The Need

Agricultural tractors operate under highly variable conditions that make power demand, fuel consumption, and equipment performance difficult to predict. Differences in soil characteristics, terrain, field layouts, implements, and operator practices can cause substantial variability in energy use, even for the same task. As the industry moves toward hybrid and electrified powertrains, the need for realistic, operation-specific duty cycles has become increasingly important. Current approaches often rely on generic assumptions that do not adequately capture real-world variability, limiting the ability to optimize energy management, evaluate new powertrain technologies, and improve operational efficiency.

The Technology

OSU engineers have developed an AI-enabled framework that generates realistic, operation-specific duty cycles and energy management strategies for agricultural tractors. The platform combines industry-standard machinery performance data with real-world operating information to create customized predictions of power demand, energy use, and vehicle loading across diverse agricultural scenarios. In parallel, the technology incorporates advanced machine learning techniques that support intelligent powertrain control for hybrid and electrified tractors, enabling more informed operational decisions under variable and uncertain field conditions while remaining adaptable to a wide range of applications.

Commercial Applications

• Hybrid and electric agricultural tractor development and optimization
• Intelligent energy management systems for off-road equipment
• Digital twin, simulation, and vehicle performance modeling platforms
• Fleet planning, operational efficiency, and fuel-consumption forecasting tools

Benefits/Advantages

• Generates customized duty cycles
• Supports fuel and energy savings
• Improves development of electrified machinery
• Adapts to changing operating conditions