Adaptive convex loss mappings for enhanced loss assessment in asynchronous drives

Control topologies in electric drive applications commonly aim at minimizing the dissipated power in the system to guarantee energy-efficient operation. Especially in vehicle electrification, loss minimization is the main objective in the supervisory control loops as this is directly related to the range of the vehicle. Advanced drive systems are characterized by an elevated complexity but require nevertheless a real-time control strategy to be implemented. Appropriate model abstraction, enabling real-time viability with a reliable system representation, is found in convex mapping procedures of the dissipated power in the drive components. These reduced-order models are generally obtained based on model information solely. This paper proposes a methodology to recursively enhance the reliability of the convex loss approximations. An instantaneous power flow estimation is assessed based on a unification of model expectations and sensor data. Using this information, a proper adaptation to the underlying convex loss coefficients is then determined. The methodology is validated in simulation for an electric drive on three different case studies. The algorithm is furthermore applied on actual experimental data of an asynchronous drive for validation purposes. Preliminary results demonstrate that the error on the loss assessment is reduced by 55.7%-89.0%. Adaptive convex loss mappings can, therefore, be consulted in practical control structures to ameliorate the reliability of loss minimization control schemes, while still maintaining a computationally efficient format

Mathematical Approaches to Modeling, Optimally Designing, and Controlling Electric Machine

Optimal performance of the electric machine/drive system is mandatory to improve the energy consumption and reliability. To achieve this goal, mathematical models of the electric machine/drive system are necessary. Hence, this motivated the editors to instigate the Special Issue “Mathematical Approaches to Modeling, Optimally Designing, and Controlling Electric Machine”, aiming to collect novel publications that push the state-of-the art towards optimal performance for the electric machine/drive system. Seventeen papers have been published in this Special Issue. The published papers focus on several aspects of the electric machine/drive system with respect to the mathematical modelling. Novel optimization methods, control approaches, and comparative analysis for electric drive system based on various electric machines were discussed in the published papers

Electric Vehicle Efficient Power and Propulsion Systems

Vehicle electrification has been identified as one of the main technology trends in this second decade of the 21st century. Nearly 10% of global car sales in 2021 were electric, and this figure would be 50% by 2030 to reduce the oil import dependency and transport emissions in line with countries’ climate goals. This book addresses the efficient power and propulsion systems which cover essential topics for research and development on EVs, HEVs and fuel cell electric vehicles (FCEV), including: Energy storage systems (battery, fuel cell, supercapacitors, and their hybrid systems); Power electronics devices and converters; Electric machine drive control, optimization, and design; Energy system advanced management methods Primarily intended for professionals and advanced students who are working on EV/HEV/FCEV power and propulsion systems, this edited book surveys state of the art novel control/optimization techniques for different components, as well as for vehicle as a whole system. New readers may also find valuable information on the structure and methodologies in such an interdisciplinary field. Contributed by experienced authors from different research laboratory around the world, these 11 chapters provide balanced materials from theorical background to methodologies and practical implementation to deal with various issues of this challenging technology. This reprint encourages researchers working in this field to stay actualized on the latest developments on electric vehicle efficient power and propulsion systems, for road and rail, both manned and unmanned vehicles

Optimal control of a flywheel-based automotive kinetic energy recovery system

This thesis addresses the control issues surrounding flywheel-based Kinetic Energy Recovery Systems (KERS) for use in automotive vehicle applications. Particular emphasis is placed on optimal control of a KERS using a Continuously Variable Transmission (CVT) for volume car production, and a wholly simulation-based approach is adopted. Following consideration of the general control issues surrounding KERS operation, a simplified system model is adopted, and the scope for use of optimal control theory is explored. Both Pontryagin’s Maximum Principle, and Dynamic Programming methods are examined, and the need for numerical implementation established. With Dynamic Programming seen as the most likely route to practical implementation for realistic nonlinear models, the thesis explores several new strategies for numerical implementation of Dynamic Programming, capable of being applied to KERS control of varying degrees of complexity. The best form of numerical implementation identified (in terms of accuracy and efficiency) is then used to establish via simulation, the benefits of optimal KERS control in comparison with a more conventional non-optimal strategy, showing clear benefits of using optimal control

Advances in Rotating Electric Machines

It is difficult to imagine a modern society without rotating electric machines. Their use has been increasing not only in the traditional fields of application but also in more contemporary fields, including renewable energy conversion systems, electric aircraft, aerospace, electric vehicles, unmanned propulsion systems, robotics, etc. This has contributed to advances in the materials, design methodologies, modeling tools, and manufacturing processes of current electric machines, which are characterized by high compactness, low weight, high power density, high torque density, and high reliability. On the other hand, the growing use of electric machines and drives in more critical applications has pushed forward the research in the area of condition monitoring and fault tolerance, leading to the development of more reliable diagnostic techniques and more fault-tolerant machines. This book presents and disseminates the most recent advances related to the theory, design, modeling, application, control, and condition monitoring of all types of rotating electric machines

