# The Current Development Status of Agricultural Machinery
## 2 Power System of Agricultural Machinery Chassis
### Mechanical Power System
Domestically, studies focus on algorithm-driven reconstruction of power systems, utilizing fuzzy mathematics and multi-objective optimization techniques to enhance the efficiency of gear transmission. However, existing chassis dynamics models largely rely on simplified assumptions--such as ideal terrain conditions and steady-state operating scenarios--which result in insufficient characterization of critical factors including tire-soil interaction, vehicle posture dynamics, and load response under complex field environments (e.g., alternating soft/hard soils, steep gradients, and asymmetric loading). This limitation compromises the robustness of control algorithms. Although certain advancements have notably improved fuel economy and achieved high transmission efficiency with moderate terrain adaptability in tractors, mechanical transmission systems still encounter challenges related to reliability and durability under high-speed, heavy-load, and highly dynamic operating conditions. Compared to hydraulic and electric drive systems, the intelligentization of mechanical systems lags behind, mainly due to the lack of real-time precision in dynamic models across full operational ranges, which hinders the integration of advanced control strategies. Contemporary development of mechanical power systems is marked by three key characteristics: (1) the parallel advancement of transmission structural innovation and lightweight design; (2) the deep integration of data-driven methodologies with classical control theory; (3) dual optimization of power performance and energy efficiency through enhanced fuel economy. While data-driven methods, such as deep learning, offer new  
Figure 4: Corolla MACH4 Tractor. modeling opportunities, their reliance on extensive scenario-specific datasets and limited generalization capabilities constrain their applicability in addressing the extreme diversity and uncertainty inherent in agricultural operations. Future research should prioritize addressing critical bottlenecks in three key areas: the development of adaptive transmission systems for highly complex terrains, the realization of deep mechatronic-hydraulic collaborative control, and the integration of novel hybrid powertrain architectures. To support these advancements, it is essential to develop high-fidelity yet computationally efficient multi-physics dynamics models that incorporate mechanical components, soil properties, and crop interactions. Such models will significantly improve predictive accuracy and generalization capabilities under complex and variable boundary conditions. Furthermore, model verification and control system development must be strengthened through the application of digital twin and hardware-in-the-loop (HIL) technologies. These approaches can effectively shorten development cycles while enhancing system reliability and performance validation. In parallel, research efforts should focus on self-adaptive model updating mechanisms and transfer learning techniques. These methodologies will empower control systems to dynamically adjust to diverse agricultural implements, varying soil conditions, and different operational tasks. Collectively, these technological advances will facilitate the intelligentization and low-carbon transformation of agricultural machinery, aligning with broader goals of sustainable and precision agriculture.