ZHU Zhen, ZENG Lingxin, LIN Yonggang, et al. Adaptive Energy Management Strategy for Hybrid Tractors Based on Condition Prediction[J]. 2023, 57(12): 201-210.
DOI:
ZHU Zhen, ZENG Lingxin, LIN Yonggang, et al. Adaptive Energy Management Strategy for Hybrid Tractors Based on Condition Prediction[J]. 2023, 57(12): 201-210.DOI: 10.7652/xjtuxb202312020.
Adaptive Energy Management Strategy for Hybrid Tractors Based on Condition Prediction
For the problems such as fixed value of equivalent factor and poor adaptability to working conditions in the traditional equivalent consumption minimization strategy(ECMS)
a predictive adaptive equivalent consumption minimization strategy(PA-ECMS)integrating tractor working condition prediction was proposed. In the study
the ECMS was applied to the power distribution of a high-horsepower tractor equipped with a hybrid hydro-mechanical continuously variable transmission powertrain. Firstly
based on the radial basis function(RBF)neural network
a tractor working condition prediction model was established to predict the working condition for a period of time in the future based on the historical working condition. Then
the equivalent factor was adjusted adaptively based on the battery state of charge(SOC)feedback and the predicted working condition. Finally
the power distribution of the hybrid tractor was optimized under the PA-ECMS framework. The results show that
compared with the ECMS with fixed equivalent factor and the adaptive equivalent consumption minimization strategy(A-ECMS)based on SOC feedback only
the fuel consumption of the tractor under ploughing condition is reduced by 6.30% and 2.55%
respectively
and that the tractor performed better in power maintenance.
LIU Mengnan, LEI Shenghui, ZHAO Jinghui, et al. Review of development process and research status of electric tractors [J]. Transactions of the Chinese Society for Agricultural Machinery, 2022, 53(S1): 348-364.
MALIK A, KOHLI S. Electric tractors: Survey of challenges and opportunities in India [J]. Materials Today: Proceedings, 2020, 28, Part 4: 2318-2324.
SCOLARO E, ALBERTI L, BARATER D. Electric drives for hybrid electric agricultural tractors [C]//2021 IEEE Workshop on Electrical Machines Design, Control and Diagnosis(WEMDCD). Piscataway, NJ, USA: IEEE, 2021: 331-336.
MOCERA F, MARTINI V. Numerical performance investigation of a hybrid eCVT specialized agricultural tractor [J]. Applied Sciences, 2022, 12(5): 2438.
ROSSI C, PONTARA D, FALCOMER C, et al. A hybrid-electric driveline for agricultural tractors based on an e-CVT power-split transmission [J]. Energies, 2021, 14(21): 6912.
DOU Haishi, ZHANG Youtong, AI Qiang, et al. Control strategy for hybrid tractor plow conditions oriented to coupled-split dynamic configuration [J]. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(23): 41-49.
MOCERA F, SOMÀ A. Analysis of a parallel hybrid electric tractor for agricultural applications [J]. Energies, 2020, 13(12): 3055.
JIA Chao, QIAO Wei, QU Liyan. Numerical methods for optimal control of hybrid electric agricultural tractors [C]//2019 IEEE Transportation Electrification Conference and Expo(ITEC). Piscataway, NJ, USA: IEEE, 2019: 1-6.
SHI Dehua, RONG Xiangwei, WANG Shaohua, et al. Fuzzy adaptive equivalent consumption minimization strategy for hybrid electric vehicle based on power ratio [J]. Journal of Xi'an Jiaotong University, 2022, 56(1): 12-21.
SAITEJA P, ASHOK B. Critical review on structural architecture, energy control strategies and development process towards optimal energy management in hybrid vehicles [J]. Renewable and Sustainable Energy Reviews, 2022, 157: 112038.
TEBALDI D, ZANASI R. Modeling control and simulation of a parallel hybrid agricultural tractor [C]//2021 29th Mediterranean Conference on Control and Automation(MED). Piscataway, NJ, USA: IEEE, 2021: 317-323.
XU Liyou, LIU Enze, LIU Mengnan, et al. Energy management strategy of fuel cell and storage battery hybrid electric tractor [J]. Journal of Henan University of Science and Technology(Natural Science), 2019, 40(2): 80-86.
JIA Chao, QIAO Wei, QU Liyan. Modeling and control of hybrid electric vehicles: a case study for agricultural tractors [C]//2018 IEEE Vehicle Power and Propulsion Conference(VPPC). Piscataway, NJ, USA: IEEE, 2018: 1-6.
KONG Yan, XU Nan, LIU Qiao, et al. A data-driven energy management method for parallel PHEVs based on action dependent heuristic dynamic programming(ADHDP)model [J]. Energy, 2023, 265: 126306.
LI Tonghui, XIE Bin, WANG Dongqing, et al. Real-time adaptive energy management strategy for dual-motor-driven electric tractors [J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(S2): 530-543.
LI Yinping, LIU Li, JIN Tianxu, et al. Energy control strategy of electric tractor power supply based on dynamic programming [J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(4): 403-410.
HAN Shaojian, ZHANG Fengqi, REN Yanfei, et al. Predictive energy management strategies in hybrid electric vehicles using hybrid deep learning networks [J]. China Journal of Highway and Transport, 2020, 33(8): 1-9.
WANG Shuhan, HUANG Xingshuai, LÓPEZ J M, et al. Fuzzy adaptive-equivalent consumption minimization strategy for a parallel hybrid electric vehicle [J]. IEEE Access, 2019, 7: 133290-133303.
REZAEI A, BURL J B, ZHOU Bin. Estimation of the ECMS equivalent factor bounds for hybrid electric vehicles [J]. IEEE Transactions on Control Systems Technology, 2018, 26(6): 2198-2205.
TIAN Xiang, CAI Yingfeng, SUN Xiaodong, et al. An adaptive ECMS with driving style recognition for energy optimization of parallel hybrid electric buses [J]. Energy, 2019, 189: 116151.
ZHOU Wei, LIU Hongyuan, XU Biao, et al. Predictive equivalent consumption minimization strategy for power split hybrid electric mining truck [J]. Journal of Mechanical Engineering, 2021, 57(2): 200-209.
MOCERA F. A model-based design approach for a parallel hybrid electric tractor energy management strategy using hardware in the loop technique [J]. Vehicles, 2020, 3(1): 1-19.
TRITSCHLER P J, BACHA S, RULLIÈRE E, et al. Energy management strategies for an embedded fuel cell system on agricultural vehicles [C]//The XIX International Conference on Electrical Machines-ICEM 2010. Piscataway, NJ, USA: IEEE, 2010: 1-6.
LIU Hui, LI Xunming, WANG Weida, et al. Markov velocity predictor and radial basis function neural network-based real-time energy management strategy for plug-in hybrid electric vehicles [J]. Energy, 2018, 152: 427-444.