AI-Enabled Power Electronics, Electrical Machines, and Energy Management Systems for High-Efficiency Sustainable Energy Conversion

AI is becoming a key enabler of high-efficiency and sustainable energy conversion in power converters, electrical machines, electric drives, renewable-energy interfaces, and advanced energy-management systems. This critical review examines how machine learning, deep learning, reinforcement learning, physics-informed models, digital twins, and optimization algorithms enhance the design, control, monitoring, and operation of modern electromechanical energy-conversion systems. The paper outlines the main AI methods relevant to power electronics and electrical machines, distinguishing between data-driven, model-assisted, and hybrid approaches. It summarizes AI applications in power converter design, modulation, fault diagnosis, thermal management, wide-bandgap semiconductor operation, grid-connected renewable energy converters, and electric vehicle thermal and energy management, range characterization, and charging. A dedicated section covers electrical machines and drives, including AI-assisted electromagnetic and thermal design, condition monitoring, sensorless control, efficiency-map optimization, and predictive maintenance. To distinguish the scale of the claimed engineering outcome from the maturity of the supporting evidence, the review introduces an E1–E4 engineering outcome scale together with the AI Energy-Conversion Evidence Maturity (AI-ECEM) M1–M5 scale, a minimum reporting bundle, a net-benefit accounting framework, and a deployment roadmap. The synthesis indicates that surrogate-assisted design and diagnostic classification are comparatively mature, whereas autonomous real-time control and lifecycle deployment require stronger hardware, robustness, cybersecurity, and field evidence.