# The Current Development Status of Agricultural Machinery
## 2 Power System of Agricultural Machinery Chassis
### Electric Power System  
Under the overarching goal of achieving the \"Dual Carbon\" targets (carbon peaking and carbon neutrality), traditional diesel-powered agricultural machinery is increasingly challenged by its high-pollution emissions and complex mechanical architectures. Electrified agricultural machinery, characterized by structural simplification through electrification integration, zero-emission operation, and low-noise performance, has emerged as a key pathway for agricultural equipment modernization. Its inherent compatibility with intelligent farming operations further solidifies its position as a transformative solution [46]. Compared to electric vehicles, electric agricultural machinery faces distinct challenges: high-torque output requirements for heavy-duty tasks; seasonal-intensive operational demands aligned with agricultural cycles; adaptability to complex and heterogeneous field environments. As a result, its power system requires deep integration with implements to ensure functional synergy [47]. The operational principles of electric drive systems are depicted in Figure 7.  
[PERSON] et al. [48] conducted a comprehensive evaluation of the agricultural and traction performance of electric all-wheel-drive (AWD) tractors, offering valuable insights for the optimization of distributed drive systems in agricultural machinery. [PERSON] et al. [49] proposed a modeling methodology for electric drive systems using the Modelica platform. They developed individual component models for the energy system, motor system, and mechanical subsystems, which were subsequently integrated into a comprehensive simulation model of the electric tractor drive system. The reliability of the model was validated through experimental traction performance tests. This study quantified the efficiency of energy transmission from electrical to mechanical energy (\(\eta_{\tau}=\eta_{mc}\eta_{m}\eta_{s}\eta_{f}\), \(\eta_{\tau}\) is the traction efficiency of the electric tractor, \(\eta_{mc}\) is the transmission efficiency of the drive system, \(\eta_{s}\) is the slip efficiency of the drive wheel, \(\eta_{f}\) is the rolling efficiency, and \(\eta_{m}\) is the efficiency of the drive motor and controller), providing essential parameters for the design and optimization of electric drive systems. However, the current model does not incorporate vehicle dynamics equations based on either the Newton-Euler formulation or the Lagrangian approach that account for slope-induced potential energy. As a result, it lacks the capability to compute wheel-ground contact forces, estimate posture and slip states, or optimize torque distribution under complex terrain conditions. Future research should focus on advancing terrain-coupled dynamic modeling to enhance adaptive control performance in variable agricultural environments. To address the challenges of low traction efficiency and poor stability in distributed electric drive tractors, [PERSON] et al. [50] proposed a control strategy based on a Barrier Lyapunov Function (BLF)-enabled multi-constrained backstepping algorithm. By constructing component load models and designing constrained Lyapunov functions, this method achieves robust control performance under variable feeding disturbances, thereby significantly enhancing overall system stability. [PERSON] et al. [51] developed a distributed drive electric tractor with a four-wheel independent drive architecture. Through static parameter matching verification, they demonstrated favorable dynamic performance under flat-road transportation conditions. [PERSON] et al. [52] introduced a torque allocation strategy that divides total torque into base torque and compensation torque components. Figure 7: Electric powertrain architecture. This approach integrates particle swarm optimization (PSO) for optimal torque positioning with battery state-of-charge (SOC)-based fuzzy rules to distribute torque between dual motors.