# The Current Development Status of Agricultural Machinery
## 2 Power System of Agricultural Machinery Chassis
### Mechanical Power System
This approach effectively improved transmission efficiency across multiple drive modes. [PERSON] et al. (2019) performed a comparative evaluation of continuously variable transmission (CVT) and fixed-power split (FPS) transmissions by analyzing their impacts on transmission efficiency and fuel consumption. Through systematic measurements of the slip rate (\(S_{I}\)), drawbar power (\(DB_{p}\)), and other dynamic output parameters, they demonstrated that FPS transmissions offer superior energy efficiency at the expense of reduced operational speed. Notably, the observed traction force fluctuations under slope conditions highlighted the critical importance of incorporating chassis dynamics modeling for accurate prediction of transmission system robustness. [PERSON] et al. (2018) investigated the mechanism through which rigid lug wheels (RLW) enhance tractor traction performance by employing a wheel-soil interaction dynamics model. Utilizing [PERSON]'s pressure-sinkage and shear theory, the researchers quantified the soil shear reinforcement effects induced by RLW lugs. By solving Newtonian dynamic equations to determine the slip rate (TR) and power loss, they achieved significant improvements in traction coefficient and power conservation performance. [PERSON] et al. (2018) conducted a comprehensive dynamics analysis that converted implement resistance and rolling resistance into power demand metrics. Their modeling framework incorporated transmission chain speed/torque equations, planetary gear power flow analysis, and chassis traction resistance calculations. This work revealed the essential trade-off between structural simplification of hydro-mechanical transmission (HMT) systems and energy consumption optimization. [PERSON] et al. (2018) designed a wheeled tractor incorporating a novel hydro-mechanical continuously variable transmission (HMCVT) based on an in-depth analysis of fuel economy evaluation metrics, achieving substantial improvements in fuel efficiency. [PERSON] et al. (2018) applied fuzzy mathematics, reliability theory, and multi-objective optimization techniques to optimize the design of single-stage external spur gears, thereby enhancing transmission efficiency. [PERSON] et al. (2018) proposed a control strategy that utilizes multi-parameter adjustments to jointly optimize engine operating points and HMCVT working conditions. The objective function was defined as the ratio of engine fuel consumption rate to transmission efficiency, providing a theoretical basis for improving both tractor power performance and fuel economy. [PERSON] et al. [35] developed a minimum-jerk polynomial-based local speed planning algorithm rooted in [PERSON]'s optimality principle. This approach effectively addresses the issues of poor operational stability and low fuel efficiency caused by frequent acceleration and deceleration events during tractor operation. It ensures smooth speed transitions in continuously variable transmission (CVT) systems while significantly improving fuel economy. [PERSON] et al. [36; 37] introduced two key innovations: (1) a gear-shifting control strategy with stochastic load adaptive correction to mitigate frequent random shifting induced by load fluctuations, and (2) a transmission system matching optimization method based on an improved non-dominated sorting genetic algorithm (NSGA). These approaches enable simultaneous enhancement of vehicle dynamic performance and fuel efficiency. Current research on agricultural machinery power systems primarily centers on traditional internal-combustion-engine-driven architectures. These systems employ mechanical components such as clutches, gear transmission systems, final reducers, and differentials to achieve effective power distribution. Internationally, well-established solutions built upon industrial expertise have redirected research efforts toward optimizing transmission efficiency and integrating intelligent control algorithms.