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Power allocation strategy for fuel cell distributed drive electric tractor based on adaptive multi-resolution analysis theory

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  • Li, Xian-zhe
  • Zhang, Ming-zhu
  • Yan, Xiang-hai
  • Liu, Meng-nan
  • Xu, Li-you

Abstract

Fuel cell distributed drive electric tractor (FCDET) provided a novel pathway for the development of green agricultural machinery technology. However, FCDET faced problems such as low traction efficiency, short endurance, and high hydrogen consumption. Relevant studies have shown that reasonable and effective power allocation strategy can improve the efficiency and reduce the energy consumption of fuel cell systems. In this paper, a drive power allocation strategy based on adaptive multi-resolution analysis theory (AMRA) was proposed. The strategy could realize effective decoupling between various energy sources, reduce the problems of frequent start-stop and large power fluctuation for fuel cells. We started with an equivalent circuit model of the fuel cell system, a total efficiency solution model, an energy dissipation model, and a drive motor response model. The first-time reconstruction strategy was based on Tunable Q-factor Wavelet Transform (TQWT) to obtain a subsequence that responds to the oscillatory characteristics of the power signal. The second-time reconstruction strategy took the low-frequency subsequence through Variational Mode Decomposition (VMD) into a number of discrete sub-signals with special sparse properties. Meanwhile, the Sparrow Search Algorithm (SSA) was used to obtain the optimal combined values of the modal decomposition layers and the quadratic penalty factor in the VMD on real time. Finally, the second-time decomposed subsequences and sub-signals were reconstructed according to the frequency characteristics, and the reconstructed power signals were redistributed among the individual energy sources. In order to validate the proposed power allocation strategy, we used the ET504-H prototype as a research object. The power information of plowing and transportation operating conditions was collected in Mengjin test base of China YTO Group. The MATLAB/Simulink-PXI joint simulation platform was set up and the driving motor bench test was carried out in the New Energy Key Laboratory of Henan Province. The results show that the AMRA-based FCDET drive power allocation strategy can effectively improve the energy utilization of the fuel cell system and the economy of the whole tractor operating unit. Compared to the power-following strategy under plowing and transportation conditions, the average efficiency of the fuel cell system was improved by 8.3 % and 3.3 %, the equivalent hydrogen consumption was reduced by 35.6 % and 43.89 %, respectively. The average efficiency of the drive motor was improved by 2.06 % and 2.08 %, the total energy consumption was reduced by 3.73 % and 2.6 %, respectively. This study can provide a theoretical foundation and a novel technical approach for the development of FCDET control system.

Suggested Citation

  • Li, Xian-zhe & Zhang, Ming-zhu & Yan, Xiang-hai & Liu, Meng-nan & Xu, Li-you, 2023. "Power allocation strategy for fuel cell distributed drive electric tractor based on adaptive multi-resolution analysis theory," Energy, Elsevier, vol. 284(C).
    • Handle: RePEc:eee:energy:v:284:y:2023:i:c:s0360544223027445
    DOI: 10.1016/j.energy.2023.129350
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    References listed on IDEAS

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    1. 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.
    2. Francesco Mocera & Aurelio Somà, 2020. "Analysis of a Parallel Hybrid Electric Tractor for Agricultural Applications," Energies, MDPI, vol. 13(12), pages 1-16, June.
    3. Min, Dehao & Song, Zhen & Chen, Huicui & Wang, Tianxiang & Zhang, Tong, 2022. "Genetic algorithm optimized neural network based fuel cell hybrid electric vehicle energy management strategy under start-stop condition," Applied Energy, Elsevier, vol. 306(PB).
    4. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Real-time energy management for fuel cell electric vehicle using speed prediction-based model predictive control considering performance degradation," Applied Energy, Elsevier, vol. 304(C).
    5. Zhang, Sheng-li & Wen, Chang-kai & Ren, Wen & Luo, Zhen-hao & Xie, Bin & Zhu, Zhong-xiang & Chen, Zhong-ju, 2023. "A joint control method considering travel speed and slip for reducing energy consumption of rear wheel independent drive electric tractor in ploughing," Energy, Elsevier, vol. 263(PD).
    6. Isaac Holmes-Gentle & Saurabh Tembhurne & Clemens Suter & Sophia Haussener, 2023. "Kilowatt-scale solar hydrogen production system using a concentrated integrated photoelectrochemical device," Nature Energy, Nature, vol. 8(6), pages 586-596, June.
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    Cited by:

    1. Li, Xianzhe & Liu, Mengnan & Hu, Chenming & Yan, Xianghai & Zhao, Sixia & Zhang, Mingzhu & Xu, Liyou, 2024. "Parameters collaborative optimization design and innovation verification approach for fuel cell distributed drive electric tractor," Energy, Elsevier, vol. 292(C).

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