Computationally Efficient Optimization of a Five-Phase Flux-Switching PM Machine Under Different Operating Conditions

This paper investigates the comparative design optimizations of a five-phase outer-rotor flux-switching permanent magnet (FSPM) machine for in-wheel traction applications. To improve the comprehensive performance of the motor, two kinds of large-scale design optimizations under different operating conditions are performed and compared, including the traditional optimization performed at the rated operating point and the optimization targeting the whole driving cycles. Three driving cycles are taken into account, namely, the urban dynamometer driving schedule (UDDS), the highway fuel economy driving schedule (HWFET), and the combined UDDS/HWFET, representing the city, highway, and combined city/highway driving, respectively. Meanwhile, the computationally efficient finite-element analysis (CE-FEA) method, the cyclic representative operating points extraction technique, as well as the response surface methodology (in order to minimize the number of experiments when establishing the inverse machine model), are presented to reduce the computational effort and cost. From the results and discussion, it will be found that the optimization results against different operating conditions exhibit distinct characteristics in terms of geometry, efficiency, and energy loss distributions. For the traditional optimization performed at the rated operating point, the optimal design tends to reduce copper losses but suffer from high core losses; for UDDS, the optimal design tends to minimize both copper losses and PM eddy-current losses in the low-speed region; for HWFET, the optimal design tends to minimize core losses in the high-speed region; for the combined UDDS/HWFET, the optimal design tends to balance/compromise the loss components in both the low-speed and high-speed regions. Furthermore, the advantages of the adopted optimization methodologies versus the traditional procedure are highlighted

Comparison and Design Optimization of a Five-Phase Flux-Switching PM Machine for In-Wheel Traction Applications

A comparative study of five-phase outer-rotor flux-switching permanent magnet (FSPM) machines with different topologies for in-wheel traction applications is presented in this paper. Those topologies include double-layer winding, single-layer winding, C-core, and E-core configurations. The electromagnetic performance in the low-speed region, the flux-weakening capability in the high-speed region, and the fault-tolerance capability are all investigated in detail. The results indicate that the E-core FSPM machine has performance advantages. Furthermore, two kinds of E-core FSPM machines with different stator and rotor pole combinations are optimized, respectively. In order to reduce the computational burden during the large-scale optimization process, a mathematical technique is developed based on the concept of computationally efficient finite-element analysis. While a differential evolution algorithm serves as a global search engine to target optimized designs. Subsequently, multiobjective tradeoffs are presented based on a Pareto-set for 20 000 candidate designs. Finally, an optimal design is prototyped, and some experimental results are given to confirm the validity of the simulation results in this paper

Multi-Objective Drive-Cycle Based Design Optimization of Permanent Magnet Synchronous Machines

Research conducted previously has shown that a battery electric vehicle (BEV) motor design incorporating drive-cycle optimization can lead to achievement of a higher torque density motor that consumes less energy over the drive-cycle in comparison to a conventionally designed motor. Such a motor indirectly extends the driving range of the BEV. Firstly, in this thesis, a vehicle dynamics model for a direct-drive machine and its associated vehicle parameters is implemented for the urban dynamometer driving schedule (UDDS) to derive loading data in terms of torque, speed, power, and energy. K-means clustering and Gaussian mixture modeling (GMM) are two clustering techniques used to reduce the number of machine operating points of the drive-cycle while preserving the characteristics of the entire cycle. These methods offer high computational efficiency and low computational time cost while optimizing an electric machine. Differential evolution (DE) is employed to optimize the baseline fractional slot concentrated winding (FSCW) surface permanent magnet synchronous machine (SPMSM). A computationally efficient finite element analysis (CEFEA) technique is developed to evaluate the machine at the representative drive-cycle points elicited from the clustering approaches. In addition, a steady-state thermal model is established to assess the electric motor temperature variation between optimization design candidates. In an alternative application, the drive-cycle cluster points are utilized for a computationally efficient drive-cycle system simulation that examines the effects of inverter time harmonics on motor performance. The motor is parameterized and modeled in a PSIM motor-inverter simulation that determines the current excitation harmonics that are injected into the machine during drive-cycle operation. These current excitations are inserted into the finite element analysis motor simulation for accurate analysis of the harmonic effects. The analysis summarizes the benefits of high-frequency devices such as gallium nitride (GaN) in comparison to insulated gate bipolar transistors (IGBT) in terms of torque ripple and motor efficiency on a drive-cycle

Computationally-efficient Finite-element-based Thermal and Electromagnetic Models of Electric Machines.

