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Highlights
A prediction model was developed for estimating the axle torque of an agricultural tractor.
The model was developed by complementing and modifying a previously proposed traction equation.
Compared to the actual axle torque, the proposed model attained MAPE of 2.1%, RMSE of 29 Nm, and RD of 2.7%.
The model predicted axle torque more accur...
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Context 1
... Therefore, this study focuses on the range-shift axle torque, which is the point where the front and rear wheel torques are first divided in the transmission. The torques of the front and rear axles were converted into the range-shift axle torque by applying the gear ratios of the front and rear wheels to the range shift based on equation 24 ( fig. 5). To calculate the combined range-shift torque, the gear ratio and efficiency of the front and rear axles from the range shift to the wheels were ...
Citations
... The power of the tractor is concentrated on the hydraulic power, for example, hydraulic transmission, hydraulic clutch, hydraulic brake, hydraulic steering, hydraulic operated loader, power take-off (PTO), and the proportional valve 7,8) , because of having high accuracy, smoothness, and comfortable driving 9,10) . Especially, a self-propelled harvester is operated by hydraulic power. ...
The power delivery is crucial to designing agricultural machinery. Therefore, the tractor-mounted potato harvester was used in this study to conduct the field experiment and analyze the power delivery for each step. This study was focused on an analysis of power delivery from the engine to the hydraulic components for the tractor-mounted harvester during potato harvesting. Finally, the simulation model of a self-propelled potato harvester was developed and validated using the experimental dataset of the tractor-mounted potato harvester. The power delivery analysis showed that approximately 90.22% of the engine power was used as traction power to drive the tractor-mounted harvester, and only 5.10% of the engine power was used for the entire hydraulic system of the tractor and operated the harvester. The statistical analysis of the simulation and experimental results showed that the coefficient of determinations (R 2) ranged from 0.80 to 0.96, which indicates that the simulation model was performed with an accuracy of over 80%. The regression models were correlated linearly with the simulation and experimental results. Therefore, we believe that this study could contribute to the design methodology and performance test procedure of agricultural machinery. This basic study would be helpful in the design of a self-propelled potato harvester. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License(http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Nomenclature P e : Engine power (kW) T e : Engine torque (Nm) N e : Engine rotational speed (rpm) P hi : Hydraulic power (kW) of i th components P ri : Hydraulic pressure (bar) of i th components Q i : Flow rate (lpm) of i th components.
... Modeling the tire-soil interaction is widely discussed in [168] based on different traction prediction methods, which are proposed in [232] and [31]. While these models provide accurate estimations of traction for vehicle control purposes, prediction models based on tractor specifications are typically adequate for the traction system design as discussed in [127]. The maximum axle torque of the tractor, T max , is calculated in [129] based on the weight of the tractor on front and rear tires, traction coefficient, and rolling radius, as ...
... The reason for this phenomenon is that primary tillage reduces the internal friction of the soil [35]. In another study by Kim et al. [36], it was reported that with increasing value of the slip ratio range, the axle torque also increased. Table 4 shows that the slip ratio increased more during secondary tillage than during primary tillage, overall. ...
The objective of this study was to analyze the effect of tillage type (i.e., primary and secondary tillage) and gear selection (P1L2 to P1L4) on the working load of tractor–implement systems during rotary tillage. Soil properties change with depth, and differences in properties along the depth distribution, such as the location of formation of the hardpan layer, internal friction angle, and moisture content, affect the load of rotary tillage operations. Therefore, the physical properties of soil along the field depth distribution were measured to analyze the effect of tillage type and gear selection on workload in rotary tillage. In addition, a load measurement system equipped with PTO torque meter, axle torque meter, proximity sensor, and RTK-GPS were configured on the 42 kW agricultural tractor. The experimental results show that the combination of tillage type and gear selection has a wide-ranging effect on the tractor’s workload and performance when the rotavator operated at the same tilling depth. Overall working load was higher by up to 14% (engine) and 29.1% (PTO shaft) in primary tillage compared to secondary tillage when the gear selection was the same. When the tillage type is the same, it was analyzed that the overall average torque increased by up to 35.9% (engine) and 33.9% (PTO shaft) in P1L4 compared to P1L2 according to gear selection. Based on load analysis results, it was found that the effect of gear selection (Engine: 4–14%, PTO: 12.1–28.6%) on engine and PTO loads was higher than that of tillage type (Engine: 31.6–35.1%, PTO: 31.9–32.8%), and the power requirement tended to decrease in secondary tillage. Therefore, working load should be considered according to the soil environment and tillage type when designing agricultural machinery system.
