This paper has taken a 25-horsepower tractor as the object and developed a distributed drive electric tractor (DDET), which may be applied to farms in paddy and upland fields. First, we designed the structure of the chassis drive system in the DDET, then analyzed the dynamic performances, built an evaluation index of dynamic performance for the DDET, and researched the influencing factors of dynamic performances. Moreover, we built the simulation model and studied the dynamic performance based on Advisor software in different working conditions. Finally, we developed the testing tractor of the DDET for test verification. Results show that the traction of DDET is about 5400 N, and the time from being static to being accelerated to a maximum speed of 28.15 km/h is about 5.5279 s. The theoretical analysis results, simulation results, and testing tractor verification results of the DDET designed in this paper are basically consistent, and the overall dynamic performance of the DDET is good.

Traditional tractors can only achieve steering through mechanical structures such as steering knuckles and steering trapezoids. Among them, the mechanical structure is more complex, and various parts are easily damaged, making the tractor malfunction. The four-wheel independent drive differential steering mode differs from the traditional Ackermann steering mode, which realizes steering by controlling the inner and outer wheel torque, which can accurately steer the working state of high-end agricultural machinery equipment and improve the operating efficiency of agricultural machinery equipment. Aiming at the dynamic control problem in the steering of electric tractor four-wheel independent drive, a layered control strategy based on the sliding mode control of yaw torque at the upper level and the optimal torque distribution level based on the mean load rate of vehicle tires at the lower was proposed. By analyzing the differential steering mechanism of a four-wheel independent drive, a dynamic model of differential steering of the electric tractor is established, and a dynamic controller of a four-wheel independent drive is designed according to the layered control strategy. The upper controller tracks and controls the expected yaw speed on the basis of the sliding mode control to track the driver’s intention, and the lower controller realizes the optimal torque distribution based on the principle of the optimal average load rate of the vehicle tire to ensure the steering stability of the electric tractor. The effect of the controller was simulated and analyzed under typical conditions of double line shift, serpentine, and step. The results showed that the sliding mode controller is better than the PID controller in driver intention tracking. Compared with the average allocation strategy, the average maximum load rate of the vehicle tire under the three working conditions is reduced by 16.9%, 13.8%, and 17.3%, respectively, which proves the effectiveness of the layered control strategy. In the real car test, the sliding mode controller is better than the PID controller in the driver intention tracking. This study has important guiding significance for improving the maneuverability and stability of electric tractors.

To obtain the load spectrum of the traction resistance of the three-point suspension device under tractor-plowing conditions, a load spectrum extrapolation method based on a genetic algorithm optimal threshold selection is proposed. This article first uses a pin force sensor to measure the plowing resistance of the tractor’s three-point suspension device under plowing conditions and preprocesses the collected load signal. Next, a genetic algorithm is introduced to select the threshold based on the Peak Over Threshold (POT) extremum extrapolation model. The Generalized Pareto Distribution (GPD) fits the extreme load distribution that exceeds the threshold range, generating new extreme points that follow the GPD distribution to replace the extreme points in the original data, achieving the extrapolation of the load spectrum. Finally, the loading spectrum that can be achieved on the test bench is obtained based on the miner fatigue theory and accelerated life theory. The results show that the upper threshold of the time-domain load data obtained by the genetic algorithm is 10.975 kN, and the grey correlation degree is 0.7249. The optimal lower threshold is 8.5455 kN, the grey correlation degree is 0.7722, and the fitting effect of the GPD distribution is good. The plowing operation was divided into five stages: plowing tool insertion, acceleration operation, constant speed operation, deceleration operation, and plowing tool extraction. A traction resistance loading spectrum that can be achieved on the test bench was developed. The load spectrum extrapolation method based on the genetic algorithm optimal threshold selection can improve the accuracy of threshold selection and achieve the extrapolation and reconstruction of the load spectrum. After processing the extrapolated load spectrum, it can be transformed into a load spectrum that can be recognized by the test bench.

In order to study the energy distribution of the tandem hybrid tractor and achieve optimal fuel economy under the whole operating condition, an energy distribution strategy based on PMP (Pontryagin minimum principle) is proposed. The performance parameters of the relevant power components are obtained by constructing the test bench of the tandem hybrid tractor. At the same time, the mathematical model of energy distribution is established from the aspects of energy distribution objective function, state variable, and variable constraint. Based on the external characteristic test of the hub motor and the resistance analysis, the transport operation condition of the tractor is selected as the simulation target condition. The ADVISOR2002 and Simulink software are used to jointly simulate the three energy distribution strategies: the thermostat type, the power-following type, and the PMP type. The simulation results show that, compared with the other two, the fuel economy and battery power loss of the PMP-based energy distribution strategy are significantly improved. The fuel consumption per 100 km is decreased by 32.91% and 26.10%, respectively, which verifies the feasibility of the strategy. This study is of great significance for improving the production efficiency and reducing fuel consumption of hybrid tractors.

This study develops a real-time energy-efficient control strategy for distributed-drive electric tractors (DDETs) to minimize electrical energy consumption during traction operations. Taking a four-wheel independently driven DDET as the research object, we conduct dynamic analysis of draft operations and establish dynamic models of individual components in the tractor’s drive and transmission system. A backpropagation (BP) neural network-based operational speed prediction model is constructed to forecast operational speed within a finite prediction horizon. Within the model predictive control (MPC) framework, a real-time energy-efficient control strategy is formulated, employing a dynamic programming algorithm for receding horizon optimization of energy consumption minimization. Through plowing operation simulation with comparative analysis against a conventional equal torque distribution strategy, the results indicate that the proposed real-time energy-efficient control strategy exhibits superior performance across all evaluation metrics, providing valuable technical guidance for future research on energy-efficient control strategies in agricultural electric vehicles.

To improve the dynamic response, linearity, and control accuracy of the YYSCT-250-3 tractor multi-circuit hydraulic output power tester, this study develops a particle swarm optimization (PSO)-tuned fuzzy-proportional–integral–derivative (Fuzzy-PID) control strategy. By modulating the actuator-driven ball valve’s rotation angle (0–90°) in the proportional flow valve, the controller uses both the flow rate error and its rate of change between the setpoint and the flow meter feedback as fuzzy inputs to adjust the PID outputs. A detailed mathematical model of the electro-hydraulic proportional flow system is established, incorporating hydraulic resistance torque on the ball valve spool and friction coefficients to enhance accuracy. Through MATLAB/Simulink (R2022a) simulations, the PSO algorithm optimizes the fuzzy membership functions and PID gains, yielding faster response, reduced overshoot, and minimal steady-state error. The optimized controller achieved relative steady-state flow errors within ±1.0% and absolute flow control errors within ±0.5 L/min, significantly outperforming the traditional PID controller. These results demonstrate that the PSO-optimized Fuzzy-PID approach effectively addresses the flow control challenges of the YYSCT-250-3, enhancing both testing efficiency and precision. This work provides a robust theoretical framework and practical reference for rapid, high-precision flow control in multi-channel hydraulic power testing.