Skip to main content
SpringerLink
Log in

Speed planning and energy management strategy of hybrid electric vehicles in a car-following scenario

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

Abstract

The development of intelligent connected technology has brought opportunities and challenges to the design of energy management strategies for hybrid electric vehicles. First, to achieve car-following in a connected environment while reducing vehicle fuel consumption, a power split hybrid electric vehicle was used as the research object, and a mathematical model including engine, motor, generator, battery and vehicle longitudinal dynamics is established. Second, with the goal of vehicle energy saving, a layered optimization framework for hybrid electric vehicles in a networked environment is proposed. The speed planning problem is established in the upper-level controller, and the optimized speed of the vehicle is obtained and input to the lower-level controller. Furthermore, after the lower-level controller reaches the optimized speed, it distributes the torque among the energy sources of the hybrid electric vehicle based on the equivalent consumption minimum strategy. The simulation results show that the proposed layered control framework can achieve good car-following performance and obtain good fuel economy.

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

Similar content being viewed by others

References

  1. Qiu, L., Qian, L., Zomorodi, H., & Pisu, P. (2017). Global optimal energy management control strategies for connected four-wheel-drive hybrid electric vehicles. IET Intelligent Transport Systems, 11(5), 264–272. https://doi.org/10.1049/iet-its.2016.0197.

    Article  Google Scholar 

  2. Gao, J., Li, M., Hu, Y., Chen, H., & Ma, Y. (2019). Challenges and developments of automotive fuel cell hybrid power system and control. Science China. Information Sciences, 62(5), 1–25. https://doi.org/10.1007/s11432-018-9690-y.

    Article  Google Scholar 

  3. Tran, D., Vafaeipour, M., El Baghdadi, M., Barrero, R., Van Mierlo, J., & Hegazy, O. (2020). Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies. Renewable & Sustainable Energy Reviews, 119, 109596. https://doi.org/10.1016/j.rser.2019.109596.

    Article  Google Scholar 

  4. Guo, L., Chen, H., Gao, B., & Liu, Q. (2019). Energy management of HEVs based on velocity profile optimization. Science China. Information Sciences, 62(8), 1–3. https://doi.org/10.1007/s11432-018-9529-7.

    Article  Google Scholar 

  5. Rahbari, O., Vafaeipour, M., Omar, N., Rosen, M. A., Hegazy, O., Timmermans, J., Heibati, S., & Bossche, P. V. D. (2017). An optimal versatile control approach for plug-in electric vehicles to integrate renewable energy sources and smart grids. Energy (Oxford), 134, 1053–1067. https://doi.org/10.1016/j.energy.2017.06.007.

    Article  Google Scholar 

  6. He, H., Wang, Y., Han, R., Han, M., Bai, Y., & Liu, Q. (2021). An improved MPC-based energy management strategy for hybrid vehicles using V2V and V2I communications. Energy (Oxford), 225, 120273. https://doi.org/10.1016/j.energy.2021.120273.

    Article  Google Scholar 

  7. Zhang, B., Cao, W., & Shen, T. (2019). Two-stage on-board optimization of merging velocity planning with energy management for HEVs. Control Theory and Technology, 17(4), 335–345. https://doi.org/10.1007/s11768-019-9129-y.

    Article  MathSciNet  Google Scholar 

  8. Shen, Y., Cui, P., Wang, X., Han, X., & Wang, Y. (2020). Variable structure battery-based fuel cell hybrid power system and its incremental fuzzy logic energy management strategy. International Journal of Hydrogen Energy, 45(21), 12130–12142. https://doi.org/10.1016/j.ijhydene.2020.02.083.

    Article  Google Scholar 

  9. Padmarajan, B. V., McGordon, A., & Jennings, P. A. (2016). Blended rule-based energy management for PHEV: System structure and strategy. IEEE Transactions on Vehicular Technology, 65(10), 8757–8762. https://doi.org/10.1109/TVT.2015.2504510.

    Article  Google Scholar 

  10. Abdelsalam, A. A., & Cui, S. (2012). Fuzzy logic global power management strategy for HEV based on permanent magnet-dual mechanical port machine. In: Proceedings of the 7th international power electronics and motion control conference. Harbin, China. https://doi.org/10.1109/IPEMC.2012.6258963.

  11. Lin, X., & Li, H. (2019). Adaptive control strategy extracted from dynamic programming and combined with driving pattern recognition for SPHEB. International Journal of Automotive Technology, 20(5), 1009–1022. https://doi.org/10.1007/s12239-019-0095-7.

    Article  Google Scholar 

  12. Song, K., Wang, X., Li, F., Sorrentino, M., & Zheng, B. (2020). Pontryagins minimum principle-based real-time energy management strategy for fuel cell hybrid electric vehicle considering both fuel economy and power source durability. Energy (Oxford), 205, 118064. https://doi.org/10.1016/j.energy.2020.118064.

    Article  Google Scholar 

  13. Li, G., & Gorges, D. (2019). Ecological adaptive cruise control and energy management strategy for hybrid electric vehicles based on heuristic dynamic programming. IEEE Transactions on Intelligent Transportation Systems, 20(9), 3526–3535. https://doi.org/10.1109/TITS.2018.2877389.

    Article  Google Scholar 

  14. Guo, L., Gao, B., Gao, Y., & Chen, H. (2017). Optimal energy management for HEVs in eco-driving applications using bi-level MPC. IEEE Transactions on Intelligent Transportation Systems, 18(8), 2153–2162. https://doi.org/10.1109/TITS.2016.2634019.

