Traction performance evaluation of a 78-kW-class agricultural tractor using cone index map in a Korean paddy field

Lee

et al. 2021

Sensors

Self Cite

The objective of this study was to develop a model to estimate the axle torque (AT) of a tractor using an artificial neural network (ANN) based on a relatively low-cost sensor. ANN has proven to be useful in the case of nonlinear analysis, and it can be applied to consider nonlinear variables such as soil characteristics, unlike studies that only consider tractor major parameters, thus model performance and its implementation can be extended to a wider range. In this study, ANN-based models were compared with multiple linear regression (MLR)-based models for performance verification. The main input data were tractor engine parameters, major tractor parameters, and soil physical properties. Data of soil physical properties (i.e., soil moisture content and cone index) and major tractor parameters (i.e., engine torque, engine speed, specific fuel consumption, travel speed, tillage depth, and slip ratio) were collected during a tractor field experiment in four Korean paddy fields. The collected soil physical properties and major tractor parameter data were used to estimate the AT of the tractor by the MLR- and ANN-based models: 250 data points were used for developing and training the model were used, the 50 remaining data points were used to test the model estimation. The AT estimated with the developed MLR- and ANN-based models showed agreement with actual measured AT, with the R2 value ranging from 0.825 to 0.851 and from 0.857 to 0.904, respectively. These results suggest that the developed models are reliable in estimating tractor AT, while the ANN-based model showed better performance than the MLR-based model. This study can provide useful results as a simple method using ANNs based on relatively inexpensive sensors that can replace the existing complex tractor AT measurement method is emphasized.

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Axle Torque for an Agricultural Tractor Using an Artificial Neural Network

Lee

et al. 2021

Sensors

Self Cite

“…The engine rated power of the conventional tractor is 112 kW, and this model similar to the specifications of an electric AWD tractor, and the detailed specifications of the conventional tractor, implements and conditions of field test were shown in Table 3 . The workload data measured in the previous study [ 36 , 37 , 38 ] was used for analyzing and comparing the axle load and agricultural performance. The axle speed and torque of the electric AWD tractor were indicated by applying the reduction ratio to the electric motor.…”

Section: Methodsmentioning

confidence: 99%

Traction Performance Evaluation of the Electric All-Wheel-Drive Tractor

Baek

Jeon

et al. 2022

Sensors

Self Cite

This study aims to design, develop, and evaluate the traction performance of an electric all-wheel-drive (AWD) tractor based on the power transmission and electric systems. The power transmission system includes the electric motor, helical gear reducer, planetary gear reducer, and tires. The electric system consists of a battery pack and charging system. An engine-generator and charger are installed to supply electric energy in emergency situations. The load measurement system consists of analog (current) and digital (battery voltage and rotational speed of the electric motor) components using a controller area network (CAN) bus. A traction test of the electric AWD tractor was performed towing a test vehicle. The output torques of the tractor motors during the traction test were calculated using the current and torque curves provided by the motor manufacturer. The agricultural work performance is verified by comparing the torque and rpm (T–N) curve of the motor with the reduction ratio applied. The traction is calculated using torque and specifications of the wheel, and traction performance is evaluated using tractive efficiency (TE) and dynamic ratio (DR). The results suggest a direction for the improvement of the electric drive system in agricultural research by comparison with the conventional tractor through the analysis of the agricultural performance and traction performance of the electric AWD tractor.

Am. J. Agric. Sci. Eng. Technol.

“…Considering the overall transmission ratio 'q' which is the ratio of engine speed to wheel speed (Ne/Nw) or wheel torque to engine torque (Tw/Te) (Macmillan, 2003) The axle power 'Pax' can be calculated by the (equation 3) (Macmillan, 2003) Drawbar power Drawbar power is obtained using the relation between pull (net traction capacity) and actual travel speed as (Equation 4) (Kim et al, 2020)…”

Section: Field Experimentsmentioning

confidence: 99%

Evaluation of Tractor Field Performance Using Visual Basics Programming for Agricultural Farm Lands

Seifu

Tola

Wako

2023

The purpose of this research was to developed a program in visual basic software for predicting tractor implement performance of tractor implement combination. A conventional tillage system with a mounted mouldboard plough and three bottoms was used to collect data from a Chery tractor (Model RM750) with a four-wheel drive. The soil texture class were determined in laboratory. Soil cone index value was measured using a SpotOn digital compaction meter. The output of the visual basics simulation in this study was obtained by varying the depth of operation and soil cone index for an experimental farm field. From the simulation output the results this study covers of drawbar power, implement draft, pull, tractive force, fuel consumption, slip, power delivery efficiency, dynamic weight and wheel dynamic reactions were drawn by varying depth and soil cone index value.