The simulation tools that are used to model vehicle systems have not been advancing as quickly as the growth of research and technology surrounding the advancements of vehicle technology itself. A topological vehicle systems modelling package would use Modelica to take advantage of the flexibility and modularity of the language, the inherent multi-domain workspace and analytical accuracy of model equations. This package is defined through the use of SuperBlocks, a generalized model that allows the user to select and parameterize the appropriate sub-system directly within the workspace. This palette of SuperBlocks would be implemented within MapleSim6 to create MapleCar. This provides a customized balance between speed and accuracy after taking advantage of advanced graph-theoretic solutions methods used in MapleSim. MapleCar provides several advantages to a user over conventional tools. The SuperBlocks would ease the required steps to model a full vehicle system by providing clear, simple connections to quickly get a simulation assembled. Next, each SuperBlock is represented by a model that contains a replaceable model, a Modelica function which allows its internal model to be changed through a user-friendly parameter selection. The combination of sub-systems accessible directly through a parameter allows a variety of vehicle systems to be easily assembled, as well as provide a container for future models to be shared and published. A short demonstration of connecting these vehicle SuperBlocks from the MapleCar package is provided using MapleSim6. The generalized vehicle component palette provides a straight-forward, customizable drag-and-drop interface to assist in generating vehicle models for simulation. Conclusions and recommendations are provided at the end.

The simulation tools that are used to model vehicle systems have not been advancing as quickly as the growth of research and technology surrounding the advancements of vehicle technology itself. A topological vehicle systems modelling package would use Modelica to take advantage of the flexibility and modularity of the language, the inherent multi-domain workspace and analytical accuracy of model equations. This package is defined through the use of SuperBlocks, a generalized model that allows the user to select and parameterize the appropriate sub-system directly within the workspace. This palette of SuperBlocks would be implemented within MapleSim6 to create MapleCar. This provides a customized balance between speed and accuracy after taking advantage of advanced graph-theoretic solutions methods used in MapleSim. MapleCar provides several advantages to a user over conventional tools. The SuperBlocks would ease the required steps to model a full vehicle system by providing clear, simple connections to quickly get a simulation assembled. Next, each SuperBlock is represented by a model that contains a replaceable model, a Modelica function which allows its internal model to be changed through a user-friendly parameter selection. The combination of sub-systems accessible directly through a parameter allows a variety of vehicle systems to be easily assembled, as well as provide a container for future models to be shared and published. A short demonstration of connecting these vehicle SuperBlocks from the MapleCar package is provided using MapleSim6. The generalized vehicle component palette provides a straight-forward, customizable drag-and-drop interface to assist in generating vehicle models for simulation. Conclusions and recommendations are provided at the end.

The mean value engine model (MVEM) is a mathematical model derived from basic physical principles such as conservation of mass and energy equations. Although the MVEM is based on some simplified assumptions and time averaged combustion engine parameters, it models the engine with a reasonable approximation and gives a satisfactory amount of information about the physics of the fluid energy passing through an engine system. MVEM can predict an engine’s main external variables such as crankshaft speed and manifold pressure, and important internal variables, such as volumetric and thermal efficiencies. Usually, the differential equations used in MVEM will predict fuel film flow, manifold pressure, and crankshaft speed. Because of its simplicity and short simulation time, the MVEM is widely used for engine control development. A mean value engine based on mathematical and parametric equations has recently been developed in the new MapleSim software. The model consists of three main components: the throttle body, the manifold, and the engine. The new MVEM uses combinations of causal and acausal components along with lookup tables and parametric equations. Adjusting the parameters allows the model to be used for new engines of interest. The model is forward-looking and so benefits from both Maple’s powerful mathematical tool and Modelica’s modern equation-based language. A set of throttle angle and mass flow data is used to find the throttle angle function, and to validate the throttle mass flow rates obtained from the model and the experiment.

