Systems Design Engineering

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This is the collection for the University of Waterloo's Department of Systems Design Engineering.

Research outputs are organized by type (eg. Master Thesis, Article, Conference Paper).

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Browsing Systems Design Engineering by Author "Azad, Nasser"

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    Dynamic Modeling and Parameter Identification of a Plug-in Hybrid Electric Vehicle
    (University of Waterloo, 2017-09-19) Buggaveeti, Sindhura; McPhee, John; Azad, Nasser
    In recent times, mechanical systems in an automobile are largely controlled by embedded systems, called micro-controllers. These automobiles, installed with micro-controllers, run complex embedded code to improve the efficiency and performance of the targeted mechanical systems. Developing and testing these control algorithms using the concept of model based design (MBD) is a cost-efficient and time-saving approach. MBD employs vehicle system models throughout the design process and offers superior understanding of the system behaviour than a traditional hardware prototype based testing. Consequently, accurate system identification constitutes an important aspect in MBD. The main focus of this thesis is to develop a validated vehicle dynamics model of a Toyota Prius Plug-in hybrid vehicle. This model plays a crucial role in achieving better fuel economy by assisting in the development process of various controller designs such as energy management system, co-operative adaptive cruise control system, and trip planning module. In this work, initially a longitudinal vehicle dynamics model was developed in MapleSim that utilizes acausal modeling techniques and symbolic code generation to create models that are capable of real-time simulation. Here, the motion in longitudinal direction was given importance as it is the crucial degree of freedom (DOF) for determining the fuel consumption. Besides, the generic and full-fledged vehicle dynamics model in Simulink-based Automotive Simulation Models (ASM) software was also modified to create a validated model of the Prius. This software specifically facilitates the implementation of the model for virtual data collection using a driving simulator. Both vehicle models were verified by studying their simulation results at every stage of the development process. Once the vehicle models were fully functional, the accurate and reliable parameters that control the vehicle motion were estimated. For this purpose, experimental data was acquired from the on-road and rolling dynamometer testing of the Prius. During these tests, the vehicle was instrumented with a vehicle measurement system (VMS), global-positioning system (GPS), and inertial measurement unit (IMU) to collect synchronized vehicle dynamics data. Parameters were identified by choosing a local optimization algorithm that minimizes the difference between simulated and experimental results. Homotopy, a global optimization technique was also investigated to check the influence of optimization algorithms on the suspension parameters. This method of parameter estimation from on-road data is highly flexible and economical. Comparison with the parameters obtained from 4-Post testing, a standardized test method, shows that the proposed methods can estimate parameters with an accuracy of 90%. Moreover, the longitudinal and lateral dynamics exhibited by the developed vehicle models are in accordance with the experimental data from on-road testing. The full vehicle simulations suggest that these validated models can be successfully used to evaluate the performance of controllers in real time.
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    Dynamics and Model-Predictive Anti-Jerk Control of Connected Electric Vehicles
    (University of Waterloo, 2018-02-14) Batra, Mohit; John, McPhee; Azad, Nasser
    Electric Vehicles (EVs) develop high torque at low speeds, resulting in a high rate of acceleration. However, the rapid rise in torque of an electric motor creates undesired torsional oscillations, with vehicle jerk arising as a result of wheel slip or flexibility in the half-shaft. These torsional oscillations in the halfshaft lead to longitudinal oscillations in the wheels, thus reducing comfort and drivability. In this research, we have designed an anti-jerk longitudinal dynamics controller that damps out driveline oscillations and improves the drivability of EVs with central-drivetrain architecture. The anti-jerk longitudinal dynamics controller has been implemented for both traction and cruise control applications. We have used a model predictive control (MPC) approach to design the controller since it allows us to deal with multiple objectives in an optimal sense. The major scope of this research involves modeling, parameter identification, design and validation of the longitudinal dynamics controller. The real-time implementation has been demonstrated using hardware-in-the-loop experiments utilizing fast MPC solvers. The MapleSim software, which utilizes symbolic computation and optimized-code generation techniques to create models that are capable of real-time simulation, has been used to develop the longitudinal dynamics plant model. Road tests have been conducted on our test vehicle, a Toyota Rav4 electric vehicle (Rav4EV), to identify the parameters for the longitudinal dynamics model. Experimental data measured using a vehicle measurement system (VMS), global-positioning system (GPS), and inertial measurement unit (IMU) was used for parameter identification. Optimization algorithms have been used to identify the model parameters. A control-oriented model of the EV, which includes a flexible halfshaft and effect of wheel-slip transients, has been developed with the aim of controlling driveline oscillations. The MPC-based anti-jerk traction controller regulates the motor torque corresponding to the accelerator pedal position, to serve the dual objectives of traction and anti-jerk control. The performance of this controllers has been compared to that of other controllers in the literature. Since most traction controllers are on-off controllers and are only activated when wheel slip exceeds a desired limit, they are not effective in anti-jerk control. The MPC-based anti-jerk controller is able to serve multiple objectives related to anti-jerk as well as traction, and is therefore superior to other controllers. A unified design combining the upper and lower level MPC-based cruise controller has also been formulated to meet the anti-jerk objective during cruise control. The cruise controller has been designed such that it is adaptive to changes in road friction conditions. The efficacy of both traction and cruise controllers has been demonstrated through model-in-the-loop simulation, and the real-time capability has been demonstrated through hardware-in-the-loop experiments.