Renewable Energy Global Innovations features: Computer simulations to maximise fuel efficiency and work performance of agricultural tractors in rotovating and ploughing operations

Significance Statement

Agricultural tractor is the one of the largest fuel consumers among agricultural machines. The tractor population is growing continuously and the tractor power also appears to increase in recent years. This trend is expected to continue and so is tractors’ fuel consumption in Korea. In addition, tractors should meet emission standards both in domestic and overseas markets. In response to such circumstances, tractor manufacturers are required to develop technologies to increase fuel efficiency of tractors and at the same time to reduce their emissions without any power loss.

In order to solve this problems, automatic control system for maximizing the fuel efficiency system will be necessary in the near future. There are many techniques for maximizing the fuel efficiency of agricultural tractor using engine, transmission and implements control separately. But few studies were conducted for investigate the interaction between separated control systems. For integrating the control system, effects of each control variables on fuel efficiency were analysed.

This study was conducted to investigate the effects of five control variables of a tractor: ballast, tyre inflation pressure, transmission gear, engine speed, and work load on fuel efficiency parameters. Tractor simulation model was developed and was validated using the field experiments results. Using the Using the tractor model, 162 simulations were performed under the various combinations of the control variables on the basis of a full factorial design. The simulation results were used to develop linear regression models from which strategies can be established to maximise fuel efficiency. The best strategy reduced FC, FCA, and SVFC by 81.3, 61.1, and 52% under ploughing, and by 58.9, 75.7 and 28.6% under rotovating operations, respectively, when compared with those for the worst strategy.

      

Figure Legend: Schematic of Tractor simulation model

About The Author

Jin Woong Lee is currently a senior researcher of LSMtron Co. tractor manufacturing company at Gyeonggi-do, Korea since 2014. He is interested in the design of control algorithm and control system architecture for agricultural tractor transmission and hydraulic system. He received a B.S and MS., and a Ph. D. degrees in Biosystems Engineering from Seoul National University, Korea.

About The Author

Jae Seung Kim studied automatic gear-shift algorithm for fuel efficiency of agricultural tractors in Off-road equipment design lab. He received a B.S. and MS degrees in Biosystems Engineering from Seoul National University, Korea. Now he works in Shinho systems Co., Ltd. since 2015 and is interested in the simulation of drivetrain and vehicle dynamics.

About The Author

Kyeong Uk Kim Has been a professor of Biosystems and Biomaterial Science and Engineering at Seoul National University specializing in farm power and machinery, soil-machine systems and life test of machine components. He has co-authored several books including principle of agricultural machines. He holds a BSc and MSc in Agricultural Engineering from Seoul National University and a PhD in Agricultural Engineering from University of Illinois at Urbana-Champaign USA (1981)

Reference

Biosystems Engineering, Volume 142, 2016, Pages 1–11.

Jin W. Lee, Jae S. Kim, Kyeong U. Kim

Department of Biosystems & Biomaterials Science and Engineering, Seoul National University, Seoul 151-921, South Korea.

This study was conducted to investigate the effects of five control variables of a tractor: ballast, tyre inflation pressure, transmission gear, engine speed, and work load on three fuel efficiency parameters: fuel consumption per work hour (FC), fuel consumption per tilled area (FCA) and specific volumetric fuel consumption (SVFC). This was done for moldboard ploughing and rotovating operations by computer simulation. A tractor model was constructed with four sub-models: engine and power train, fuel consumption, tractive performance, and draught and power requirement. The simulated fuel efficiency values were in a range of 3.3–6.5% error in average when compared with those obtained from field experiments carried out in a paddy field under the same operational conditions. Based on these results, the tractor model was considered acceptable for simulations to find a general relationship between the fuel efficiency parameters and the control variables.

Using the tractor model, 162 simulations were performed under the various combinations of the control variables on the basis of a full factorial design. The simulation results were used to develop linear regression models from which strategies can be established to maximise fuel efficiency. The best strategy reduced FC, FCA, and SVFC by 81.3, 61.1, and 52% under ploughing, and by 58.9, 75.7 and 28.6% under rotovating operations, respectively, when compared with those for the worst strategy.

Go To Biosystems Engineering  Read more research excellence studies on: Renewable Energy Global Innovations (http://ift.tt/21cCPA4)