Topology Optimization and AI-based Design of Power Electronic and Electrical Devices

Topology optimization and AI-based design of power electronic and electrical devices provides an essential foundation in the emergent design methodology as it moves toward commercial development, including electrical devices as traction motors for electric motors, transformers, inductors, reactors, and power electronics circuits.

Opening with an introduction to electromagnetism and computational electromagnetics for optimal design, this book outlines principles and foundations in finite element methods and illustrates numerical techniques useful for finite element analysis. It summarizes the foundations of deterministic and stochastic optimization methods, including genetic algorithm, CMA-ES, and simulated annealing for quantum and quantum-inspired optimization, alongside representative algorithms. The book goes on to discuss parameter optimization and topology optimization of electrical devices alongside current implementations including magnetic shields, 2D and 3D models of electric motors, and wireless power transfer devices. Finally, it concludes with a lengthy exposition of AI-based design methods, including surrogate models for optimization, Bayesian optimization, direct inverse modeling, deep neural networks, explainable AI, variational autoencoder, and integrated design methods using Monte Carlo tree searches for electrical devices and circuits.