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An ECMS Based on Model Prediction Control for Series Hybrid Electric Mine Trucks

Author

Listed:
  • Jichao Liu

    (Jiangsu XCMG Research Institute Co., Ltd., Xuzhou 221004, China)

  • Yanyan Liang

    (Jiangsu XCMG Research Institute Co., Ltd., Xuzhou 221004, China)

  • Zheng Chen

    (School of Materials and Physics, China University of Mining and Technology, Xuzhou 221116, China)

  • Hai Yang

    (Jiangsu XCMG Research Institute Co., Ltd., Xuzhou 221004, China)

Abstract

This paper presents an equivalent consumption minimization strategy (ECMS) based on model predictive control for series hybrid electric mine trucks (SHE-MTs), the objective of which is to minimize fuel consumption. Two critical works are presented to achieve the goal. Firstly, to gain the real-time speed trajectory on-line, a speed prediction model is established by utilizing a recurrent neural network (RNN). Specifically, a hybrid optimization algorithm based on the genetic algorithm (GA) and the particle swarm optimization algorithm (PSOA) is used to enhance the prediction precision of the speed prediction model. Then, on this basis, an ECMS based on MPC (ECMS-MPC) is proposed. In this process, to improve the real-time and working condition adaptability of the ECMS-MPC, the power-optimal fuel consumption mapping model of the range extender is established, and the equivalent factor (EF) is real-time adjusted by means of the PSOA. Finally, taking a cement mining road as the research object, the proposed strategy is simulated with the collected actual vehicle data. The experimental results indicate that the prediction precision of the proposed speed prediction model is over 98%, realizing on-line speed prediction effectively. Furthermore, compared to the existing real-time EMSs, its fuel-saving rate had an increase of more than 13%. This indicates that the designed ECMS-MPC is able to offer a novel and effective method for the on-line energy management of the SHE-MTs.

Suggested Citation

  • Jichao Liu & Yanyan Liang & Zheng Chen & Hai Yang, 2023. "An ECMS Based on Model Prediction Control for Series Hybrid Electric Mine Trucks," Energies, MDPI, vol. 16(9), pages 1-26, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3942-:d:1141264
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    References listed on IDEAS

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    3. Guo, Hongqiang & Sun, Qun & Wang, Chong & Wang, Qinpu & Lu, Silong, 2018. "A systematic design and optimization method of transmission system and power management for a plug-in hybrid electric vehicle," Energy, Elsevier, vol. 148(C), pages 1006-1017.
    4. Sun, Chao & Sun, Fengchun & He, Hongwen, 2017. "Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles," Applied Energy, Elsevier, vol. 185(P2), pages 1644-1653.
    5. Wu, Jingda & He, Hongwen & Peng, Jiankun & Li, Yuecheng & Li, Zhanjiang, 2018. "Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus," Applied Energy, Elsevier, vol. 222(C), pages 799-811.
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    Cited by:

    1. Benxiang Lin & Chao Wei & Fuyong Feng & Tao Liu, 2024. "A Predictive Energy Management Strategy for Heavy Hybrid Electric Vehicles Based on Adaptive Network-Based Fuzzy Inference System-Optimized Time Horizon," Energies, MDPI, vol. 17(10), pages 1-23, May.

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