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Predictive energy management of fuel cell supercapacitor hybrid construction equipment

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  • Li, Tianyu
  • Liu, Huiying
  • Ding, Daolin

Abstract

The application of fuel cell hybrid construction equipment (FCHCE) represents an attractive option for future industrial development. Construction equipment has highly repetitive modes of operation, which can be used for predictive control. However, the loads usually change markedly and frequently, leading to adverse scenarios for energy management. In this paper, we focus on alleviating this problem by presenting a predictive energy management strategy based on a model predictive control (MPC) framework for FCHCE. The supervisory-level energy management objectives are to respond to rapid variations in the load of the construction equipment while minimizing the fuel consumption, improving fuel cell durability, and maintaining the state of charge (SoC) of supercapacitors within the allowable bounds. To improve the performance and practicality of the predictive controllers, we present two power demand prediction methodologies based on Markov chains and neural networks. Simulations are carried out on the representative driving cycles of a wheel loader. Simulation results validate the feasibility and effectiveness of the proposed MPC. Neural network model made better predictions than the Markov chain model, which is preferable when modeling comprehensive driving cycles.

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  • Li, Tianyu & Liu, Huiying & Ding, Daolin, 2018. "Predictive energy management of fuel cell supercapacitor hybrid construction equipment," Energy, Elsevier, vol. 149(C), pages 718-729.
    • Handle: RePEc:eee:energy:v:149:y:2018:i:c:p:718-729
    DOI: 10.1016/j.energy.2018.02.101
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    Cited by:

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    2. Lu Han & Xiaohong Jiao & Zhao Zhang, 2020. "Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging," Energies, MDPI, vol. 13(1), pages 1-22, January.
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    11. Sulaiman, N. & Hannan, M.A. & Mohamed, A. & Ker, P.J. & Majlan, E.H. & Wan Daud, W.R., 2018. "Optimization of energy management system for fuel-cell hybrid electric vehicles: Issues and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 2061-2079.
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    13. Jinquan, Guo & Hongwen, He & Jianwei, Li & Qingwu, Liu, 2022. "Driving information process system-based real-time energy management for the fuel cell bus to minimize fuel cell engine aging and energy consumption," Energy, Elsevier, vol. 248(C).
    14. He, Xiangyu & Liu, Hao & He, Shanghong & Hu, Bili & Xiao, Guangxin, 2019. "Research on the energy efficiency of energy regeneration systems for a battery-powered hydrostatic vehicle," Energy, Elsevier, vol. 178(C), pages 400-418.
    15. Mojgan Fayyazi & Paramjotsingh Sardar & Sumit Infent Thomas & Roonak Daghigh & Ali Jamali & Thomas Esch & Hans Kemper & Reza Langari & Hamid Khayyam, 2023. "Artificial Intelligence/Machine Learning in Energy Management Systems, Control, and Optimization of Hydrogen Fuel Cell Vehicles," Sustainability, MDPI, vol. 15(6), pages 1-38, March.
    16. Wei Zhang & Jixin Wang & Shaofeng Du & Hongfeng Ma & Wenjun Zhao & Haojie Li, 2019. "Energy Management Strategies for Hybrid Construction Machinery: Evolution, Classification, Comparison and Future Trends," Energies, MDPI, vol. 12(10), pages 1-26, May.
    17. Hoai Vu Anh Truong & Hoang Vu Dao & Tri Cuong Do & Cong Minh Ho & Xuan Dinh To & Tri Dung Dang & Kyoung Kwan Ahn, 2020. "Mapping Fuzzy Energy Management Strategy for PEM Fuel Cell–Battery–Supercapacitor Hybrid Excavator," Energies, MDPI, vol. 13(13), pages 1-27, July.
    18. Ren, Xiaoxia & Ye, Jinze & Xie, Liping & Lin, Xinyou, 2024. "Battery longevity-conscious energy management predictive control strategy optimized by using deep reinforcement learning algorithm for a fuel cell hybrid electric vehicle," Energy, Elsevier, vol. 286(C).
    19. Zhang, Wei & Wang, Jixin & Liu, Yong & Gao, Guangzong & Liang, Siwen & Ma, Hongfeng, 2020. "Reinforcement learning-based intelligent energy management architecture for hybrid construction machinery," Applied Energy, Elsevier, vol. 275(C).
    20. Ioan-Sorin Sorlei & Nicu Bizon & Phatiphat Thounthong & Mihai Varlam & Elena Carcadea & Mihai Culcer & Mariana Iliescu & Mircea Raceanu, 2021. "Fuel Cell Electric Vehicles—A Brief Review of Current Topologies and Energy Management Strategies," Energies, MDPI, vol. 14(1), pages 1-29, January.
    21. Jixiang Yang & Yongming Bian & Meng Yang & Jie Shao & Ao Liang, 2021. "Parameter Matching of Energy Regeneration System for Parallel Hydraulic Hybrid Loader," Energies, MDPI, vol. 14(16), pages 1-26, August.
    22. Li, Tianyu & Huang, Lingtao & Liu, Huiying, 2019. "Energy management and economic analysis for a fuel cell supercapacitor excavator," Energy, Elsevier, vol. 172(C), pages 840-851.
    23. Zhang, Wei & Wang, Jixin & Xu, Zhenyu & Shen, Yuying & Gao, Guangzong, 2022. "A generalized energy management framework for hybrid construction vehicles via model-based reinforcement learning," Energy, Elsevier, vol. 260(C).
    24. Yuan, Jingni & Yang, Lin, 2019. "Predictive energy management strategy for connected 48V hybrid electric vehicles," Energy, Elsevier, vol. 187(C).

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