Multi-objective optimization design of the transmission system parameters of a dual-motor-driven electric tractor based on improved deep deterministic policy gradient algorithm

Electric tractors driven by dual motors have become a critical research topic in the field of pure electric tractor research. As a key component of the transmission system, the optimization of the internal parameters of the power-coupled transmission gearbox has a crucial impact on the power transmission of the whole machine. This work combines the characteristics of low speed and high torque during tractor operation, and adopts the transmission form of double motor input and double planetary group coupling output to design the transmission structure of the gearbox. Then, this paper proposes a dynamic optimization method of the transmission system based on the Improved Deep Deterministic Policy Gradient (IDDPG) algorithm, which realizes the optimization of the gear ratio of the transmission system by constructing a virtual prototype and hardware-in-the-loop simulation environment. In transport mode, the optimized gear ratios shorten the acceleration time of the tractor from 0-20 km/h by 13.6% and increase the motor efficiency by 10%; in rotary mode, the acceleration performance is improved by 28.5% and the motor efficiency is increased by 5%. The study shows that the proposed method is significantly better than the traditional static design and provides a new technical path for the intelligent optimization of the electric tractor drive train, while promoting the efficient and sustainable development of agricultural machinery.