Generating proper dynamic models for truck mobility and handling

In previous work, a "proper model" was defined as the model of minimal complexity, with physically meaningful parameters, which accurately predicts dynamic system outputs. Proper models can be generated by an energy-based model reduction methodology that removes unnecessary complexity from models (linear or nonlinear) without altering the physical interpretation of the remaining parameters and variables. Energy-based model reduction allows design in a search space of reduced dimension, and in general improves computational efficiency. The current work demonstrates the effectiveness and utility of an energy-based model reduction algorithm for vehicle system modelling applications. Model reduction is performed for two different vehicle modelling applications. The first case study focuses on the vehicle dynamics model of a military heavy-duty tractor semi-trailer. The full model is developed using a classical multibody system approach and accurate reduced models are then generated for a lane change manoeuvre. The second case study develops, validates, and reduces an integrated vehicle system model of a single-unit medium-size commercial truck composed of engine, drivetrain, and vehicle dynamic subsystems. The systematically reduced model accurately predicts vehicle forward speed/acceleration and engine behaviour during full-throttle acceleration and braking with a twofold increase in computation efficiency. The reduced models generated by the energy-based methodology retain predictive quality, are useful for studying trade-offs involved in redesigning components and control strategies for improved vehicle performance, and are less computationally intensive.