With the modern trend of transportation electrification, electric machines are a key component of electric/hybrid electric vehicle (EV/HEV) powertrains. It is therefore important that vehicle powertrain-level and system-level designers and control engineers have access to accurate yet computationally-efficient (CE), physics-based modeling tools of the thermal and electromagnetic (EM) behavior of electric machines. In this dissertation, CE yet accurate thermal and EM models for electric machines, which are suitable for use in vehicle powertrain design, optimization, and control, are developed. This includes not only creating fast and accurate thermal and EM models for specific machine designs, but also the ability to quickly generate and determine the performance of new machine designs through the application of scaling techniques to existing designs. With the developed techniques, the thermal and EM performance can be accurately and efficiently estimated. Furthermore, powertrain or system designers can easily and quickly adjust the characteristics and the performance of the machine in ways that are favorable to the overall vehicle performance.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113497/1/kanzhou_1.pd

Overview of Sensitivity Analysis Methods Capabilities for Traction AC Machines in Electrified Vehicles

© 2021 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.A robust design in electrified powertrains substantially helps to enhance the vehicle's overall efficiency. Robustness analyses come with complexity and computational costs at the vehicle level. The use of sensitivity analysis (SA) methods in the design phase has gained popularity in recent years to improve the performance of road vehicles while optimizing the resources, reducing the costs, and shortening the development time. Designers have started to utilize the SA methods to explore: i) how the component and vehicle level design options affect the main outputs i.e. energy efficiency and energy consumption; ii) observing sub-dependent parameters, which might be influenced by the variation of the targeted controllable (i.e. magnet thickness) and uncontrollable (i.e. magnet temperature) variables, in nonlinear dynamic systems; and iii) evaluating the interactions, of both dependent, and sub-dependent controllable/uncontrollable variables, under transient conditions. Hence the aim of this study is to succinctly review recent utilization of SA methods in the design of AC electric machines (EM)s used in vehicle powertrains, to evaluate and discuss the findings presented in recent research papers while summarizing the current state of knowledge. By systematically reviewing the literature on applied SAs in electrified powertrains, we offer a bibliometric analysis of the trends of application-oriented SA studies in the last and next decades. Finally, a numerical-based case study on a third-generation TOYOTA Prius EM will be given, to verify the SA-related findings of this article, alongside future works recommendations.Peer reviewe

A Novel Design Optimization of a Fault-Tolerant AC Permanent Magnet Machine-Drive System

In this dissertation, fault-tolerant capabilities of permanent magnet (PM) machines were investigated. The 12-slot 10-pole PM machines with V-type and spoke-type PM layouts were selected as candidate topologies for fault-tolerant PM machine design optimization problems. The combination of 12-slot and 10-pole configuration for PM machines requires a fractional-slot concentrated winding (FSCW) layout, which can lead to especially significant PM losses in such machines. Thus, a hybrid method to compute the PM losses was investigated, which combines computationally efficient finite-element analysis (CE-FEA) with a new analytical formulation for PM eddy-current loss computation in sine-wave current regulated synchronous PM machines. These algorithms were applied to two FSCW PM machines with different circumferential and axial PM block segmentation arrangements. The accuracy of this method was validated by results from 2D and 3D time-stepping FEA. The CE-FEA approach has the capabilities of calculating torque profiles, induced voltage waveforms, d and q-axes inductances, torque angle for maximum torque per ampere load condition, and stator core losses. The implementation techniques for such a method are presented. A combined design optimization method employing design of experiments (DOE) and differential evolution (DE) algorithms was developed. The DOE approaches were used to perform a sensitivity study from which significant independent design variables were selected for the DE design optimization procedure. Two optimization objectives are concurrently considered for minimizing material cost and power losses. The optimization results enabled the systematic comparison of four PM motor topologies: two different V-shape, flat bar-type and spoke-type, respectively. A study of the relative merits of each topology was determined. An automated design optimization method using the CE-FEA and DE algorithms was utilized in the case study of a 12-slot 10-pole PM machine with V-type PM layout. An engineering decision process based on the Pareto-optimal front for two objectives, material cost and losses, is presented together with discussions on the tradeoffs between cost and performance. One optimal design was finally selected and prototyped. A set of experimental tests, including open circuit tests at various speeds and on-load tests under various load and speed conditions, were performed successfully, which validated the findings of this work

