농업과학연구 (Korean Journal of Agricultural Science)

  • 제46권3호
  • /
  • Pages.613-627
  • /
  • 2019
  • /
  • 2466-2402(pISSN)
  • /
  • 2466-2410(eISSN)
충남대학교 농업과학연구소 (Institute of Agricultural Science, CNU)
권호 표지
DOI QR코드

DOI QR Code

Work load analysis for determination of the reduction gear ratio for a 78 kW all wheel drive electric tractor design

  • Kim, Wan-Soo (Department of Biosystems Machinery Engineering, Chungnam National University) ;
  • Baek, Seung-Yun (Department of Biosystems Machinery Engineering, Chungnam National University) ;
  • Kim, Taek-Jin (Department of Biosystems Machinery Engineering, Chungnam National University) ;
  • Kim, Yeon-Soo (Department of Biosystems Machinery Engineering, Chungnam National University) ;
  • Park, Seong-Un (Research and Development Institute, Tongyang Moolsan Co., Ltd.) ;
  • Choi, Chang-Hyun (Department of Bio-mechatronics Engineering, Sungkyunkwan University) ;
  • Hong, Soon-Jung (Department of General Education, Korea National College of Agriculture and Fisheries) ;
  • Kim, Yong-Joo (Department of Biosystems Machinery Engineering, Chungnam National University)
  • 투고 : 2019.05.08
  • 심사 : 2019.07.23
  • 발행 : 2019.09.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The purpose of this study was to design a powertrain for a 78 kW AWD (all wheel drive) electric tractor by analyzing the combination of various reduction gear ratios on a commercial motor using data from actual agricultural work and driving conditions. A load measurement system was constructed to collect data using wheel torque meters, proximity sensors, and a data acquisition system. Field experiments for measuring load data were performed for two environmental driving conditions (on asphalt and soil) and four agricultural operations (plow tillage, rotary tillage, loader operation, and baler operation). The attached implements and gear stages were selected through farmer surveys. The range of the reduction ratio was determined by selecting the minimum reduction ratio needed to satisfy the torque condition required for agricultural operations and the maximum reduction gear ratio to satisfy the maximum travel speed. The minimum reduction gear ratio selected was 57 in consideration of the working load condition and the maximum reduction gear ratio selected was 62 considering the maximum running speed. In the range of the reduction gear ratio 57 - 62, the selected motor satisfied all working torque conditions. As a result, the combination of the selected motor and reduction gear ratio was applicable for satisfying the loads required during agricultural operation and driving operation.

