ABSTRACT

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This work presents the development, validation and use of a SIMULINK integrated vehicle system simulation composed of engine, driveline and vehicle dynamics modules. The engine model links the appropriate number of single-cylinder modules, featuring thermodynamic models of the in-cylinder processes with transient capabilities to ensure high fidelity predictions. A detailed fuel injection control module is also included. The engine is coupled to the driveline, which consists of the torque converter, transmission, differential and prop shaft and drive shafts. An enhanced version of the point mass model is used to account for vehicle dynamics in the longitudinal and heave directions. A vehicle speed controller replaces the operator and allows the feedforward simulation to follow a prescribed vehicle speed schedule. For the particular case reported here, the simulation is configured for the International 4700 series, Class VI, 4x2 delivery truck powered by a V8 turbocharged, intercooled diesel engine. The integrated vehicle simulation is validated against transient data measured on the proving ground. Comparisons of predicted and measured responses of engine and vehicle variables during vehicle acceleration from 0 to 60 mph and from 30 to 50 mph show very good agreement. The simulation is also used to study trade-offs involved in redesigning control strategies for improved performance of the vehicle system.

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A dynamic system model is proper for a particular application if it achieves the accuracy required by the application with minimal complexity. Because model complexity often—but not always—correlates inversely with simulation speed, a proper model is often alternatively defined as one balancing accuracy and speed. Such balancing is crucial for applications requiring both model accuracy and speed, such as system optimization and hardware-in-the-loop simulation. Furthermore, the simplicity of proper models conduces to control system analysis and design, particularly given the ease with which lower-order controllers can be implemented compared to higher-order ones. The literature presents many algorithms for deducing proper models from simpler ones or reducing complex models until they become proper. This paper presents a broad survey of the proper modeling literature. To simplify the presentation, the algorithms are classified into frequency, projection, optimization, and energy based, based on the metrics they use for obtaining proper models. The basic mechanics, properties, advantages, and limitations of the methods are discussed, along with the relationships between different techniques, with the intention of helping the modeler to identify the most suitable proper modeling method for a given application.