Simulation and experimental research of electric tractor drive system based on Modelica

Mao, Yawei; Wu, Yiwei; Yan, Xianghai; Liu, Mengnan; Xu, Longya

doi:10.1371/journal.pone.0276231

Cited by 6 publications

(6 citation statements)

References 9 publications

(6 reference statements)

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“…Mao et al developed models of the energy system, motor system, and mechanical parts using Modelica and suggested a modeling approach for the drive system of an electric tractor. The findings supported the validity of the simulation and the accuracy of the model, laying the groundwork for upcoming agricultural machinery research and development (Mao et al 2022).…”

Section: Literature Reviewsupporting

confidence: 66%

Electric Vehicle Power System in Intelligent Manufacturing Based on Soft Computing Optimization

Zhao,

Guo

2023

Preprint

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Soft computing technology has attracted extensive attention in the fields of computer engineering and automatic control because it can deal with uncertainties, fuzziness and complex practical problems. In order to realize the cooperative optimization of electric vehicle's dynamic performance and economic performance, this paper adopts Genetic Algorithm (GA) in soft computing technology. The advantage of soft computing technology lies in its adaptability to uncertainty, fuzziness and complex practical problems, which makes GA an effective tool to solve complex optimization problems. Firstly, the power system structure and energy management strategy of electric vehicles are investigated and analyzed. Then, the improved non-dominated sorting genetic algorithm II (NSGA-II) is selected to optimize the parameters of electric vehicles because of its simple operation and high optimization accuracy. Then, NSGA-II is used to construct the power and energy configuration of electric vehicles, with power performance and economic performance as the main optimization objectives. Finally, in order to achieve the optimization goal, the relevant variables are selected, and the optimization objective function and constraint conditions are established, and the model is simulated and evaluated. The results show that the acceleration time of the optimized electric vehicle is significantly reduced, the dynamic performance is improved by more than 7%, and the power loss is reduced by 5%. In addition, compared with the current multi-objective optimization model, this model enables electric vehicles to travel longer distances under the same power. These findings provide valuable reference for the performance improvement of electric vehicles in intelligent manufacturing.

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Section: Literature Reviewsupporting

confidence: 66%

Electric Vehicle Power System in Intelligent Manufacturing Based on Soft Computing Optimization

Zhao,

Guo

2023

Preprint

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“…The cross-sectional area to which the pressure of the 3-stage proportional control valve was applied was area 1 regardless of the sleeve position. Equation (2) shows the sleeve force calculated when the sleeve position is negative. When the 1-stage engagement signal current was applied, the 1-stage PCV supplied pressure, and the 3-stage PCV did not.…”

Section: Hcu Modelingmentioning

confidence: 99%

“…Cheng et al [1] developed a fulltractor simulation model, including the control system, using MATLAB and Carsim to improve the performance of the electronic stability program system among the tractor control systems. Mao et al [2] used Modelica to build a drive system model for an electric tractor and predicted its performance. Yiwei et al [3] built a power-shift transmission (PST) model, including mechanical, hydraulic, and control systems, using Adams, AMEsim, and MATLAB to develop a new type of tractor.…”

Section: Introductionmentioning

confidence: 99%

Development of a Real-Time Tractor Model for Gear Shift Performance Verification

Han,

Ahn,

Kwon

et al. 2023

Agriculture

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Verification of the system is essential during the development of a tractor; however, there are cost and time limitations when verification is performed on an actual tractor. To solve this problem, we developed a tractor model for real-time simulation to verify the gear shift performance of the tractor and evaluate the control algorithm. This study examined and modeled a dual-clutch transmission (DCT)-type 105 kW class tractor. The proportional control valve, synchronizer, and clutch were modeled to accurately implement the shift behavior, and the developed individual model was verified based on actual individual product test data. The 45 s driving simulation was conducted to confirm whether real-time simulation of the entire developed tractor model was possible and whether it simulated the behavior of the target tractor well. The driving simulation results confirmed that the driving speed of the tractor model matched the engine speed, transmission gear ratio, and tractor specifications, and the gear shift performance of the tractor model according to the number of gears was confirmed. The simulated model thus satisfies the characteristics of the target tractor and can be used to verify the gear shift performance, indicating that the model can verify the performance of the control algorithm in real time.

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“…The electromechanical actuator (Fig. 4) consists of the following components: a 12 V / 250 W / 3000 rpm permanent magnet DC motor (PMDC Motor) (1), a PCM040U worm gear reducer (2) with a transmission ratio i = 60, a BSH 12 x 04 ball nut (3), an E40S6-50-3-T-24 rotary encoder (4), a 2GT-6 mm toothed belt transmission (5), and a C7 12 x 04 ball screw (6). The operation involves the electric motor transmitting rotational motion to the ball screw, subsequently converted into translational motion by the ball nut, connected to the actuator drive rod (8).…”

Section: Fig 3 -Experimental Standmentioning

confidence: 99%

“…The price of tractors in agricultural operations is influenced by the trend of electrification and automation of agricultural machinery. The electric agricultural tractor has seen an exponential increase in interest among farmers, manufacturers and researchers in the agricultural industry (Mao et al, 2022;Scolaro et al, 2021;Troncon and Alberti, 2020;Mocera et al, 2023;Dhond et al, 2021), with electric vehicle (EV) sales reaching record highs. The presence of electric tractors on modern agricultural farms is also influenced by farmers' desire to use renewable sources to power them.…”

Section: Introductionmentioning

confidence: 99%

Optimizing and Integrating Electromechanical Actuators in Agricultural Excavator Booms for Enhanced Efficiency and Battery Longevity

SAVANIU,

CHIRIȚĂ,

POPOVICI

et al. 2023

INMATEH

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The research optimizes electric consumption in agricultural excavator booms by integrating electromechanical actuators and Power-by-Wire technologies. Utilizing a PID controller reduces electric motor peak current consumption, enhancing battery longevity. The research, carried out on a reduced-scale experimental stand, reveals the potential applications of the excavator arm equipment in agriculture, such as the precise location of irrigation systems, arrangement of terraces for orchards or vineyards, setting up greenhouses, and efficient unloading/loading of bulk materials. These applications signify the versatility and adaptability of electrically powered excavators in addressing diverse agricultural needs, emphasizing the significance of the developed mechatronic system for enhancing efficiency and sustainability in agricultural practices.

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Simulation and experimental research of electric tractor drive system based on Modelica

Cited by 6 publications

References 9 publications

Electric Vehicle Power System in Intelligent Manufacturing Based on Soft Computing Optimization

Electric Vehicle Power System in Intelligent Manufacturing Based on Soft Computing Optimization

Development of a Real-Time Tractor Model for Gear Shift Performance Verification

Optimizing and Integrating Electromechanical Actuators in Agricultural Excavator Booms for Enhanced Efficiency and Battery Longevity

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