    Article  Google Scholar 

  15. Amini, M. R., Wang, H., Gong, X., Liao-McPherson, D., Kolmanovsky, I., Sun, J. (2020). Cabin and battery thermal management of connected and automated HEVs for improved energy efficiency using hierarchical model predictive control. IEEE Transactions on Control Systems Technology, 28(5), 1711–1726. https://doi.org/10.1109/TCST.2019.2923792.

  16. Chen, H., Guo, L., Ding, H., Li, Y., & Gao, B. (2019). Real-time predictive cruise control for eco-driving taking into account traffic constraints. IEEE Transactions on Intelligent Transportation Systems, 20(8), 2858–2868. https://doi.org/10.1109/TITS.2018.2868518.

    Article  Google Scholar 

  17. Lopes, D. R., & Evangelou, S. A. (2019). Energy savings from an eco-cooperative adaptive cruise control: A BEV platoon investigation. https://doi.org/10.23919/ECC.2019.8796226.

  18. Zhang, S., & Zhuan, X. (2019). Study on adaptive cruise control strategy for battery electric vehicle. Mathematical Problems in Engineering, 2019, 1–14. https://doi.org/10.1155/2019/7971594.

    Article  Google Scholar 

  19. Cheng, S., Li, L., Chen, X., Fang, S., Wang, X., Wu, X., & Li, W. (2020). Longitudinal autonomous driving based on game theory for intelligent hybrid electric vehicles with connectivity. Applied Energy, 268, 115030. https://doi.org/10.1016/j.apenergy.2020.115030.

    Article  Google Scholar 

  20. 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. https://doi.org/10.1109/TVT.2019.2950042.

    Article  Google Scholar 

  21. 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. https://doi.org/10.1016/j.apenergy.2020.114873.

    Article  Google Scholar 

  22. Buccoliero, G., Anselma, P. G., Bonab, S. A., Belingardi, G., & Emadi, A. (2020). A new energy management strategy for multimode power-split hybrid electric vehicles. IEEE Transactions on Vehicular Technology, 69(1), 172–181. https://doi.org/10.1109/TVT.2019.2950033.

    Article  Google Scholar 

  23. Yu, K., Yang, J., & Yamaguchi, D. (2015). Model predictive control for hybrid vehicle ecological driving using traffic signal and road slope information. Control Theory and Technology, 13(1), 17–28. https://doi.org/10.1007/s11768-015-4058-x.

    Article  Google Scholar 

  24. Hou, S., Gao, J., Zhang, Y., Chen, M., Shi, J., & Chen, H. (2020). A comparison study of battery size optimization and an energy management strategy for FCHEVs based on dynamic programming and convex programming. International Journal of Hydrogen Energy, 45(41), 21858–21872. https://doi.org/10.1016/j.ijhydene.2020.05.248.

    Article  Google Scholar 

  25. 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. https://doi.org/10.1007/s11768-020-0020-7.

    Article  MathSciNet  MATH  Google Scholar 

  26. Moser, D., Schmied, R., Waschl, H., & del Re, L. (2018). Flexible spacing adaptive cruise control using stochastic model predictive control. IEEE Transactions on Control Systems Technology, 26(1), 114–127. https://doi.org/10.1109/TCST.2017.2658193.

    Article  Google Scholar 

  27. Guo, Q., Zhao, Z., Shen, P., & Zhou, P. (2019). Optimization management of hybrid energy source of fuel cell truck based on model predictive control using traffic light information. Control Theory and Technology, 17(4), 309–324. https://doi.org/10.1007/s11768-019-9118-1.

    Article  MathSciNet  Google Scholar 

  28. Kim, N., Jeong, J., & Zheng, C. (2019). Adaptive energy management strategy for plug-in hybrid electric vehicles with Pontryagins minimum principle based on daily driving patterns. International Journal of Precision Engineering and Manufacturing-Green Technology, 6(3), 539–548. https://doi.org/10.1007/s40684-019-00046-z.

    Article  Google Scholar 

  29. Xu, F., Tsunogawa, H., Kako, J., Hu, X., Li, S. E., Shen, T., Eriksson, L., & Guardiola, C. (2020). Real-time energy optimization of HEVs under connected environment: ECOSM 2021 benchmark problem and a case study. IEEE/ASME Transactions on Mechatronics, 24, 1–36.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, 130022, Jilin, China

    Shengyan Hou & Jinwu Gao

  2. Department of Control Science and Engineering, Jilin University, Changchun, 130025, Jilin, China

    Shengyan Hou, Hong Chen & Jinwu Gao

  3. Department of Control Science and Engineering, Tongji University, Shanghai, 200092, China

    Hong Chen

  4. Suzhou Zhito Technology Co., Ltd., Suzhou, 215129, Jiangsu, China

    Yu Zhang

Authors
  1. Shengyan Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jinwu Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinwu Gao.

Additional information

This work was supported by the National Natural Science Foundation of China (Grant No. 62111530196), and the Technology Development Program of Jilin Province (Grant No.20200501010G X).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hou, S., Chen, H., Zhang, Y. et al. Speed planning and energy management strategy of hybrid electric vehicles in a car-following scenario. Control Theory Technol. 20, 185–196 (2022). https://doi.org/10.1007/s11768-022-00088-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11768-022-00088-w

Keywords

Search

Navigation