The electric tractor has the advantages of zero-emission, high efficiency, and low noise, which is the direction of future development and transformation of agricultural power machinery. Aim at the problem that the simulation methods commonly used in the development of electric tractor drive system are poorly generalized and cannot meet the simulation needs of complex multi-domain physical systems. This paper proposes a modeling method for an electric tractor drive system, takes the YTO-500 tractor as the research object, designs and calculates the overall scheme and parameters of its drive system, divides the drive system into modules, establishes the energy system, motor system and mechanical parts model based on Modelica, and integrates the simulation model of electric tractor drive system on this basis. The traction performance and transportation working conditions were simulated and tested. With compared and analyzed, in the traction characteristics, the simulation and test results of maximum speed, maximum traction force, and maximum traction power of each gear are consistent; within 400s transportation simulation conditions, the speed range of electric tractor is 13~28km·h-1, which is consistent with the speed range of electric tractor transportation gear. The results show that the simulation and the test results are consistent, which verifies the credibility of the simulation and the correctness of the model built, providing a basis for future research and development of agricultural machinery.

A tractor is a type of agricultural machinery with complex structure and harsh operating conditions. It is evolving toward a large-scale, multifunctional, and intelligent system. Digital prototype technology is an effective approach for experts in multidisciplinary fields to collaborate in the development of new tractor products. Tractor performance prototype design is an important part of realizing digital tractor design. In the modeling process, the performance prototype models designed by experts have a problem with inconsistent expressions, making tractor digital design difficult to implement. This study aims to investigate the unified modeling of a tractor performance prototype. The design process of the tractor performance prototype was analyzed according to the characteristics of new tractor product development. Combined with the ontology modeling method, the construction process of the tractor performance prototype ontology was designed. Based on ontology metamodel theory, a multidisciplinary unified modeling method for a tractor performance prototype is proposed, and an ontology metamodel architecture was constructed. Using a wheeled tractor as an example, a performance prototype ontology was designed. Subsequently, an ontology model was created and verified in Protégé. The results indicate that the model can be used for the digital design of new tractor product development. An ontology model database was established, which realized the sharing and management of ontology model data, and the effectiveness of the method was verified.

For the poor working conditions of tractors, single-motor drive tractors usually choose a motor with large parameters, which leads to the lower efficiency of the motor under low-load conditions. Taking a wheeled tractor of YTO as the research object, a driving scheme of a dual-motor electric tractor was proposed, and the main components of the tractor electric drive system were matched. Based on MATLAB/Simulink, the electric tractor drive system model was established. On the premise of meeting the dynamic needs of the tractor, an optimization model was established with the tractor power consumption as the optimization goal and the power distribution coefficient of dual motors as the decision variable. The optimization model was solved by a genetic algorithm. The driving characteristics of the optimized electric tractor and the original tractor, as well as the instantaneous power consumption and total power consumption of the tractor before and after optimization, are compared and analyzed. The results show that the traction efficiency of the optimized electric tractor is about 2.7% higher than that of the original tractor, and the total power consumption of the electric tractor before and after optimization is reduced by 8.2%. The power performance and economy of the tractor after parameter matching and optimization are significantly improved.