IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i17p6389-d903933.html
   My bibliography  Save this article

Research on Energy Management Strategy of Fuel Cell Electric Tractor Based on Multi-Algorithm Fusion and Optimization

Author

Listed:
  • Hongtu Yang

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
    Department of Vehicle Engineering, Changzhou Vocational Institute of Mechatronic Technology, Changzhou 213164, China)

  • Yan Sun

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Changgao Xia

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Hongdang Zhang

    (Department of Vehicle Engineering, Changzhou Vocational Institute of Mechatronic Technology, Changzhou 213164, China)

Abstract

To solve the serious pollution problems of traditional fuel tractors and the short continuous operation time of pure electric tractors, a hybrid tractor with fuel cell as the primary power source and battery as the auxiliary power source is proposed. A novel energy management strategy was also designed, which integrates thermostat control strategy, power following strategy, and fuzzy logic control. The energy management strategy utilizes the advantages of different algorithms and realizes the rational distribution of fuel cell and battery output power. The system economy and fuel cell durability are improved by the tabu search algorithm. The simulation results show that the proposed energy management strategy can work well in different SOC states and reduce the fuel cell’s power fluctuations. The tractor is equipped with 960 g of hydrogen, the initial state of charge (SOC) is 90%, and it can operate continuously for 2.65 h.

Suggested Citation

  • Hongtu Yang & Yan Sun & Changgao Xia & Hongdang Zhang, 2022. "Research on Energy Management Strategy of Fuel Cell Electric Tractor Based on Multi-Algorithm Fusion and Optimization," Energies, MDPI, vol. 15(17), pages 1-15, September.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6389-:d:903933
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/17/6389/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/17/6389/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ganesh, Akhil Hannegudda & Xu, Bin, 2022. "A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    2. Zhou, Jianhao & Xue, Yuan & Xu, Da & Li, Chaoxiong & Zhao, Wanzhong, 2022. "Self-learning energy management strategy for hybrid electric vehicle via curiosity-inspired asynchronous deep reinforcement learning," Energy, Elsevier, vol. 242(C).
    3. 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.
    4. Elkhatib Kamal & Lounis Adouane, 2022. "Optimized EMS and a Comparative Study of Hybrid Hydrogen Fuel Cell/Battery Vehicles," Energies, MDPI, vol. 15(3), pages 1-20, January.
    5. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Electric vehicle powertrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 238(PC).
    6. Ma, Shuai & Lin, Meng & Lin, Tzu-En & Lan, Tian & Liao, Xun & Maréchal, François & Van herle, Jan & Yang, Yongping & Dong, Changqing & Wang, Ligang, 2021. "Fuel cell-battery hybrid systems for mobility and off-grid applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    7. Ruan, Shumin & Ma, Yue & Yang, Ningkang & Xiang, Changle & Li, Xunming, 2022. "Real-time energy-saving control for HEVs in car-following scenario with a double explicit MPC approach," Energy, Elsevier, vol. 247(C).
    8. Xu, Nan & Kong, Yan & Yan, Jinyue & Zhang, Yuanjian & Sui, Yan & Ju, Hao & Liu, Heng & Xu, Zhe, 2022. "Global optimization energy management for multi-energy source vehicles based on “Information layer - Physical layer - Energy layer - Dynamic programming” (IPE-DP)," Applied Energy, Elsevier, vol. 312(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. 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.
    2. Hyoung-Jong Ahn & Young-Jun Park & Su-Chul Kim & Chanho Choi, 2023. "Theoretical Calculations and Experimental Studies of Power Loss in Dual-Clutch Transmission of Agricultural Tractors," Agriculture, MDPI, vol. 13(6), pages 1-16, June.
    3. Valerio Martini & Francesco Mocera & Aurelio Somà, 2022. "Numerical Investigation of a Fuel Cell-Powered Agricultural Tractor," Energies, MDPI, vol. 15(23), pages 1-19, November.