키워드

참고문헌

  1. Hong SJ, Kim YJ, Chung SO, Choi CH, Park SB, Noh HS, Jang JH. 2017. Determination of safety factor for agricultural gear reducer using simulation software. Korean Journal of Agricultural Science 45:283-289. [in Korean] https://doi.org/10.7744/KJOAS.20170064
  2. Jang JH, Kim WS, Choi CH, Park SU, Kim YJ. 2018. Analysis of power requirement of the underground crop harvester attached on agricultural tractor during traction operation. Journal of Korea Institute of Information, Electronics, and Communication Technology 11:150-155. [in Korean] https://doi.org/10.17661/JKIIECT.2018.11.2.150
  3. Jang ME, Jeong SK, Han KH, Yeo ST. 2014. A study on optimization of components sizing for 4x4 series hybrid electric propulsion systems. Journal of the Korea Institute of Military Science and Technology 17:159-166. [in Korean] https://doi.org/10.9766/KIMST.2014.17.2.159
  4. Jung KH. 2013. Gear train design of 8-speed automatic transmission for tractor. Journal of the Korean Society for Fluid Power and Construction Equipments 10:30-36. [in Korean]
  5. Kang HM, Jung DB, Kim MJ, Min KD. 2013. Study of energy management strategy considering various working modes of plug-in hybrid electric tractor. Transactions of the Korean Society of Mechanical Engineers B 37:181-186. [in Korean] https://doi.org/10.3795/KSME-B.2013.37.2.181
  6. Kim HK, Choi JW, Yoo SJ, Yi KS. 2011a. Development of power management strategies for a compound hybrid excavator. Journal of Mechanical Science and Technology 35:1537-1542. [in Korean]
  7. Kim JY, Park YI. 2012. Analysis of agricultural working load experiments for reduction gear ratio design of an electric tractor powertrain. Transactions of the Korean Society of Automotive Engineers 20:138-144. [in Korean] https://doi.org/10.7467/KSAE.2012.20.5.138
  8. Kim WS, Kim YJ, Chung SO, Lee DH, Choi CH, Yoon YW. 2016. Development of simulation model for fuel efficiency of agricultural tractor. Korean Journal of Agricultural Science 43:116-126. [in Korean] https://doi.org/10.7744/kjoas.20160014
  9. Kim YJ, Chung SO, Choi CH. 2013. Effects of gear selection of an agricultural tractor on transmission and PTO load during rotary tillage. Soil & Tillage Research 134:90-96. https://doi.org/10.1016/j.still.2013.07.013
  10. Kim YJ, Chung SO, Choi CH, Lee DH. 2011b. Evaluation of tractor PTO severeness during rotary tillage operation. Journal of Biosystems Engineering 36:163-170. [in Korean] https://doi.org/10.5307/JBE.2011.36.3.163
  11. Kim YJ, Chung SO, Park SJ, Choi CH. 2011c. Analysis of the power requirements of agricultural tractors by major field operation. Journal of Biosystems Engineering 36:79-88. [in Korean] https://doi.org/10.5307/JBE.2011.36.2.79
  12. Kim YJ, Lee DH, Chung SO, Park SJ, Choi CH. 2011d. Analysis of power requirement of agricultural tractor during baler operation. Journal of Biosystems Engineering 36:243-251. [in Korean] https://doi.org/10.5307/JBE.2011.36.4.243
  13. Lee DH, Choi CH, Chung SO, Kim YJ, Inoue E, Okayasu T. 2016a. Evaluation of tractor fuel efficiency using dynamometer and baler operation cycle. Journal of the Faculty of Agriculture Kyushu University 61:173-182. https://doi.org/10.5109/1564100
  14. Lee DH, Kim YJ, Choi CH, Chung SO, Inoue E, Okayasu T. 2017. Development of a parallel hybrid systems for agricultural tractors. Journal of the Faculty of Agriculture Kyushu University 62:137-144. https://doi.org/10.5109/1800848
  15. Lee DH, Kim YJ, Chung SO, Choi CH, Lee KH, Shin BS. 2015. Analysis of the PTO load of a 75 kW agricultural tractor during rotary tillage and baler operation in Korean upland fields. Journal of Terramechanics 60:75-83. https://doi.org/10.1016/j.jterra.2015.06.002
  16. Lee PU, Chung SO, Choi CH, Joo JH, Rhee JY, Choi YS, Ha JW, Park YJ, Hong SJ, Kim YJ. 2016b. Load capacity simulation of an agricultural gear reducer by surface heat treatment. Korean Journal of Agricultural Science 43:656-664. [in Korean] https://doi.org/10.7744/kjoas.20160069
  17. MAFRA (Ministry of Agriculture, Food and Rural Affairs). 2006. Development of optimal tillage system based on soil physical properties. p. 32-33. MAFRA, Sejong, Korea. [in Korean]
  18. Park SJ, Kim YJ, Lee DH. 2013. Analysis of load and fuel efficiency of agricultural tractor by gear selection during plow tillage. Journal of Food, Agriculture & Environment 11:631-635.
  19. Park YI, Kim JY, Lee YJ. 2010. Development of the all electric range extended electric tractor. pp. 1901-1906. In proceedings of the Korean Society of Automotive Engineers. [in Korean]
  20. RDA (Rural Development Administration). 2015. A study on the using state of agricultural machinery and mechanized rate. p. 11-12. RDA, Jeonju, Korea. [in Korean]
  21. Shen YH, Gao Y, Xu T. 2016. Multi-axle vehicle dynamics stability control algorithm with all independent drive wheel. International Journal of Automotive Technology 17:795-805. https://doi.org/10.1007/s12239-016-0078-x
  22. Sim KJ, Cho WY, Park TG, Oh SH, Choi GJ, Lee YJ. 2010. Design of the driving motor power for an electric tractor using the duty analysis. pp. 1709-1714. In proceedings of the Korean Society of Automotive Engineers. [in Korean]
  23. Wu X, Zheng D, Wang T, Du J. 2019. Torque optimal allocation strategy of all-wheel drive electric vehicle based on difference of efficiency characteristics between axis motors. Energies 12:1-16. https://doi.org/10.3390/en12010001
  24. Yoo IH, Kim BW. 2013. A study of driving simulation considering the various working modes of electric tractor. Journal of Korea Academia-Industrial Cooperation Society 14:5357-5365. [in Korean] https://doi.org/10.5762/KAIS.2013.14.11.5357

