Full-power shift gearbox has been one of the key configurations to improve the operation efficiency of heavy tractors. However, the conventional shift strategy cannot fully meet the requirements of the global optimization under multiple-gear overlap. In heavy tractors, the frequent shifting and high fuel consumption can be caused by the overlap of adjacent gear speeds, seriously restricting the intelligent equipment in modern agriculture. It is often required for the global collaborative optimization of optimal working conditions in real time. In this study, a compound shift strategy was proposed to integrate the dynamic programming (DP) with the global optimization and back propagation neural network (BPNN) for real-time prediction. The shift control was divided into a field mode (throttle, speed, and slip rate) and a road mode (throttle and speed), according to the operational classification. A DP bi-level optimization was constructed to incorporate a penalty function of the shift frequency. Fuel economy was prioritized as the core target. Offline optimization was also performed on the real-vehicle plow load spectrum and road transportation from the suburban cycle driving profiles. A double-hidden-layer BPNN controller for the gear prediction was trained using the DP offline optimization dataset. A series of tests was conducted to validate the optimization via an AMESim-MATLAB/Simulink co-simulation platform. Results demonstrated that the shift strategy fully met the power demands of the working conditions under the DP offline solution. In plowing, the shift frequency and fuel consumption decreased by 50.24% and 5.48%, respectively. In road transportation, the shift frequency and fuel consumption decreased by 13.89% and 15.80%, respectively. Both DP global optimization and BPNN real-time control effectively tracked the vehicle speed to meet the demand of the power after plowing simulation. Furthermore, the BPNN was maintained on the minimum gear at the low speed, indicating the more frequent shifting. The higher gears of the DP were utilized for the significant fuel savings during high-speed segments. While the BPNN instantaneously maintained the higher gears at the low speed, this resulted in slightly higher fuel consumption. Both DP and BPNN were also accurately track the speed under road transportation. The available 24 gears were more fully utilized in the DP with fewer shifts during high-speed sections. Although the driving resistance was relatively low, the engine was placed outside the most economical zone, in order to secure the more efficient points of the DP. The high-gear configuration was optimized in the multiple high-speed segments. The DP achieved significantly lower fuel consumption than the BPNN. Collectively, the gear selection and shift smoothness of the DP were optimized to attain the lower fuel consumption using global information. While the DP real-time strategy shared much higher-speed gear utilization and fuel economy, compared with the BPNN. There were only a few differences in the fuel consumption under working conditions. There was some increase in the shift frequency for the BPNN. The BPNN strategy was verified to meet the real-time requirements with acceptable fuel economy. The global gear optimization was also integrated with the real-time adaptive decision-making. Conventional multi-condition strategies on manual calibration were reduced by the adaptive fuzzy control. The finding can also provide a valuable technical pathway for the intelligent control of the mechanical transmission in modern agriculture.

Cotton is an important economic crop and strategic resource. The 14th Five-Year Plan for agricultural mechanization development emphasizes the need to vigorously promote agricultural mechanization and intelligentization. As a key agricultural equipment for achieving whole process mechanization of cotton harvesting, cotton pickers have attracted increasing attention. In China, the traditional walking transmission system of cotton pickers usually adopts the form of hydraulic infinite variable speed combined with mechanical stepped transmission. However, during operation, these pickers face significant variations in working conditions and fluctuating external loads, resulting in an overall high load. The hydraulic walking transmission system finds it struggles to continuously adjust speed and torque to match the constantly changing actual load, which leads to frequent fluctuations in the engine operating points, mismatch of torque in transmission components, and low efficiency of the transmission system. At the same time, due to the large overall load during the operation of the cotton picker, the engine runs for a long time in the high-speed and high-load area, and it is unable to maintain the high-efficiency area, resulting in the problem of high fuel consumption. To address the high fuel consumption associated with traditional cotton picker engines operating in inefficient areas, as well as the low efficiency of the hydraulic drive transmission system, replacing the original 410 kW engine with a 560KW engine allows it to operate in the high-efficiency area. Simultaneously, employing hybrid technology to replace hydraulic transmission with electric drive can significantly improve the performance and efficiency of the cotton picker, yielding better operational outcomes and economic benefits. In order to solve the problems of low system efficiency and high energy consumption caused by the hydraulic transmission of the main working subsystem of the traditional diesel engine power six-row cotton picker, a series hybrid power system configuration is proposed for the first time. Based on this, a hybrid power system configuration for a six-row cotton picker is proposed for the first time. To accommodate the actual working conditions of cotton pickers, a two-speed gearbox is implemented, which includes a working gear and a transporting gear. The front and rear axles are driven by front and rear drive motors through gearboxes and front and rear axle reducers. This configuration allows for efficient operation and transmission of power, enhancing the overall performance of the cotton picker. A vehicle simulation model for a six-row series hybrid cotton picker has been developed using MATLAB/Simulink, and the effects of three strategies on the performance of the cotton picker are studied: power following strategy, equivalent consumption minimization strategy (ECMS), and torque distribution combined with equivalent consumption minimization strategy (TD-ECMS). Simulation results show that under a combined road transportation and field operation condition, the total simulation duration is 1 319 seconds, with 0 to 688 seconds allocated for road transportation and 689 to 1 319 seconds for field operation. The ECMS strategy significantly minimizes fluctuations in the engine's speed point. In comparison to the power-following strategy, which allows the engine to operate in a stable state only 34.34% of the time, the ECMS strategy enhances stability, increasing the stable operating time to 75.28%. This results engine speed stabilization time increased by 40.94 %. The TD-ECMS strategy achieves optimal control of the comprehensive efficiency of the dual motors, maintaining consistency with power-sharing control when the required power is high, and when the required power is low, the torque is completed by the front drive motor alone, the reduction of working points in the low-efficiency area of the rear-mounted motor leads to a decrease in the overall proportion of operation within the low-efficiency area. The comprehensive efficiency of the dual motors under the TD-ECMS strategy increased from 93.92% to 94.83% compared to power-sharing control. Compared to the power-following strategy, the ECMS strategy has reduced fuel consumption by 4.87%, and the TD-ECMS has reduced fuel consumption by 5.62%, resulting in a significant decrease and an improvement in overall economic performance. This paper presents an efficient energy management strategy for a six-row series hybrid cotton picker, laying the foundation for engineering practice.