    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. 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).
    2. Hou, Zhuoran & Guo, Jianhua & Chu, Liang & Hu, Jincheng & Chen, Zheng & Zhang, Yuanjian, 2023. "Exploration the route of information integration for vehicle design: A knowledge-enhanced energy management strategy," Energy, Elsevier, vol. 282(C).
    3. Mudhafar Al-Saadi & Maher Al-Greer & Michael Short, 2023. "Reinforcement Learning-Based Intelligent Control Strategies for Optimal Power Management in Advanced Power Distribution Systems: A Survey," Energies, MDPI, vol. 16(4), pages 1-38, February.
    4. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    5. Eckert, Jony Javorski & Silva, Fabrício L. & da Silva, Samuel Filgueira & Bueno, André Valente & de Oliveira, Mona Lisa Moura & Silva, Ludmila C.A., 2022. "Optimal design and power management control of hybrid biofuel–electric powertrain," Applied Energy, Elsevier, vol. 325(C).
    6. Matteo Acquarone & Claudio Maino & Daniela Misul & Ezio Spessa & Antonio Mastropietro & Luca Sorrentino & Enrico Busto, 2023. "Influence of the Reward Function on the Selection of Reinforcement Learning Agents for Hybrid Electric Vehicles Real-Time Control," Energies, MDPI, vol. 16(6), pages 1-22, March.
    7. Bizon, Nicu, 2019. "Efficient fuel economy strategies for the Fuel Cell Hybrid Power Systems under variable renewable/load power profile," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    8. Michal Carda & Nela Adamová & Daniel Budáč & Veronika Rečková & Martin Paidar & Karel Bouzek, 2022. "Impact of Preparation Method and Y 2 O 3 Content on the Properties of the YSZ Electrolyte," Energies, MDPI, vol. 15(7), pages 1-17, April.
    9. Daniel Egan & Qilun Zhu & Robert Prucka, 2023. "A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation," Energies, MDPI, vol. 16(8), pages 1-31, April.
    10. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Vehicle drivetrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 257(C).
    11. Gao, Yan & Jiang, Chen & Yu, Dahai & Ahmad, Maiwand, 2023. "A novel electric differential and synchronization control method for 4WD/4WS electric vehicles based on fictitious master," Energy, Elsevier, vol. 274(C).
    12. García, Antonio & Monsalve-Serrano, Javier & Martinez-Boggio, Santiago & Gaillard, Patrick, 2021. "Emissions reduction by using e-components in 48 V mild hybrid trucks under dual-mode dual-fuel combustion," Applied Energy, Elsevier, vol. 299(C).
    13. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Feedback linearization-based MIMO model predictive control with defined pseudo-reference for hydrogen regulation of automotive fuel cells," Applied Energy, Elsevier, vol. 293(C).
    14. Zongjun Yin & Xuegang Ma & Chunying Zhang & Rong Su & Qingqing Wang, 2023. "A Logic Threshold Control Strategy to Improve the Regenerative Braking Energy Recovery of Electric Vehicles," Sustainability, MDPI, vol. 15(24), pages 1-33, December.
    15. Caulfield, Brian & Furszyfer, Dylan & Stefaniec, Agnieszka & Foley, Aoife, 2022. "Measuring the equity impacts of government subsidies for electric vehicles," Energy, Elsevier, vol. 248(C).
    16. Perčić, Maja & Vladimir, Nikola & Fan, Ailong, 2021. "Techno-economic assessment of alternative marine fuels for inland shipping in Croatia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    17. Hao, Zhaojun & Di Maio, Francesco & Zio, Enrico, 2023. "A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    18. Rudravaram Venkatasatish & Dhanamjayulu Chittathuru, 2023. "Coyote Optimization Algorithm-Based Energy Management Strategy for Fuel Cell Hybrid Power Systems," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
    19. Tian, Yang & Zhang, Yahui & Li, Hongmin & Gao, Jinwu & Swen, Austin & Wen, Guilin, 2023. "Optimal sizing and energy management of a novel dual-motor powertrain for electric vehicles," Energy, Elsevier, vol. 275(C).
    20. Kandidayeni, M. & Macias, A. & Boulon, L. & Kelouwani, S., 2020. "Investigating the impact of ageing and thermal management of a fuel cell system on energy management strategies," Applied Energy, Elsevier, vol. 274(C).

    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:jeners:v:15:y:2022:i:17:p:6389-:d:903933. 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.