IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i12p1986-d981558.html
   My bibliography  Save this article

Fuzzy Adaptive Energy Management Strategy for a Hybrid Agricultural Tractor Equipped with HMCVT

Author

Listed:
  • Zhen Zhu

    (Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China)

  • Lingxin Zeng

    (Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China)

  • Long Chen

    (Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China)

  • Rong Zou

    (School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Yingfeng Cai

    (Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China)

Abstract

In order to solve the problem of high fuel consumption and poor emission performance in high horsepower tractors, a parallel hybrid tractor system was designed using a dual power source of an engine and motor matched with a hydro-mechanical continuously variable transmission (HMCVT). An equivalent fuel consumption minimization strategy (ECMS) was used for power distribution of this hybrid system. To address the problem of poor adaptability of the equivalence factor to different working cycles in the conventional ECMS, a fuzzy adaptive equivalent fuel consumption minimization strategy (FA-ECMS) was proposed. A fuzzy PI controller based on battery SOC (State of Charge) feedback was designed to adjust the equivalence factor in real time, so as to achieve adaptive control of the equivalence factor. The physical model of the system was built by SimulationX, and the model of the control strategy was built using Matlab/Simulink. Two typical cycles of tractor plowing and road transportation were simulated. Under ECMS, the fuel consumption of the hybrid agricultural tractor was 14.3 L and 1.19 L in one plowing cycle and one transport cycle, respectively, with final battery SOC values of 60.75% and 60.32%, respectively. Under FA-ECMS, the hybrid farm tractor consumed 13.34 L and 1.13 L in one plowing cycle and one transport cycle, respectively, with final battery SOC values of 60.27% and 60.17%, respectively. The results showed that, with the introduction of a fuzzy PI controller to dynamically adjust the equivalence factor, the overall fuel consumption was reduced by 6.71% and 5.04%, respectively, and the battery power maintenance performance was improved. The designed control strategy could achieve a more reasonable power distribution between the engine and motor while maintaining the balance of the battery SOC.

