Identification and Adaptive Control for High-performance AC Drive Systems.

High-performance AC machinery and drive systems can be found in a variety of applications ranging from motion control to vehicle propulsion. However, machine parameters can vary significantly with electrical frequency, flux levels, and temperature, degrading the performance of the drive system. While adaptive control techniques can be used to estimate machine parameters online, it is sometimes desirable to estimate certain parameters offline. Additionally, parameter identification and control are typically conflicting objectives with identification requiring plant inputs which are rich in harmonics, and control objectives often consisting of regulation to a constant set-point. In this dissertation, we present research which seeks to address these issues for high-performance AC machinery and drive systems. The first part of this dissertation concerns the offline identification of induction machine parameters. Specifically, we have developed a new technique for induction machine parameter identification which can easily be implemented using a voltage-source inverter. The proposed technique is based on fitting steady-state experimental data to the circular stator current locus in the stator flux linkage reference-frame for varying steady-state slip frequencies, and provides accurate estimates of the magnetic parameters, as well as the rotor resistance and core loss conductance. Experimental results for a 43 kW induction machine are provided which demonstrate the utility of the proposed technique by characterizing the machine over a wide range of flux levels, including magnetic saturation. The remainder of this dissertation concerns the development of generalizable design methodologies for Simultaneous Identification and Control (SIC) of overactuated systems via case studies with Permanent Magnet Synchronous Machines (PMSMs). Specifically, we present different approaches to the design of adaptive controllers for PMSMs which exploit overactuation to achieve identification and control objectives simultaneously. The first approach utilizes a disturbance decoupling control law to prevent the excitation input from perturbing the regulated output. The second approach uses a Lyapunov-based adaptive controller to constrain the states to the output error-zeroing manifold on which they are varied to provide excitation for parameter identification. Finally, a receding-horizon control allocation approach is presented which includes a metric for generating persistently exciting reference trajectories.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120862/1/davereed_1.pd

A New Powertrain Architecture: From Electromagnetic-Structural Dynamics to Platooning

Electrification and vehicle-to-vehicle connectivity have become two of the major areas of vehicle development in recent years. Electrified vehicles show significant advantages because of their high performance in fuel economy and low emissions compared to conventional vehicles. Although hybrid electric vehicle (HEV) development has resulted in a variety of powertrain architectures, novel high-performance powertrain solutions with fewer components and low cost remain an important need. In addition, common HEV configurations use small internal combustion engines, which can suffer from high torque fluctuations detrimental for NVH performance and safety. Advanced powertrains that absorb these fluctuations efficiently are needed. This thesis presents a novel HEV powertrain architecture without any planetary gears or clutches. Using physics-based component model, a proof-of-concept powertrain model is implemented and demonstrated ability to remove over 99.5% torque fluctuation and fulfill vehicle driving demands. A comprehensive design and control optimization for the novel powertrain is performed. A single utility function is designed by combining multiple objectives, and is tuned using the Pareto front of the novel powertrain performance to obtain different optimal powertrain designs. Optimal novel powertrain designs show comparable performance with optimal designs of commercially available power-split benchmark powertrains. Torque fluctuations in HEVs may result in electromagnetic-structural (EMS) phenomena within the electric machines of the powertrain. Periodic forces generated by permanent magnets or windings and other disturbances to the EM device can lead to excitation of specific structural resonances due to EMS coupling. Existing EMS models are usually 2D and do not capture the EMS coupling. Thus, a model that accurately and efficiently captures EMS phenomena is required. To capture the EMS phenomena, displacement-dependent EM forces are introduced in the modal space to the structural dynamics of electric machines. Both linear and nonlinear approximations of EM forces are calculated using high-fidelity FEA models, forming a reduced-order model (ROM) with EMS coupling, namely the EMS ROM. The dynamics of the EMS ROM is similar to a damped dynamical system governed by Mathieu's equation, which exhibits parametric excitation. The EMS ROM is used to compute the stability transition threshold for the parametric excitation. Parametric resonance peaks are revealed in the responses from an unstable device with EMS. In addition, a frequency shift of the primary resonance peak caused by (nonlinear) EM force harmonics is detected. Time-domain analyses using the high-fidelity FEA model confirm the EMS phenomena and accuracy of the EMS ROM. Multiple vehicles, each with an advanced powertrain can be used in platoons to enhance fuel economy, road capacity, and safety compared to a single vehicle. Studies that focus on platooning usually do not focus on task-based longitudinal planning and do not capture detailed powertrain operations, which impact the control and energy consumption of the overall platoon. In this thesis, multiple vehicles, each equipped with the novel powertrain, are investigated when they form a platoon and drive on a specified path. The drive schedule and vehicle controllers are optimized to minimize the total energy consumption of the platoon. Energy optimization requires an integrated vehicle-following model and a high-fidelity powertrain model. In addition, component-level, vehicle-level, and platoon-level constraints are applied. Parametric studies are performed for both homogeneous and heterogeneous platoons. Optimization is shown to effectively reduce the maximum headway error by an order of magnitude and enhance energy saving of 17% to 37%.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/166142/1/albertyi_1.pd