Induction motors versus permanent magnet actuators for aerospace applications

This paper introduces a comparative study on the design of aerospace actuators concerning Induction Motor (IM) and Permanent Magnet Motor (PMM) technologies. In the analysis undertaken, the two candidate configurations are evaluated in terms of both their electromagnetic and thermal behavior in a combined manner. On a first step, the basic dimensioning of the actuators and their fundamental operational characteristics are determined via a time-stepping Finite Element (FE) analysis. The consideration of the thermal robustness of the proposed motor configurations is integrated in the design procedure, through the appropriate handling of their respective constraints. As a result, all comparisons are carried out on a common thermal evacuation basis. On a second step, a single objective optimization procedure is employed, considering several performance and efficiency indexes using appropriate weights. Manufacturing and construction related costs for both investigated topologies are considered employing specific penalty functions. The impact of the utilized materials is also examined. The resultant motor designs have been validated through manufactured prototypes illustrating their suitability for aerospace actuatio

Multiple Objective Co-Optimization of Switched Reluctance Machine Design and Control

This dissertation includes a review of various motor types, a motivation for selecting the switched reluctance motor (SRM) as a focus of this work, a review of SRM design and control optimization methods in literature, a proposed co-optimization approach, and empirical evaluations to validate the models and proposed co-optimization methods. The switched reluctance motor (SRM) was chosen as a focus of research based on its low cost, easy manufacturability, moderate performance and efficiency, and its potential for improvement through advanced design and control optimization. After a review of SRM design and control optimization methods in the literature, it was found that co-optimization of both SRM design and controls is not common, and key areas for improvement in methods for optimizing SRM design and control were identified. Among many things, this includes the need for computationally efficient transient models with the accuracy of FEA simulations and the need for co-optimization of both machine geometry and control methods throughout the entire operation range with multiple objectives such as torque ripple, efficiency, etc. A modeling and optimization framework with multiple stages is proposed that includes robust transient simulators that use mappings from FEA in order to optimize SRM geometry, windings, and control conditions throughout the entire operation region with multiple objectives. These unique methods include the use of particle swarm optimization to determine current profiles for low to moderate speeds and other optimization methods to determine optimal control conditions throughout the entire operation range with consideration of various characteristics and boundary conditions such as voltage and current constraints. This multi-stage optimization process includes down-selections in two previous stages based on performance and operational characteristics at zero and maximum speed. Co-optimization of SRM design and control conditions is demonstrated as a final design is selected based on a fitness function evaluating various operational characteristics including torque ripple and efficiency throughout the torque-speed operation range. The final design was scaled, fabricated, and tested to demonstrate the viability of the proposed framework and co-optimization method. Accuracy of the models was confirmed by comparing simulated and empirical results. Test results from operation at various torques and speeds demonstrates the effectiveness of the optimization approach throughout the entire operating range. Furthermore, test results confirm the feasibility of the proposed torque ripple minimization and efficiency maximization control schemes. A key benefit of the overall proposed approach is that a wide range of machine design parameters and control conditions can be swept, and based on the needs of an application, the designer can select the appropriate geometry, winding, and control approach based on various performance functions that consider torque ripple, efficiency, and other metrics

Design Synthesis and Optimization of Permanent Magnet Synchronous Machines Based on Computationally-Efficient Finite Element Analysis

In this dissertation, a model-based multi-objective optimal design of permanent magnet ac machines, supplied by sine-wave current regulated drives, is developed and implemented. The design procedure uses an efficient electromagnetic finite element-based solver to accurately model nonlinear material properties and complex geometric shapes associated with magnetic circuit design. Application of an electromagnetic finite element-based solver allows for accurate computation in intricate performance parameters and characteristics. The first contribution of this dissertation is the development of a rapid computational method that allows accurate and efficient exploration of large multi-dimensional design spaces in search of optimum design(s). The computationally efficient finite element-based approach developed in this work provides a framework of tools that allow rapid analysis of synchronous electric machines operating under steady-state conditions. In the developed modeling approach, major steady-state performance parameters such as, winding flux linkages and voltages, average, cogging and ripple torques, stator core flux densities, core losses, efficiencies and saturated machine winding inductances, are calculated with minimum computational effort. In addition, the method includes means for rapid estimation of distributed stator forces and three-dimensional effects of stator and/or rotor skew on the performance of the machine. The second contribution of this dissertation is the development of the design synthesis and optimization method based on a differential evolution algorithm. The approach relies on the developed finite element-based modeling method for electromagnetic analysis and is able to tackle large-scale multi-objective design problems using modest computational resources. Overall, computational time savings of up to two orders of magnitude are achievable, when compared to current and prevalent state-of-the-art methods. These computational savings allow one to expand the optimization problem to achieve more complex and comprehensive design objectives. The method is used in the design process of several interior permanent magnet industrial motors. The presented case studies demonstrate that the developed finite element-based approach practically eliminates the need for using less accurate analytical and lumped parameter equivalent circuit models for electric machine design optimization. The design process and experimental validation of the case-study machines are detailed in the dissertation