Powertrain parameter matching and optimal design of dual-motor driven electric tractor
Keywords:
electric tractor, parameter matching design, parameter optimization design, powertrain, traction performanceAbstract
The rationality of powertrain parameter design has a significant influence on the traction performance and economic performance of electric tractor. At present, researches on powertrain parameter design mainly focus on electric vehicles, and electric agricultural machinery draw much less attention. Therefore, a method of powertrain parameter matching and optimization design for electric tractor was proposed in this paper, which was based on dual-motor coupling drive mode. The particle swarm optimization (PSO) algorithm based on mixed penalty function was used for parameter optimization. Parameter optimization design was programmed using MATLAB. A simulation dynamic model with optimization design variables of electric tractor powertrain was established based on MATLAB/Simulink. Compared with the simulation results before optimization, the objective functions were optimized and the traction performance of electric tractor was improved, which indicated the effectiveness of the proposed method. Keywords: electric tractor, parameter matching design, parameter optimization design, powertrain, traction performance DOI: 10.25165/j.ijabe.20191201.3720 Citation: Chen Y N, Xie B, Du Y F, Mao E R. Powertrain parameter matching and optimal design of dual-motor driven electric tractor. Int J Agric & Biol Eng, 2019; 12(1): 33–41.References
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[3] Gao H S, Zhu S H. Study on design theory and method for driving line of electric tractor. J. Nanjing Agri. Univ., 2009; 32(1): 140–145. (in Chinese)
[4] Xu L Y, Liu M N, Zhou Z L. Design of drive system for series hybrid electric tractor. Trans. of the CSAE, 2014; 30(9): 11–18.
[5] Zhang S, Xiong R, Zhang C N, Sun F C. An optimal structure selection and parameter design approach for a dual-motor-driven system used in an electric bus. Energy, 2016; 96: 437–448.
[6] Mozaffari A, Vajedi M, Chehresaz M, Azard N L. Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines. Engineering Optimization, 2016; 48(3): 361–379.
[7] Wu J, Zhang C H, Cui N X. PSO algorithm-based parameter optimization for HEV powertrain anad its control strategy. Int. J. Automotive Technol., 2008; 9(1): 53–59.
[8] Zhou Y M. Theory of automobile and tractor. Beijing: China Agricultural University Press, 2000. (in Chinese)
[9] Gao Y M, Ehsani M. A torque and speed coupling hybrid drivetrain-architecture, control, and simulation. IEEE Trans. Power Electron, 2006; 21(3): 741–748.
[10] David D W, Lumkes J H. Manufacturing agricultural utility vehicles in sub-Saharan Africa. Agric. Eng. Int.: CIGR Journal. 2015; Special Issue 2015: 18th World Congress of CIGR.pp.148–159.
[11] Xie B, Li H, Song Z H, Mao E R. Powertrain system design of medium-sized hybrid electric tractor. Inf. Technol. J., 2013; 12(23): 7228–7233.
[12] Niu S Q. An analysis to the draw efficiency of wheeled tractor. J. Zhangjiakou Agric. College, 1995; 11(2): 19–21. (in Chinese)
[13] Mollazade K, Ahmadi H, Alimardani R. Optimal design of rotary tiller's rotor and width proportionate to tractor power using energy method. Int. J. Agric.& Biol. Eng., 2009; 2(2): 1–7.
[14] Hu L. Based on finite element technology of rotary cultivator design and working parts simulation research. Master dissertation. Kunming: Kunming Univ. Sci. Technol., 2014. (in Chinese)
[15] Miller T. Brushless permanent-magnet motor drives. Power Eng. J., 1988; 2(1): 55–60.
[16] Chen Y N, Xie B, Mao E R. Electric tractor motor drive control based on FPGA. IFAC-PapersOnLine, 2016; 49(16): 271–276.
[17] Li Q, Shi S B, Liu H L. The study on tractor automatic shift schedule. J. Agri. Mechanization Res., 2012; 5: 221–223. (in Chinese)
[18] He R, Liu X R, He Z M. Research and development of optimal matching of automobile powertrain. J. Jiangsu Univ. Sci. Technol., 1997; 18(1): 37–41. (in Chinese)
[19] Hegazy R A, Molari G, El-Sheikha A M. Prototype of harvesting system for some aromatic and medical plants. Int. J. Agric. Res., 2011; 6(5): 420–428.
[20] Han L J, Liu H, Wang W D, et al. A study on the parameter matching and optimization of a power split HEV. Automotive Engng., 2014; 36(8): 904–910. (in Chinese)
[21] Yue L L. Matching research of vehicle engine and transmission system based on Matlab and VC++ mixed programming. Master dissertation. Wuhan: Wuhan Univ. Technol., 2010. (in Chinese)
[22] Gao H S. Researches on drive system of electric tractor. PhD dissertation. Nanjing: Nanjing Agric. Univ., 2008. (in Chinese)
[23] Rao Z G. The design and research of close planetary transmission of power diffluence. Drive System Techn., 1996; 96(3): 30–33. (in Chinese)
[24] Desai C, Williamson S S. Optimal design of a parallel hybrid electric vehicle using multi-objective genetic algorithms. 2009 IEEE Vehicle Power and Propulsion Conf., Dearborn:USA, 2009; pp.871–876.
[25] Madanipour V, Montazeri-Gh M, Mahmoodi-K M. Multi-objective component sizing of plug-in hybrid electric vehicle for optimal energy management. Clean Techn. Environ. Policy, 2016; 18(4): 1–14.
[26] Liu M N, Zhou Z L, Xu L Y, Zhao J H, Yan X H. Multi-objective optimization and design of tractor trailer systems. Trans. of the CSAE, 2017; 33(8): 62–68. (in Chinese)
[27] Kennedy J, Eberhart R. Particle swarm optimization. IEEE Int. Conf. on Neural Networks, Perth:Australia, 1995; 4(8): 1942–1948.
[28] Hegazy O, Mierlo J V, Lataire P. Design optimization and optimal power control of fuel cell hybrid electric vehicles based on Swarm Intelligence. Int. Review of Electrical Eng., 2011; 6(4): 1727–1738.
[29] Shi Y H, Eberhart R C. Empirical study of particle swarm optimization. Proc. 1999 Congress on Evolutionary Computation, Washington: USA, 1999; 3(1): 505–510.
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Published
2019-02-01
How to Cite
Chen, Y., Xie, B., Du, Y., & Mao, E. (2019). Powertrain parameter matching and optimal design of dual-motor driven electric tractor. International Journal of Agricultural and Biological Engineering, 12(1), 33–41. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/3720
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Power and Machinery Systems
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