Suggested Citation

  • Zhen Zhu & Lingxin Zeng & Long Chen & Rong Zou & Yingfeng Cai, 2022. "Fuzzy Adaptive Energy Management Strategy for a Hybrid Agricultural Tractor Equipped with HMCVT," Agriculture, MDPI, vol. 12(12), pages 1-21, November.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:12:p:1986-:d:981558
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/12/1986/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/12/12/1986/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Md. Abu Ayub Siddique & Yong-Joo Kim & Seung-Min Baek & Seung-Yun Baek & Tae-Ho Han & Wan-Soo Kim & Yeon-Soo Kim & Ryu-Gap Lim & Yong Choi, 2022. "Development of the Reliability Assessment Process of the Hydraulic Pump for a 78 kW Tractor during Major Agricultural Operations," Agriculture, MDPI, vol. 12(10), pages 1-15, October.
    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. Tian, Xiang & Cai, Yingfeng & Sun, Xiaodong & Zhu, Zhen & Xu, Yiqiang, 2019. "An adaptive ECMS with driving style recognition for energy optimization of parallel hybrid electric buses," Energy, Elsevier, vol. 189(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Francesco Mocera & Aurelio Somà & Salvatore Martelli & Valerio Martini, 2023. "Trends and Future Perspective of Electrification in Agricultural Tractor-Implement Applications," Energies, MDPI, vol. 16(18), pages 1-36, September.
    2. Liming Sun & Mengnan Liu & Zhipeng Wang & Chuqiao Wang & Fuqiang Luo, 2023. "Research on Load Spectrum Reconstruction Method of Exhaust System Mounting Bracket of a Hybrid Tractor Based on MOPSO-Wavelet Decomposition Technique," Agriculture, MDPI, vol. 13(10), pages 1-18, September.
    3. Ugnė Koletė Medževeprytė & Rolandas Makaras & Vaidas Lukoševičius & Sigitas Kilikevičius, 2023. "Application and Efficiency of a Series-Hybrid Drive for Agricultural Use Based on a Modified Version of the World Harmonized Transient Cycle," Energies, MDPI, vol. 16(14), pages 1-16, July.
    4. Rundong Zhou & Lin Wang & Xiaoting Deng & Chao Su & Song Fang & Zhixiong Lu, 2024. "Research on Energy Distribution Strategy of Tandem Hybrid Tractor Based on the Pontryagin Minimum Principle," Agriculture, MDPI, vol. 14(3), pages 1-17, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shi, Dehua & Liu, Sheng & Cai, Yingfeng & Wang, Shaohua & Li, Haoran & Chen, Long, 2021. "Pontryagin’s minimum principle based fuzzy adaptive energy management for hybrid electric vehicle using real-time traffic information," Applied Energy, Elsevier, vol. 286(C).
    2. Yongbing Xiang & Xiaomin Yang, 2021. "An ECMS for Multi-Objective Energy Management Strategy of Parallel Diesel Electric Hybrid Ship Based on Ant Colony Optimization Algorithm," Energies, MDPI, vol. 14(4), pages 1-21, February.
    3. Yi Du & Jiayan Zhou & Zhuofan He & Yandong Sun & Ming Kong, 2022. "A Dual-Harmonic Pole-Changing Motor with Split Permanent Magnet Pole," Energies, MDPI, vol. 15(20), pages 1-14, October.
    4. Davide Clerici & Francesco Mocera & Aurelio Somà, 2021. "Experimental Characterization of Lithium-Ion Cell Strain Using Laser Sensors," Energies, MDPI, vol. 14(19), pages 1-17, October.
    5. Penghui Qiang & Peng Wu & Tao Pan & Huaiquan Zang, 2021. "Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain," Energies, MDPI, vol. 14(23), pages 1-22, November.
    6. Chen, Z. & Liu, Y. & Ye, M. & Zhang, Y. & Chen, Z. & Li, G., 2021. "A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    7. Yang, Chao & Liu, Kaijia & Jiao, Xiaohong & Wang, Weida & Chen, Ruihu & You, Sixiong, 2022. "An adaptive firework algorithm optimization-based intelligent energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 239(PB).
    8. Wei, Changyin & Sun, Xiuxiu & Chen, Yong & Zang, Libin & Bai, Shujie, 2021. "Comparison of architecture and adaptive energy management strategy for plug-in hybrid electric logistics vehicle," Energy, Elsevier, vol. 230(C).
    9. Wang, Yue & Li, Keqiang & Zeng, Xiaohua & Gao, Bolin & Hong, Jichao, 2023. "Investigation of novel intelligent energy management strategies for connected HEB considering global planning of fixed-route information," Energy, Elsevier, vol. 263(PB).
    10. Guo, Xiaokai & Yan, Xianguo & Chen, Zhi & Meng, Zhiyu, 2022. "Research on energy management strategy of heavy-duty fuel cell hybrid vehicles based on dueling-double-deep Q-network," Energy, Elsevier, vol. 260(C).
    11. Francesco Mocera & Aurelio Somà & Salvatore Martelli & Valerio Martini, 2023. "Trends and Future Perspective of Electrification in Agricultural Tractor-Implement Applications," Energies, MDPI, vol. 16(18), pages 1-36, September.
    12. 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).
    13. Zhou, Quan & Du, Changqing & Wu, Dongmei & Huang, Cheng & Yan, Fuwu, 2023. "A tolerant sequential correction predictive energy management strategy of hybrid electric vehicles with adaptive mesh discretization," Energy, Elsevier, vol. 274(C).
    14. Du, Guodong & Zou, Yuan & Zhang, Xudong & Guo, Lingxiong & Guo, Ningyuan, 2022. "Energy management for a hybrid electric vehicle based on prioritized deep reinforcement learning framework," Energy, Elsevier, vol. 241(C).
    15. Francesco Mocera & Valerio Martini & Aurelio Somà, 2022. "Comparative Analysis of Hybrid Electric Architectures for Specialized Agricultural Tractors," Energies, MDPI, vol. 15(5), pages 1-22, March.
    16. Du, Yi & Cui, Naxin & Cui, Wei & Li, Tao & Ren, Fei & Zhang, Chenghui, 2023. "AGRU and convex optimization based energy management for plug-in hybrid electric bus considering battery aging," Energy, Elsevier, vol. 277(C).
    17. Penghui Qiang & Peng Wu & Tao Pan & Huaiquan Zang, 2022. "Real-Time Energy Management Strategy Based on Driving Conditions Using a Feature Fusion Extreme Learning Machine," Energies, MDPI, vol. 15(12), pages 1-22, June.
    18. Liu, Huanlong & Chen, Guanpeng & Li, Dafa & Wang, Jiawei & Zhou, Jianyi, 2021. "Energy active adjustment and bidirectional transfer management strategy of the electro-hydrostatic hydraulic hybrid powertrain for battery bus," Energy, Elsevier, vol. 230(C).
    19. Claudio Rossi & Davide Pontara & Carlo Falcomer & Marco Bertoldi & Riccardo Mandrioli, 2021. "A Hybrid–Electric Driveline for Agricultural Tractors Based on an e-CVT Power-Split Transmission," Energies, MDPI, vol. 14(21), pages 1-23, October.
    20. Salvatore Martelli & Francesco Mocera & Aurelio Somà, 2023. "Carbon Footprint of an Orchard Tractor through a Life-Cycle Assessment Approach," Agriculture, MDPI, vol. 13(6), pages 1-22, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:12:y:2022:i:12:p:1986-:d:981558. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.