T. (2016). Real-time fuel economy optimization with nonlinear MPC for PHEVs. IEEE Transactions on Control Systems Technology, 24(6), 2167–2175.

    Article  Google Scholar 

  16. Sciarretta, A., Serrao, L., Dewangan, P. C., et al. (2014). A control benchmark on the energy management of a plug-in hybrid electric vehicle. Control Engineering Practice, 29, 287–298.

    Article  Google Scholar 

  17. Lars, E. (2019). An overview of various control benchmarks with a focus on automotive control. Control Theory and Technology, 17(2), 121–130.

    Article  MathSciNet  MATH  Google Scholar 

  18. Moura, S. J., Fathy, H. K., Callaway, D. S., & Stein, J. L. (2010). A stochastic optimal control approach for power management in plug-in hybrid electric vehicles. IEEE Transactions on Control Systems Technology, 19(3), 545–555.

    Article  Google Scholar 

  19. Sun, C., Hu, X., Moura, S. J., & Sun, F. (2014). Velocity predictors for predictive energy management in hybrid electric vehicles. IEEE Transactions on Control Systems Technology, 23(3), 1197–1204.

    Google Scholar 

  20. Xiang, C., Ding, F., Wang, W., & He, W. (2017). Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control. Applied Energy, 189, 640–653.

    Article  Google Scholar 

  21. Sun, C., Sun, F., & He, H. (2017). Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles. Applied Energy, 185, 1644–1653.

    Article  Google Scholar 

  22. Zhang, F., Hu, X., Langari, R., & Cao, D. (2019). Energy management strategies of connected HEVs and PHEVs: Recent progress and outlook. Progress in Energy and Combustion Science, 73, 235–256.

    Article  Google Scholar 

  23. Yang, C., Zha, M., Wang, W., Liu, K., & Xiang, C. (2020). Efficient energy management strategy for hybrid electric vehicles/plug-in hybrid electric vehicles: Review and recent advances under intelligent transportation system. IET Intelligent Transport Systems, 14(7), 702–711. https://doi.org/10.1049/iet-its.2019.0606

    Article  Google Scholar 

  24. Zhang, J., Xu, F., Zhang, Y., & Shen, T. (2019). ELM-based driver torque demand prediction and real-time optimal energy management strategy for HEVs. Neural Computing and Applications, 32: 14411C14429.

  25. Zhang, B., Zhang, J., Xu, F., & Shen, T. (2020). Optimal control of power-split hybrid electric powertrains with minimization of energy consumption. Applied Energy, 266, 114873.

  26. Zhang, F., Xi, J., & Langari, R. (2016). Real-time energy management strategy based on velocity forecasts using V2V and V2I communications. IEEE Transactions on Intelligent Transportation Systems, 18(2), 416–430.

    Article  Google Scholar 

  27. Li, J., Zhou, Q., He, Y., et al. (2019). Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles. Applied Energy, 253, 113617.

    Article  Google Scholar 

  28. Qi, X., Wu, G., Hao, P., Boriboonsomsin, K., & Barth, M. J. (2017). Integrated-connected eco-driving system for PHEVs with co-optimization of vehicle dynamics and powertrain operations. IEEE Transactions on Vehicular Technology, 2(1), 2–13.

    Google Scholar 

  29. Uebel, S., Murgovski, N., B\(\ddot{\rm a}\)ker, B., & Sj\(\ddot{\rm o}\)berg, J. (2019). A two-level mpc for energy management including velocity control of hybrid electric vehicles. IEEE Transactions on Vehicular Technology, 68(6): 5494–5505.

  30. Chen, B., Evangelou, S. A., & Lot, R. (2019). Hybrid electric vehicle two-step fuel efficiency optimization with decoupled energy management and speed control. IEEE Transactions on Vehicular Technology, 68(12), 11492–11504.

    Article  Google Scholar 

  31. Wang, S., & Lin, X. (2020). Eco-driving control of connected and automated hybrid vehicles in mixed driving scenarios. Applied Energy, 271, 115233.

    Article  Google Scholar 

  32. Zhang, J., & Xu, F. (2020). Real-time optimization of energy consumption under adaptive cruise control for connected HEVs. Control Theory and Technology, 18(2), 182–192.

    Article  MathSciNet  MATH  Google Scholar 

  33. Fu, Q., Xu, F., Shen, T., & Takai, K. (2020). Distributed optimal energy consumption control of HEVs under MFG-based speed consensus. Control Theory and Technology, 18(2), 193–203.

    Article  MathSciNet  MATH  Google Scholar 

  34. Chen, B., Evangelou, S. A., & Lot, R. (2019). Series hybrid electric vehicle simultaneous energy management and driving speed optimization. IEEE/ASME Transactions on Mechatronics, 24(6), 2756–2767.

    Article  Google Scholar 

  35. Hu, Q., Amini, M. R., Feng, Y., Yang, Z., Wang, H., Kolmanovsky, I., & Seeds, J. B. (2020). Engine and aftertreatment co-optimization of connected HEVs via multi-range vehicle speed planning and prediction. SAE Technical Paper, -01-0590.

  36. Xu, F., & Shen, T. (2020). Look-ahead prediction-based real-time optimal energy management for connected HEVs. IEEE Transactions on Vehicular Technology, 69(3), 2537–2551.

    Article  MathSciNet  Google Scholar 

  37. Xu, F., & Shen, T. (2019). MPC-based optimal control for diesel engine coupled with lean NOx trap system. SICE Journal of Control, Measurement, and System Integration, 12(3), 94–101.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Engineering and Applied Sciences, Sophia University, Tokyo, 102-8554, Japan

    Fuguo Xu & Tielong Shen

  2. Higashi-Fuji Research Center, Toyota Motor Corporation, Shizuoka, 410-1107, Japan

    Hiroki Tsunogawa & Junichi Kako

  3. The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China

    Xiaosong Hu

  4. The State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China

    Shengbo Eben Li

  5. Vehicular Systems, Department Electrical Engineering, Linköping University, SE, 581 83, Linköping, Sweden

    Lars Eriksson

  6. Departamento de Máquinas y Motores Térmicos, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain

    Carlos Guardiola

Authors
  1. Fuguo Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hiroki Tsunogawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Junichi Kako
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaosong Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shengbo Eben Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tielong Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lars Eriksson
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Carlos Guardiola
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fuguo Xu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, F., Tsunogawa, H., Kako, J. et al. Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution. Control Theory Technol. 20, 145–160 (2022). https://doi.org/10.1007/s11768-022-00086-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11768-022-00086-y

Keywords

Search

Navigation