... Unmanned driving technology for tractors has become an urgent research topic. Since the articulated steering tractor has special kinematics and dynamics characteristics, it is essential to establish an accurate simulation model for dynamic analysis [5][6][7][8] . ...
... The rollover process of the self-propelled radish harvester was divided into lateral overturning (tilting to the left or right) and longitudinal overturning (tilting forward or backward). In general, such rollovers are affected by irregular soil conditions caused by obstacles such as stones, pebbles, and steep slopes [11,33]. A rollover angle test should consider the irregular soil surface conditions (i.e., upland field slope) in which a self-propelled radish harvester would operate. ...
This study was focused on the stability analysis of the self-propelled radish harvester. A 3D simulation model was developed using RecurDyn and used to analyze the rollover angle. The rollover angle of the original radish harvester was analyzed and checked to see if it satisfied the standard overturning angle (i.e., 30°). To improve it, three simulated weights (50, 100, and 150 kg) were added to three positions (front, center, and rear). The rollover angle of the radish harvester was slightly less than the criterion angle at a deflection angle of 180°. This issue was solved by attaching an additional weight to the front with a deflection angle of 180 degrees. In particular, when an additional weight of 50 kg was attached to the front or an additional weight of 150 kg was attached to the center or rear, the criterion angle range satisfied all conditions. In conclusion, it is feasible that the self-propelled radish harvester prototype could satisfy the criterion angle with the additional load and could be applied to field agriculture.
... In this study, the tractor was operated by an experienced operator using a conventional method to improve the reliability of the tractor. The plow and rotary tillage operations were conducted at 150~200 mm of the tillage depth, which is commonly used in Korea, especially in paddy fields [18][19][20][21][22]. Additionally, according to the moldboard specifications, the maximum working depth was 200 cm. ...
This study focuses on the development of the reliability test method for the hydraulic pump of a tractor during major agricultural operations (plow, rotary, baler, and wrapping) at various driving and PTO (power takeoff) gear stages. The hydraulic-pressure-measurement system was installed on the tractor. The measured hydraulic pressure and engine rotational speed were converted to the equivalent pressure and engine speed for each agricultural operation using a mathematical formula. Additionally, the overall equivalent pressure and overall engine speed were calculated to determine the acceleration lifetime. The average equivalent pressure and engine speed for plow tillage were calculated at around 5.44 MPa and 1548.37 rpm, respectively, whereas the average equivalent pressure and engine speed for rotary tillage were almost 5.70 MPa and 2074.73 rpm, accordingly. In the case of baler and wrapping operations, the average equivalent pressure and engine speed were approximately 11.22 MPa and 2203.01 rpm, and 11.86 MPa and 913.76 rpm, respectively. The overall hydraulic pressure of the pump and the engine rotational speed were found to be around 10.07 MPa and 1512.93 rpm, respectively. The acceleration factor was calculated using the overall pressure and engine speed accounting for 336. In summary, the developed reliability test method was evaluated by RS-B-0063, which is the existing reliability evaluation standard for agricultural hydraulic gear pumps. The evaluation results proved that the developed reliability test method for the hydraulic pump of a tractor satisfied the standard criteria. Therefore, it could be said that the developed reliability test method could be applicable to the hydraulic pump of the tractor during agricultural field operations.
... Therefore, the torque distribution strategy's innovative design and system validation are crucial. Kim et al. developed a model for predicting the shaft torque of tractors during tillage operations [11][12][13]. Yin et al. proposed a new torque distribution control for four-wheel independent drive vehicles [14]. Mao et al. proposed a brushless DC motor, based on a sliding mode variable structure, to provide a reference for the development of domestic controllers [15]. ...
Aiming at the existing single-motor agricultural tractors, which often have a mismatch between power and working conditions and a poor operation effect under different tillage modes, this paper designs a torque allocation strategy for agricultural electric tractors under different tillage modes. Firstly, the torque is divided into basic and compensating, and a calculation model is established. Then, the Particle Swarm Optimization algorithm is used to find the optimal demand torque position, and fuzzy control rules allocate the motor torque in combination with the battery SOC. Finally, the strategy’s effectiveness in different tillage modes is verified by MATLAB/Simulink simulation and bench test. The test results show that the strategy can distribute the motor torque stably according to the load torque change and pedal opening under three PTO transitions and the plowing and rotary tillage modes. The main and speed control motors respond in about 3 s with good real-time performance. The drive wheel torque can reach 1600 N-m during plowing and rotating operation. The PTO torque can reach 60 N-m during the rotating process. The maximum torque of the output shaft can reach 150 N-m with good plowing performance. During all operations, the SOC of the battery shows a steady linear decrease, and the battery discharge has stability.
