Abstract
This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.
Highlights
Electromagnetic devices have been widely employed in many domestic appliances, biomedical instruments, and industrial equipment and systems, such as electrical drive systems for air conditioners, artificial hearts, electric vehicles (EVs), and more electric aircraft, wireless power transmission systems for mobile and EV battery charging, and superconducting magnetic energy storage (SMES) for power systems
The worst-case approach is typically more affected by modeling errors, as this quantity is estimated based on a single numerical result, while design for six-sigma (DFSS) measures are determined by evaluating a significant number of design variations
A major challenge for optimizing models (1)–(6) or their single-objectives forms with an appropriate algorithm is the large computation cost, as accurate magnetic field distribution obtained from 2-D or 3-D finite element analysis (FEA) is required for many applications, like permanent magnet (PM) motors
Summary
Electromagnetic devices have been widely employed in many domestic appliances, biomedical instruments, and industrial equipment and systems, such as electrical drive systems for air conditioners, artificial hearts, electric vehicles (EVs), and more electric aircraft, wireless power transmission systems for mobile and EV battery charging, and superconducting magnetic energy storage (SMES) for power systems. From the perspective of industrial production, the performance of a good design of an electromagnetic device, like a transformer, should not be sensitive to those uncertainties To achieve this goal, reliability-based and robust optimizations have attracted significant research attention recently, especially when the industrial big data about the material and manufacturing process are considered [6,10,11,12,13,14,15]. The application of multidisciplinary analysis and/or industrial big data brings many challenges to the design optimization process and degrades the optimization performance with conventional optimization methods Advanced technologies, such as machine learning and cloud computing, will greatly improve the handling of these design optimization problems. We present an overview of the recent advances in design optimization of electromagnetic devices, including multi-objective, multidisciplinary, multilevel, topology, and robust optimization methods.
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