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Research on energy-saving control of agricultural hybrid tractors integrating working condition prediction

Author

Listed:
  • Ganghui Feng
  • Junjiang Zhang
  • Xianghai Yan
  • Chunhong Dong
  • Mengnan Liu
  • Liyou Xu

Abstract

To address the issues of tractors using too much fuel and not being energy efficient, a predictive control strategy based on Pontryagin’s minimum principle integrating working condition prediction is proposed for agricultural hybrid tractors. The Dongfanghong 1804 tractor is being used for research. Firstly, the main parameters of the hybrid drive system are determined and modeled. Secondly, based on the adaptive cubic exponential forecasting method, the working condition information for a period of time in the future is predicted through historical working condition information. Furthermore, combining the predicted working conditions information, the goal is to minimize the total energy consumption cost of the entire machine. Motor power and diesel engine power are control variables. The battery state of charge is a state variable. Subsequently, a predictive control strategy based on Pontryagin’s minimum principle integrating working condition prediction is proposed. Finally, the simulation test is carried out based on the MATLAB simulation platform. Research indicates: under plowing conditions, compared with the power following control strategy, the proposed predictive control strategy can effectively manage the performance of the diesel engine and motor, ensuring they operate at their most efficient level. The total energy consumption costs of the power following control and predictive control strategies are 37.17 China Yuan (CNY) and 33.67 CNY, respectively. The cost of energy used is decreased by 9. 42%, which helps make tractor field plowing more efficient and economical.

Suggested Citation

  • Ganghui Feng & Junjiang Zhang & Xianghai Yan & Chunhong Dong & Mengnan Liu & Liyou Xu, 2024. "Research on energy-saving control of agricultural hybrid tractors integrating working condition prediction," PLOS ONE, Public Library of Science, vol. 19(3), pages 1-26, March.
    • Handle: RePEc:plo:pone00:0299658
    DOI: 10.1371/journal.pone.0299658
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    References listed on IDEAS

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    1. 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.
    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. Zeng, Tao & Zhang, Caizhi & Zhang, Yanyi & Deng, Chenghao & Hao, Dong & Zhu, Zhongwen & Ran, Hongxu & Cao, Dongpu, 2021. "Optimization-oriented adaptive equivalent consumption minimization strategy based on short-term demand power prediction for fuel cell hybrid vehicle," Energy, Elsevier, vol. 227(C).
    4. Zhen Zhu & Yanpeng Yang & Dongqing Wang & Yingfeng Cai & Longhui Lai, 2022. "Energy Saving Performance of Agricultural Tractor Equipped with Mechanic-Electronic-Hydraulic Powertrain System," Agriculture, MDPI, vol. 12(3), pages 1-22, March.
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