... The research reproducibility is low, making it difficult to design load. Therefore, if the effect of the working environments on the tractor performance becomes clear through this study, it will be useful information for studies related to the reliability of agricultural machinery, such as axle load prediction [29] and component life evaluation [30], and the tillage performance of the attached implement can be analyzed at the same time. ...
The purpose of this study was to analyze the tillage depth effect on the tractor-moldboard plow systems in various soil environments and tillage depths using a field load measurement system. A field load measurement system can measure the engine load, draft force, travel speed, wheel axle load, and tillage depth in real-time. In addition, measurement tests of soil properties in the soil layer were preceded to analyze the effect of field environments. The presented results show that moldboard plow at the same tillage depth had a wide range of influences on the tractor’s working load and performance under various environments. As the draft force due to soil–tool interaction occurred in the range of 5.6–17.7 kN depending on the field environment, the overall mean engine torque and rear axle torque were up to 2.14 times and 1.67 times higher in hard and clayey soil, respectively, than in soft soil environments. In addition, the results showed tractive efficiency of 0.56–0.73 and were analyzed to have a lugging ability of 67.8% with a 44% maximum torque rise. The engine power requirement in hardpan was similar within 3.6–9.6%, but the power demand of the rear axle differed by up to 18.4%.
... where Tf and Tr are the front and rear axles torque (Nm), respectively; W, Wf, and Wr are the gross, front, and rear axle weight of the tractor (N), respectively; rf and rr are the front and rear wheel tires radius (m), respectively; µ is the coefficient of traction (0.8) [21], ɷf, and ɷr are the weight distribution ratio of both front and rear axles (%), respectively. ...
... where T f and T r are the front and rear axles torque (Nm), respectively; W, W f , and W r are the gross, front, and rear axle weight of the tractor (N), respectively; r f and r r are the front and rear wheel tires radius (m), respectively; µ is the coefficient of traction (0.8) [21], ...
... and Tr are the front and rear axles torque (Nm), respectively; W, Wf, and Wr are front, and rear axle weight of the tractor (N), respectively; rf and rr are the front wheel tires radius (m), respectively; µ is the coefficient of traction (0.8) [21], ɷf, the weight distribution ratio of both front and rear axles (%), respectively. ...
This study is focused on the estimation of fuel consumption of the power-shift transmission (PST) tractor based on PTO (power take-off) dynamometer test. The simulation model of PST tractor was developed using the configurations and powertrain of the real PST tractor. The PTO dynamometer was installed to measure the engine load and fuel consumption at various engine load levels (40, 50, 60, 70, 80, and 90%), and verify the simulation model. The axle load was also predicted using tractor’s specifications as an input parameter of the simulation model. The simulation and measured results were analyzed and compared statistically. It was observed that the engine load, as well as fuel consumption, were directly proportional to the engine load levels. However, it was statistically proved that there was no significant difference between the simulation and measured engine torque and fuel consumption at each load level. The regression equations show that there was an exponential relationship between the fuel consumption and engine load levels. However, the specific fuel consumptions (SFC) for both simulation and measured were linear relationships and had no significant difference between them at each engine load level. The results were statistically proved that the simulation and measured SFCs were similar trends. The plow tillage operation could be performed at the gear stage of 7.65 km/h with higher working efficiency at low fuel consumption. The drawback of this study is to use a constant axle load instead of dynamic load. This study can provide useful information for both researchers and manufacturers related to the automated transmission of an agricultural tractor, especially PST tractor for digital farming solutions. Finally, it could contribute to the manufacturers developing a new agricultural tractor with higher fuel efficiency.
... Thus, loadsensing under actual agricultural working conditions is required to secure the reliability of the tractor transmission [4]. The axle torque (AT) of a tractor during agricultural operations is directly related to the transmission torque, which makes it possible to estimate the torque acting on all parts of the transmission by using the AT [5]. These torque data can be applied to achieve the optimal design of the transmission and can also be used as important data for carrying out various performance and durability tests, such as transmission endurance test [3]. ...
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.