Multi-objective torque control of switched reluctance machine

PhD ThesisThe recent growing interest in Switched Reluctance Drives (SRD) is due to the electrification of many products in industries including electric/hybrid electric vehicles, more-electric aircrafts, white-goods, and healthcare, in which the Switched Reluctance Machine (SRM) has potential prospects in satisfying the respective requirements of these applications. Its main merits are robust structure, suitability for harsh environments, fault-tolerance, low cost, and ability to operate over a wide speed range. Nevertheless, the SRM has limitations such as large torque ripple, high acoustic noise, and low torque density. This research focuses on the torque control of the SRD with the objectives of achieving zero torque error, minimal torque ripple, high reliability and robustness, and lower size, weight, and cost of implementation. Direct Torque Control and Direct Instantaneous Torque Control are the most common methods used to obtain desired torque characteristics including optimal torque density and minimized torque ripple in SRD. However, these torque control methods, compared to conventional hysteresis current control, require the use of power devices with a higher rating of about 150% to achieve the desired superior performance. These requirements add extra cost, conduction loss, and stress on the drive’s semiconductors and machine winding. To overcome these drawbacks, a simple and intuitive torque control method based on a novel adaptive quasi sliding mode control is developed in this study. The proposed torque control approach is designed considering the findings of an investigation performed in this thesis of the existing widely used control techniques for SRD based on information flow complexity. A test rig comprising a magnet assisted SRM driven by an asymmetric converter is constructed to validate the proposed torque control method and to compare its performance with that of direct instantaneous torque control, and current hysteresis control methods. The simulation and experimental results show that the proposed torque control reduces the torque ripple over a wide speed range without demanding a high current and/or a high switching frequency. In addition, It has been shown that the proposed method is superior to current hysteresis control method in the sensorless operation of the machine. Furthermore, the sensorless performance of the proposed method is investigated with the lower component count R-Dump converter. The simulation results have also demonstrated the excellent controller response using the standard R-Dump converter and also with its novel version developed in this thesis that needs only one current sensor

Optimised control of an advanced hybrid powertrain using combined criteria for energy efficiency and driveline vibrations

This thesis discusses a general approach to hybrid powertrain control based on optimisation and optimal control techniques. A typical strategy comprises a high level non-linear control for optimised energy efficiency, and a lower level Linear Quadratic Regulator (LQR) to track the high-level demand signals and minimise the first torsional vibration mode. The approach is demonstrated in simulation using a model of the Toyota Prius hybrid vehicle, and comparisons are made with a simpler control system which uses proportional integral (PI) control at the lower level. The powertrain of the Toyota Prius has a parallel configuration, comprising a motor, engine and generator connected via an epicyclic gear train. High level control is determined by a Power Efficient Controller (PE C) which dynamically varies the operating demands for the motor, engine and generator. The PEC is an integrated nonlinear controller based on an iterative downhill search strategy for optimising energy efficiency and battery state of charge criteria, and fully accounts for the non-linear nature of the various efficiency maps. The PEC demand signals are passed onto the LQR controller where a cost function balances the importance of deviations from these demands against an additional criterion relating to the amplitude of driveline vibrations. System non-linearity is again accounted for at the lower level through gain scheduling of the LQR controller. Controller performance is assessed. in simulation, the results being compared with a reference system that uses simple PI action to deliver low-level control. Consideration is also given to assessing performance against that of a more general, fully non-linear dynamic optimal controller

Operation Simulation and Control of a Hybrid Vehicle Based on a Dual Clutch Configuration

Today, the world thrives on making more fuel-efficient vehicles that consume less energy, emit fewer emissions and have enhanced overall performance. Hybrid Electric Vehicles (HEVs) offer the advantages of improved fuel economy and emissions without sacrificing vehicle performance factors such as safety, reliability and other features. The durability and performance enhancements of HEVs have encouraged researchers to develop various hybrid power-train configurations and improve associated issues, such as component sizing and control strategies. HEVs with dual clutch transmissions (HDCT) are used in operation modes to improve fuel efficiency and dynamic performance for both diesel engines and high-speed gas engines. Dual clutch transmissions (DCTs) are proved to be the first automatic transmission type to provide better efficiency than manual transmissions. DCTs also provide reduced shift shocks and shift time that result in better driving experience. In addition, advanced software allows more simplistic approaches and tunable launch strategies in HDCT development. In this dissertation, an innovative approach to develop a desired mode controller for a HDCT configuration is proposed. This mode controller allows the driver to select the desired driving style of the vehicle. The proposed controller was developed based on adaptive control theory for the overall HDCT system. The proposed Model Reference Adaptive Control (MRAC) was applied to a parallel hybrid electric vehicle with dual clutch transmission (HDCT), and yielded good performance under different conditions. This implies that the MRAC is adaptive to different torque distribution strategies. The current study, which was performed on adaptive control applications, revealed that the Lyapunov method was effective and yielded good performance. The MRAC method was also applied to the mode transition of an HDCT bus. The simulation results confirmed that the MRAC outperformed the conventional operation method for an HDCT with reduced vehicle jerk and the torque interruption for the driveline and with improved fuel efficiency.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/145173/1/Final Dissertation Elzaghir.pdfDescription of Final Dissertation Elzaghir.pdf : Dissertatio

Power Converter of Electric Machines, Renewable Energy Systems, and Transportation

Power converters and electric machines represent essential components in all fields of electrical engineering. In fact, we are heading towards a future where energy will be more and more electrical: electrical vehicles, electrical motors, renewables, storage systems are now widespread. The ongoing energy transition poses new challenges for interfacing and integrating different power systems. The constraints of space, weight, reliability, performance, and autonomy for the electric system have increased the attention of scientific research in order to find more and more appropriate technological solutions. In this context, power converters and electric machines assume a key role in enabling higher performance of electrical power conversion. Consequently, the design and control of power converters and electric machines shall be developed accordingly to the requirements of the specific application, thus leading to more specialized solutions, with the aim of enhancing the reliability, fault tolerance, and flexibility of the next generation power systems