Traditional plowing efficiency control methods are difficult to balance the tillage efficiency and uniform plowing depth, and the impact of the motor temperature rise on the control accuracy cannot be ignored during electric tractor operations. Therefore, a plowing drag-adaptive operation control method considering the motor temperature rise was proposed for an electric tractor equipped with a sliding battery pack. Firstly, a field-oriented control model with temperature compensation for the PMSM was developed based on the obtained winding resistances and flux links at different temperatures. Then, the driving torque and battery displacement were regulated to adapt the drag variation by the fuzzy neural network algorithm, allowing joint control of the speed and slip rate, and the simulation analysis was performed. Finally, a field plowing test was conducted. The results showed that the traction efficiency is increased by 23.33 % compared with those without control, and when the motor temperature rises, it can be compensated for temperature to output the required torque accurately, and the average relative errors in both speed and slip rate are reduced. The proposed method can improve the slip and greatly enhance the plowing operational stability, which provided technical support for the automatic precision operation of electric tractors.

传统的犁耕效率控制方法难以平衡耕作效率和均匀犁深，且电动拖拉机作业时电机温升对控制精度的影响不可忽视。因此，针对配备滑动电池组的电动拖拉机，提出了一种考虑电机温升的犁耕阻力自适应运行控制方法。首先，根据获得的不同温度下的绕组电阻和磁链，建立了具有温度补偿的永磁同步电机磁场定向控制模型。然后，通过模糊神经网络算法调节驱动扭矩和电池位移以适应阻力变化，从而实现速度和滑移率的联合控制，并进行仿真分析。最后进行了田间耕作试验。结果表明，牵引效率较无控制提高了23.33%，且当电机温度升高时，可进行温度补偿，准确输出所需扭矩，速度和滑差率的平均相对误差为减少。该方法可改善打滑现象，大大增强犁耕作业的稳定性，为电动拖拉机的自动精准作业提供技术支撑。