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Research on collaborative optimization of fuel cell tractor transmission system and control strategy parameters

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The distributed fuel cell tractor is a new type of power tractor. The transmission system and control strategy parameters affect the energy utilization efficiency of the entire machine. There is currently no research in this area. In order to solve the problem of low energy utilization of the whole machine of distributed dual-motor-driven hydrogen fuel cell tractor, a cooperative optimization method is proposed, based on particle swarm optimization (PSO) algorithm for the parameters of the transmission system and energy-saving control strategy of distributed dual-motor-driven hydrogen fuel cell tractor. According to the tractor dynamics analysis and equivalent hydrogen consumption theory, a fuel cell tractor transmission parameter-equivalent hydrogen consumption model is established, The wheel-side transmission ratio and the upper and lower threshold values of the hydrogen fuel cell working power are taken as control variables, and the minimum equivalent hydrogen consumption is taken as the optimization goal, the optimization method is simulated and tested based on the MATLAB simulation platform. The results show that under plowing conditions, compared with the rule-based control strategy, the proposed collaborative optimization method of the fuel cell tractor transmission system and control strategy parameters can reasonably control the operating status of the fuel cell and the power battery, ensure that the fuel cell works in a high-efficiency range, enhance the overall performance of the fuel cell system, and control the power battery state of charge (SOC) to remain in a reasonable range. The tractor equivalent hydrogen consumption is reduced by 7.84%.
Rocznik
Strony
art. no. e153232
Opis fizyczny
Bibliogr. 33 poz., rys., tab., wykr.
Twórcy
autor
  • College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, China
  • State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471039, China
autor
  • College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, China
  • College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, China
  • State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471039, China
autor
  • YTO Group Corporation, Luoyang 471004, China
autor
  • College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, China
  • State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471039, China
autor
  • College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, China
  • State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471039, China
Bibliografia
  • [1] L. Xu, J. Zhang, X. Yan, S. Zhao, Y. Wu, and M. Liu, “Review of Research for Agricultural Equipment Electrification Technology,” Trans. Chin. Soc. Agric. Mach., vol. 54, no. 09, pp. 1–12, 2023, doi: 10.6041/j.issn.1000-1298.2023.09.001.
  • [2] M. Liu et al., “Review of Development Process and Research Status of Electric Tractors,” Trans. Chin. Soc. Agric. Mach., vol. 53, no. S1, pp. 348–364, 2022, doi: 10.6041/j.issn.1000-1298.2022.S1.039.
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  • [6] J. Teng, Y. Zhang, and X. Ruan, “Some important sciemtific problems for development of renewable and new energy – The only way for development of non-fossil energy,” Prog. Geophys., vol. 25, no. 04, pp. 1115–1152, 2010, doi: 10.3969/j.issn.1004-2903.2010.04.001.
  • [7] J. Carroquino, J.-L. Bernal-Agustín, and R. Dufo-López, “Standalone Renewable Energy and Hydrogen in an Agricultural Context: A Demonstrative Case,” Sustainability, vol. 11, no. 4, p. 951, 2019, doi: 10.3390/su11040951.
  • [8] Y. Chen, B. Xie, Y. Du, and E. Mao, “Powertrain parameter matching and optimal design of dual-motor driven electric tractor,” Int. J. Agric. Biol. Eng., vol. 12, no. 1, pp. 33–41, 2019, doi: 10.25165/j.ijabe.20191201.3720.
  • [9] X. Zou et al., “Driving System Parameter Optimization and Energy Control Strategy of Hybrid Electric Buses,” J. Uni. Jinan-Sci. Technol., vol. 36, no. 03, pp. 315–321+337, 2022, doi: 10.13349/j.cnki.jdxbn.20211214.007.
  • [10] B. Li et al., “Optimization method of speed ratio for power-shift transmission of agricultural tractor,” Machines, vol. 11, no. 4, p. 438, 2023, doi: 10.3390/machines11040438.
  • [11] X. Li et al., “Optimized Design and Validation of Distributed Drive System for ElectricTractor Based on Multi-island Genetic Algorithm,” Trans. Chin. Soc. Agric. Mach., vol. 55, no. 03, pp. 401–411, 2024, doi: 10.6041/j.issn.1000-1298.2024.03.040.
  • [12] X. Li et al., “Parameters collaborative optimization design and innovation verification approach for fuel cell distributed drive electric tractor,” Energy, vol. 292, p. 130485, 2024, doi: 10.1016/j.energy.2024.130485.
  • [13] S. Hou et al., “Reinforcement learning-based energy optimization for a fuel cell electric vehicle,” in 2022 4th International Conference on Smart Power & Internet Energy Systems (SPIES), 2022, pp. 1928–1933, doi: 10.1109/SPIES55999.2022.10082644.
  • [14] W. Xu, M. Liu, L. Xu, and S. Zhang, “Energy management strategy of hydrogen fuel cell/battery/ultracapacitor hybrid tractor based on efficiency optimization,” Appl. Sci., vol. 13, no. 1, p. 151, 2022, doi: 10.3390/app13010151.
  • [15] B. Liu, X. Wei, C. Sun, B. Wang, and W. Huo, “A controllable neural network-based method for optimal energy management of fuel cell hybrid electric vehicles,” Int. J. Hydrog. Energy, vol. 55, pp. 1371–1382, 2024, doi: 10.1016/j.ijhydene.2023.10.215.
