Off Road Vehicle

This theme investigates the design, modeling, and control strategies of hybrid electric vehicles (HEVs), especially focusing on Through-the-Road (TTR) architecture, which is characterized by separate propulsion on each axle (one electric, one combustion). This concept is critical for off-road vehicles, which require high efficiency, flexible propulsion, and enhanced mobility features such as four-wheel drive. Understanding TTR hybrid configurations addresses how to combine electric and internal combustion engines to optimize energy use without mechanical coupling, relevant for environmental sustainability and performance in rough terrains.

This research theme focuses on understanding the dynamic stability, kinematic behavior, and control challenges of off-road vehicles navigating rough, uneven, and deformable terrains. It also includes semi-trailer articulated vehicle stability—an area addressing complex multi-body dynamics relevant to heavy off-road vehicle applications. The work encompasses terramechanics models that relate soil-vehicle interactions to vehicle dynamics, enabling design and control frameworks that improve ride comfort, safety, and traction in unstructured environments typical of off-road driving.

This theme investigates integration of diverse sensor modalities (cameras, LiDAR, inertial units, aerial imagery) and machine learning techniques (support vector regression, reinforcement learning) to improve environment perception, terrain classification, and path planning in off-road or unstructured environments. Such methods are critical to enabling autonomous navigation and safe operation in scenarios where road infrastructure may be sparse or unreliable, and where natural obstacles and variable terrain pose severe challenges for traditional sensing and control.