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On-board torque management approach to the E-COSM benchmark problem with a prediction-based engine assignment

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Abstract

This paper proposes an energy management strategy for the benchmark problem of E-COSM 2021 to improve the energy efficiency of hybrid electric vehicles (HEVs) on a road with a slope. We assume that HEVs are in a connected environment with real-time vehicle-to-everything information, including geographic information, vehicle-to-infrastructure information and vehicle-to-vehicle information. The benchmark problem to be solved is based on HEV powertrain control using traffic information to achieve fuel economy improvements while satisfying the constraints of driving safety and travel time. The proposed strategy includes multiple rules and model predictive control (MPC). The rules of this strategy are designed based on external environment information to maintain safe driving and to determine the driving mode. To improve fuel economy, the optimal energy management strategy is primarily considered, and to perform real-time energy management via RHC-based optimization in a connected environment with safety constraints, a key issue is to predict the dynamics of the preceding vehicle during the targeted horizon. Therefore, this paper presents a real-time model-based optimization strategy with learning-based prediction of the vehicle’s future speed. To validate the proposed optimization strategy, a powertrain control simulation platform in a traffic-in-the-loop environment is constructed, and case study results performed on the constructed platform are reported and discussed.

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References

  1. Wirasingha, S. G., & Emadi, A. (2010). Classication and review of control strategies for plug-in hybrid electric vehicles. IEEE Transactions on Vehicular Technology, 60(1), 111–122.

    Article  Google Scholar 

  2. Malikopoulos, A. A. (2014). Supervisory power management control algorithms for hybrid electric vehicles: A survey. IEEE Transactions on Intelligent Transportation Systems, 15(5), 1869–1885.

    Article  Google Scholar 

  3. 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.

    Article  MathSciNet  Google Scholar 

  4. Yang, Y., Hu, X., Pei, H., & Peng, Z. (2016). Comparison of power-split and parallel hybrid powertrain architectures with a single electric machine: Dynamic programming approach. Applied Energy, 168, 683–690.

    Article  Google Scholar 

  5. Prez, L. V., Bossio, G. R., Moitre, D., & Garca, G. O. (2006). Optimization of power management in an hybrid electric vehicle using dynamic programming. Mathematics and Computers in Simulation, 73(1–4), 244–254.

    Article  MathSciNet  Google Scholar 

  6. Kim, N., Cha, S., & Peng, H. (2010). Optimal control of hybrid electric vehicles based on Pontryagin’s minimum principle. IEEE Transactions on Control Systems Technology, 19(5), 1279–1287.

    Google Scholar 

  7. Kazemi, H., Fallah, Y. P., Nix, A., & Wayne, S. (2017). Predictive AECMS by utilization of intelligent transportation systems for hybrid electric vehicle powertrain control. IEEE Transactions on Intelligent Vehicles, 2(2), 75–84.

    Article  Google Scholar 

  8. 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 

  9. Paganelli, G., Guerra, T. M., Delprat, S., Santin, J. J., Delhom, M., & Combes, E. (2000). Simulation and assessment of power control strategies for a parallel hybrid car. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 214(7), 705–717.

    Google Scholar 

  10. Zhang, B., Xu, F., & Shen, T. (2020). Receding horizon optimal control of HEVs with on-board prediction of driver’s power demand. IET Intelligent Transport Systems, 14(12), 1534–1545.

    Article  Google Scholar 

  11. 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 Intelligent Vehicles, 2(1), 2–13.

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Zhang, J., Xu, F., Zhang, Y., & Shen, T. L. (2020). ELM-based driver torque demand prediction and real-time optimal energy management strategy for HEVs. Neural Computing and Applications, 32(18), 14411–14429.

    Article  Google Scholar 

  14. Inuzuka, S., Zhang, B., & Shen, T. (2021). Real-time HEV energy management strategy considering road congestion based on deep reinforcement learning. Energies, 14(17), 5270.

    Article  Google Scholar 

  15. Kazemi, H., Fallah, Y. P., Nix, A., & Wayne, S. (2017). Predictive AECMS by utilization of intelligent transportation systems for hybrid electric vehicle powertrain control. IEEE Transactions on Intelligent Vehicles, 2(2), 75–84.

    Article  Google Scholar 

  16. Zhang, S., & Xiong, R. (2015). Adaptive energy management of a plug-in hybrid electric vehicle based on driving pattern recognition and dynamic programming. Applied Energy, 155, 68–78.

    Article  Google Scholar 

  17. Wu, Y., Tan, H., Peng, J., Zhang, H. L., & He, H. W. (2019). Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus. Applied Energy, 247, 454–466.

    Article  Google Scholar 

  18. Liu, T., Li, S. E., & Cao, D. (2017). Reinforcement learning optimized look-ahead energy management of a parallel hybrid electric vehicle. IEEE/ASME Transactions on Mechatronicss, 22(4), 1497–1507.

    Article  Google Scholar 

  19. 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. https://www.enerarxiv.org/thesis/1596463176.pdf.

  20. Zhang, J., Shen, T., & Kako, J. (2019). 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 

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Authors and Affiliations

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

    Bo Zhang & Fuguo Xu

  2. College of Mechanical and Electronic Engineering, Dalian Minzu University, Dalian, 116600, Liaoning, China

    Jiangyan Zhang

Authors
  1. Bo Zhang
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  2. Jiangyan Zhang
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  3. Fuguo Xu
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Corresponding author

Correspondence to Bo Zhang.

Additional information

This work was partially supported by the National Natural Science Foundation of China (No. 61973053). The authors would like to thank the Toyota Motor Corporation for the technical support on this research work.

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Zhang, B., Zhang, J. & Xu, F. On-board torque management approach to the E-COSM benchmark problem with a prediction-based engine assignment. Control Theory Technol. 20, 173–184 (2022). https://doi.org/10.1007/s11768-022-00089-9

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  • DOI: https://doi.org/10.1007/s11768-022-00089-9

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