Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.685053
Title: Agricultural tractor powertrains : fundamental characteristics and opportunities for intelligent control
Author: Sayer, David
Awarding Body: Cranfield University
Current Institution: Cranfield University
Date of Award: 2005
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Abstract:
The use of microprocessor-based control systems on agricultural tractors has eased operator burden by allowing changes to tractor and implement settings to be made with little physical effort. However, maintaining the optimum tractor-implement settings whilst encountering the variable nature of agricultural conditions still requires a high level of operator skill, partly due to the need to adjust individual sub-system controllers. CAN-bus communication between electronically controlled vehicle sub. systems provided a new opportunity to enhance vehicle powertrain operation, by intelligently integrating control of the sub-systems. The aim of the project was to develop ways to improve the operational characteristics of a tractor powertrain, by investigating system behaviour, and identifying opportunities for intelligent control. Market research was undertaken which highlighted power-split continuously variable transmissions as a credible alternative to powershift-type transmissions in specific specialist applications where the additional purchase price could be justified. However, there is little scientific evidence to suggest that there are significant improvements in overall vehicle performance to be gained through the use of a CVT tractor compared to a well operated powershift-type transmission. Improvements to gearshift quality and more intelligent use of the powertrain control features could ensure powershift-type transmissions remain competitive for the foreseeable future. A dynamic mathematical powertrain model was developed for a lOOkW, 16 speed semi-powershift transmission, four wheel drive tractor based on fundamental Newtonian principles. With the addition of implement models, this allowed accurate representation of the tractor-implement system and provided a platform to develop improved vehicle control strategies. Validation of the model with experimental data showed it was an accurate representation of the real system. The steady state and transient field performance of the tractor operating with a mouldboard plough, a power harrow and a laden trailer was determined for a number of tractor-implement configurations across a range of conditions. This provided a David Sayer, 2005 Cranfield University, Silsoe 11 large dataset for this vehicle for use in this, and other investigations. The level of powertrain loading for field experiments was found to be influenced by soil type, implement working width and depth as well as forward speed and engine speed. For the road investigation, the surface quality and terrain were major influencing factors on performance. It was found there was considerable variation in tractor response to the different gearshift types experienced in the semi-powershift transmission: the non-powershift changes being severe, particularly during downshifts; double-swap powershifts were markedly more severe than single-swap shifts. A unique investigation of the tractor driveline torque loss characteristics across the full operating spectrum using the axle dynamometer identified that the torque losses for this transmission are predominantly speed, rather than torque related. A mathematical model was developed to predict driveline torque losses from transmission output speed, flywheel torque and the number of power-transmitting gears in mesh. The axle dynamometer was also used to successfully replicate field loading patterns in real time. Throughout this investigation a number of undesirable powertrain characteristics were identified. Potential improvements to vehicle performance through the development of solutions to these characteristics have been made either through analysis of field data, experiments with the axle dynamometer, or using the developed mathematical model.
Supervisor: Scarlett, A. J. ; Godwin, R. J. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.685053  DOI: Not available
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