The field of robotics is in continuous development. Driving forces for the development are higher demands on robot accuracy and being more cost effective in the development process. To reduce costs, product development is moving towards virtual prototyping to enable early analysis and testing. This process demands realistic models and modelling is therefore of utmost importance. In the process of modelling high fidelity robot dynamics many different physical aspects have to be taken into account. Phenomena studied in this thesis stretch from where to introduce flexibilities, mechanical and dynamical coupling effects, and how to describe friction. By using a bottom up approach the effects are analysed individually to evaluate their contribution both to accuracy and computationalcomplexity. A strategy for how to model a flexible parallel linkage manipulator by introducing some crucial simplifications is presented. The elastic parameters are identified using a frequency domain identification algorithm developed in [Wernholt, 2007] and shows that the presented method works well up to a certain level of fidelity. Friction is modelled using empirically derived static and dynamic models. Evaluation of accuracy is conducted through identification of friction models for a real manipulator and it is seen that to capture all existing phenomena in low velocities  a dynamic model is needed. It is also seen that friction characteristics vary with temperature and a Kalman filter is suggested to adaptively estimate friction parameters. Finally an implementation of a flexible manipulator model using the software MapleSim is presented. The tool severely simplifies the process of modelling manipulators and enables for export to other environment such as simulation, optimization and control.

The mean value engine model (MVEM) is a mathematical model derived from basic physical principles such as conservation of mass and energy equations. Although the MVEM is based on some simplified assumptions and time averaged combustion engine parameters, it models the engine with a reasonable approximation and gives a satisfactory amount of information about the physics of the fluid energy passing through an engine system. MVEM can predict an engine’s main external variables such as crankshaft speed and manifold pressure, and important internal variables, such as volumetric and thermal efficiencies. Usually, the differential equations used in MVEM will predict fuel film flow, manifold pressure, and crankshaft speed. Because of its simplicity and short simulation time, the MVEM is widely used for engine control development. A mean value engine based on mathematical and parametric equations has recently been developed in the new MapleSim software. The model consists of three main components: the throttle body, the manifold, and the engine. The new MVEM uses combinations of causal and acausal components along with lookup tables and parametric equations. Adjusting the parameters allows the model to be used for new engines of interest. The model is forward-looking and so benefits from both Maple’s powerful mathematical tool and Modelica’s modern equation-based language. A set of throttle angle and mass flow data is used to find the throttle angle function, and to validate the throttle mass flow rates obtained from the model and the experiment.

The full model of a double-wishbone suspension has more than 30 differential-algebraic equations which takes a remarkably long time to simulate. By contrast, the look-up table for the same suspension is simulated much faster, but may not be very accurate. Therefore, developing reduced models that approximate complex systems is necessary because model reduction decreases the simulation time in comparison with the original model, enables real time applications, and produces acceptable accuracy. In this research, we focus on model reduction techniques for vehicle systems such as suspensions and how they are approximated by models having lower degrees of freedom. First, some existing model reduction techniques, such as irreducible realization procedures, balanced truncation, and activity-based reduction, are implemented to some vehicle suspensions. Based on the application of these techniques, their disadvantages are revealed. Then, two methods of model reduction for multi-body systems are proposed. The first proposed method is 2-norm power-based model reduction (2NPR) that combines 2-norm of power and genetic algorithms to derive reduced models having lower degrees of freedom and fewer number of components. In the 2NPR, some components such as mass, damper, and spring are removed from the original system. Afterward, the values of the remaining components are adjusted by the genetic algorithms. The most important advantage of the 2NPR is keeping the topology of multi-body systems which is useful for design purposes. The second method uses proper orthogonal decomposition. First, the equations of motion for a multi-body system are converted to explicit second-order differential equations. Second, the projection matrix is obtained from simulation or experimental data by proper orthogonal decomposition. Finally, the equations of motion are transferred to a lower-dimensional state coordinate system. The implementation of the 2NPR to two double-wishbone suspensions and the comparison with other techniques such as balanced truncation and activity-based model reduction also demonstrate the efficiency of the new reduction technique.