Modeling, optimization, and motion control for off-road Vehicles

Abstract

The transportation sector, which consumes a significant share of national energy and petroleum, requires innovative solutions to improve efficiency and reduce environmental impact—especially for off-road vehicles such as wheel loaders. This dissertation presents a comprehensive framework addressing the challenges in dynamic modeling, optimization, motion control, and validation for off-road vehicles like wheel loaders. Four primary objectives guide this work. First, a high-fidelity, integrated model is developed to capture the complex interactions among the diesel engine, drivetrain, and hydraulic circuits. Rigorously validated against field data (with fuel consumption predictions within 2.3%), this robust diesel-based model accurately replicates real-world operations under various loads. Second, a novel optimization and control strategy is proposed. The high-level planner leverages reduced-order models and optimization tools (such as CasADi and IPOPT) to generate energy-efficient trajectories that minimize fuel consumption and cycle time, while the low-level Electro-Hydraulic Servo System (EHSS) precisely tracks desired trajectories for bucket lift and tilt under nonlinear dynamics and disturbances. Third, a Hardware-in-the-Loop (HIL) testbed is developed to experimentally validate the model and control architecture, effectively bridging simulation and practical application. Fourth, these methodologies are extended to electrified wheel loaders, addressing critical challenges in powertrain management and vehicle and bucket motion coordination, thereby further reducing energy losses in electrified systems. Simulation results and energy flow analyses reveal that significant energy losses in conventional wheel loaders occur mainly in the engine, torque converter, and hydraulic main pump. For diesel systems, the proposed optimization and control approach improves the operating conditions of each component, thereby reducing losses and improving efficiency. In contrast, the electrification approach eliminates the diesel engine and torque converter, inherently reducing energy losses, while powertrain and vehicle and bucket motion co-optimization further improves hydraulic circuit efficiency. Together, these advances lay the foundation for more sustainable, efficient, and intelligent wheel loader operations.