  • [16] X. Lin et al., “Trip distance adaptive equivalent hydrogen consumption minimization strategy for fuel cell electric vehicles integrating driving cycle prediction,” Chin. J. Eng., vol. 46, no. 2, pp. 376–384, 2024, doi: 10.13374/j.issn2095-9389.2022.11.22.005.
  • [17] Z. Chen, R. Liang, F. Qi, and Y. Yan, “Research on Energy Management Method of Fuel Cell Hybrid Power System Based on Improved Equivalent Hydrogen Consumption,” Urban Mass Transit., vol. 27, no. 05, pp. 20–24, 2024, doi: 10.16037/j.1007-869x.2024.05.005.
  • [18] T. Li et al., “Real-time Adaptive Energy Management Strategy for Dual-motor-driven Electric Tractors,” Trans. Chin. Soc. Agric. Mach., vol. 51, pp. 530–543, 2020, doi: 10.6041/j.issn.1000-1298.2020.S2.066.
  • [19] J. Zhang, B. Zhao, X. Yan, M. Liu, L. Xu, and Ch. Shang, “Design and optimization of dual-motor electric tractor drive system based on driving cycles,” Plos One, vol. 18, no. 16, p. e0286378, 2023, doi: 10.1371/journal.pone.0286378.
  • [20] G. Pan, Y. Bai, H. Song, Y. Qu, Y. Wang, and X. Wang., “Hydrogen fuel cell power system – development perspectives for hybrid topologies,” Energies, vol. 16, no. 6, p. 2680, 2023, doi: 10.3390/en16062680.
  • [21] J. Zhang et al., “Design and optimization of dual-motor electric tractor drive system based on driving cycles,” Trans. Chin. Soc. Agric. Mach., vol. 54, pp. 396–406, 2023, doi: 10.6041/j.issn.1000-1298.2023.05.041.
  • [22] W. Zhou, Y. Zheng, Z. Pan, and Q. Lu, “Review on the battery model and SOC estimation method,” Processes, vol. 9, no. 9, p. 1685, 2021, doi: 10.3390/pr9091685.
  • [23] N. Campagna et al., “Battery models for battery powered applications: A comparative study,” Energies, vol. 13, no. 16, p. 4085, 2020, doi: 10.3390/en13164085.
  • [24] C. Wang, H. Wang and Y. Tian, “Improved State Machine Strategy Based on Consumption Minimization for Fuel Cell/ Battery/Ultracapacitor Hybrid Electric Vehicles,” in 2021 33rd Chinese Control and Decision Conference (CCDC), IEEE, 2021, pp. 3778–3783, doi: 10.1109/CCDC52312.2021.9601905.
  • [25] T. Wang et al., “Fuel Cell Hybrid Power Generation System Equivalent Hydrogen Consumption Instantaneous Optimization Energy Management Method,” Proc. CSEE, vol. 38, no. 14, pp. 4173–4182, 2018, doi: 10.13334/j.0258-8013.pcsee.171334.
  • [26] H. Li et al., “A novel equivalent consumption minimization strategy for hybrid electric vehicle powered by fuel cell, battery and supercapacitor,” J. Power Sources, vol. 395, pp. 262–270, 2018, doi: 10.1016/j.jpowsour.2018.05.078.
  • [27] X. Li et al., “Drive power allocation strategy for electric tractor based on adaptive multi resolution analysis,” Trans. Chin. Soc. Agric. Mach., vol. 39, no. 23, pp. 55–66, 2023, doi: 10.11975/j.issn.1002-6819.202306204.
  • [28] T. Drugeot et al., “Experimental assessment of proton exchange membrane fuel cell performance degradations during emulated start-up/shut-down phases,” Int. J. Hydrog. Energy, vol. 48, no. 14, pp. 5630–5642, 2023, doi: 10.1016/j.ijhydene.2022.11.020.
  • [29] S. Cheng et al., “Investigation and analysis of proton exchange membrane fuel cell dynamic response characteristics on hydrogen consumption of fuel cell vehicle,” Int. J. Hydrog. Energy, vol. 47, no. 35, pp. 15845–15864, 2022, doi: 10.1016/j.ijhydene.2022.03.063.
  • [30] B. He and M. Yang, “Optimisation-based energy management of series hybrid vehicles considering transient behaviour,” Int. J. Altern. Propul., vol. 1, no. 1, pp. 79–96, 2006, doi: 10.1504/IJAP.2006.010759.
  • [31] M. Jain, V. Saihjpal, N. Singh, and S.B. Singh, “An overview of variants and advancements of PSO algorithm,” Appl. Sci., vol. 12, no. 17, p. 8392, 2022, doi: 10.3390/app12178392.
  • [32] J. Cai, Q. Li, L. Li, H. Peng, and Y. Yang, “Optimisation-based energy management of series hybrid vehicles considering transient behaviour,” Energy Conv. Manag., vol. 53, no. 1, pp. 175–181, 2012, doi: 10.1016/j.enconman.2011.08.023.
  • [33] N.K. Kulkarni et al., “Particle swarm optimization applications to mechanical engineering – A review,” Mater. Today-Proc., vol. 2, no. 4–5, pp. 2631–2639, 2015, doi: 10.1016/j.matpr.2015.07.223
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-69c65f11-b67f-4ad7-867e-ad12a3eec546
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