Semi-Analytical Approach Towards Design and Optimization of Induction Machines for Electric Vehicles

Electric machine design is a comprehensive task depending on the several factors, such as material resource limitations and economic factors. Therefore, an induction machine is a promising candidate because of the absence of magnetic material in the rotor. However, the conventional design approach can neither reflect the advances of the induction machine(IM) design nor exploit the trade-offs between design factors and the multi-physics nature of the electrical machine. Therefore, proposing fast and accurate novel methods to design, develop and analyze IMs using electromagnetic field oriented approaches is competitive to the old-fashion numerical methods. To achieve improved IM design from a baseline design to an optimal design, this dissertation: (1) Investigates the challenges of the high speed IM design specified for the electric vehicle application at the rated operating condition considering electromagnetic boundaries for the reasonable saturation level within a compact volume; (2) Proposes a new design approach of IM using modified equivalent circuit parameters to reduce spatial harmonics because of slotting effect and skewing effect; and also presents the importance of the 3-D analysis over 2-D analysis while developing the IM; (3) Proposes a novel electromagnetic field oriented mathematical model considering the slotting effect and axial flux variation because of skewing rotor bars to evaluate the IM performance with a lower and precise computational effort; (4) developed baseline IM is optimized with genetic algorithm incorporated in proposed subdomain model to improve the torque-speed profile. In order to further simplify the optimization procedure, a parametric and sensitivity based design approach is implemented to reduce the design variables. To evaluate the proposed optimal IM with extended constant power region and high torque density within a compact volume using novel 3-D subdomain model, the machine has been prototyped and tested from low to high speed under no-load and loaded condition. Electrical circuit parameter variation is demonstrated and compared to the one simulated in the FEA environment. This innovation can be applied to a family of electric machines with various topologies

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

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

Modeling, Analysis, and Neural Network Control of an EV Electrical Differential

International audienceThis paper presents system modeling, analysis, and simulation of an electric vehicle (EV) with two independent rear wheel drives. The traction control system is designed to guarantee the EV dynamics and stability when there are no differential gears. Using two in-wheel electricmotorsmakes it possible to have torque and speed control in each wheel. This control level improves EV stability and safety. The proposed traction control system uses the vehicle speed, which is different from wheel speed characterized by a slip in the driving mode, as an input. In this case, a generalized neural network algorithm is proposed to estimate the vehicle speed. The analysis and simulations lead to the conclusion that the proposed system is feasible. Simulation results on a test vehicle propelled by two 37-kW induction motors showed that the proposed control approach operates satisfactorily

Optimized Energy Control Scheme for Electric Drive of EV Powertrain Using Genetic Algorithms

Automotive applications often experience conflicting-objective optimization problems focusing on performance parameters that are catered through precisely developed cost functions. Two such conflicting objectives which substantially affect the working of traction machine drive are maximizing its speed performance and minimizing its energy consumption. In case of an electric vehicle (EV) powertrain, drive energy is bounded by battery dynamics (charging and capacity) which depend on the consumption of drive voltage and current caused by driving cycle schedules, traffic state, EV loading, and drive temperature. In other words, battery consumption of an EV depends upon its drive energy consumption. A conventional control technique improves the speed performance of EV at the cost of its drive energy consumption. However, the proposed optimized energy control (OEC) scheme optimizes this energy consumption by using robust linear parameter varying (LPV) control tuned by genetic algorithms which significantly improves the EV powertrain performance. The analysis of OEC scheme is conducted on the developed vehicle simulator through MATLAB/Simulink based simulations as well as on an induction machine drive platform. The accuracy of the proposed OEC is quantitatively assessed to be 99.3% regarding speed performance which is elaborated by the drive speed, voltage, and current results against standard driving cycles