피인용 문헌

  1. Development of Cooperative Controller for Dual-Motor Independent Drive Electric Tractor vol.2020, 2019, https://doi.org/10.1155/2020/4826904
  2. 트랙터 최종구동축용 복합유성기어 방식 감속기의 Micro-geometry를 이용한 전달 오차 및 치면 하중 분포 개선에 관한 연구 vol.17, pp.1, 2019, https://doi.org/10.7839/ksfc.2020.17.1.001
  3. 농작업 부하 데이터를 활용한 80 kW급 전기구동 AWD 트랙터의 시뮬레이션 모델 개발 vol.17, pp.1, 2019, https://doi.org/10.7839/ksfc.2020.17.1.027
  4. Development of Control System for Automated Manual Transmission of 45-kW Agricultural Tractor vol.10, pp.8, 2019, https://doi.org/10.3390/app10082930
  5. Development and Verification of a Simulation Model for 120 kW Class Electric AWD (All-Wheel-Drive) Tractor during Driving Operation vol.13, pp.10, 2019, https://doi.org/10.3390/en13102422
  6. Tillage boundary detection based on RGB imagery classification for an autonomous tractor vol.47, pp.2, 2019, https://doi.org/10.7744/kjoas.20200006
  7. Analysis of the load distribution and contact safety factor of PTO gears of a 71 kW class agricultural tractor vol.47, pp.2, 2019, https://doi.org/10.7744/kjoas.20200023
  8. 원추 지수가 트랙터 작업 부하에 미치는 영향 vol.17, pp.2, 2020, https://doi.org/10.7839/ksfc.2020.17.2.009
  9. 55kW급 농업용 트랙터 정유압 기계식 변속기 설계 vol.17, pp.2, 2019, https://doi.org/10.7839/ksfc.2020.17.2.019
  10. Development of a Prediction Model for Tractor Axle Torque during Tillage Operation vol.10, pp.12, 2019, https://doi.org/10.3390/app10124195
  11. Power Transmission Efficiency Analysis of 42 kW Power Agricultural Tractor According to Tillage Depth during Moldboard Plowing vol.10, pp.9, 2019, https://doi.org/10.3390/agronomy10091263
  12. 토양 조건에 따른 농업용 트랙터의 견인 성능 분석 vol.17, pp.4, 2020, https://doi.org/10.7839/ksfc.2020.17.4.133
  13. Design and Verification of a Modular Reconfigurable Test Platform for Electric Tractors vol.11, pp.4, 2021, https://doi.org/10.3390/app11041881
  14. Estimation of Axle Torque for an Agricultural Tractor Using an Artificial Neural Network vol.21, pp.6, 2021, https://doi.org/10.3390/s21061989