Revolutionizing power electronics design through large language models: applications and future directions - Ibrahim, Khalifa Aliyu; Luk, Patrick Chi-Kwong; Luo, Zhenhua; Ng, Seng Yim; Harrison, Lee (Elsevier, 2025)
The design of electronic circuits is critical for a wide range of applications, from the electrification of transportation to the Internet of Things (IoT). It demands substantial resources, is time-intensive, and can be highly intricate. Current design methods often lead to inefficiencies, prolonged design cycles, and susceptibility to human error. Advancements in artificial intelligence (AI) play a crucial role in power electronics design by increasing efficiency, promoting automation, and enhancing sustainability of electrical systems. Research has demonstrated the applications of AI in power electronics to enhance system performance, optimization, and control strategy using machine learning, fuzzy logic, expert systems, and metaheuristic methods. However, a review that includes the recent AI advancements and potential of large language models (LLMs) like generative pre-train transformers (GPT) has not been reported. This paper presents an overview of applications of AI in power electronics (PE) including the potential of LLMs. The influence of LLMs-AI on the design process of PE and future research directions is also highlighted. The development of advanced AI algorithms such as pre-train transformers, real-time implementations, interdisciplinary collaboration, and data-driven approaches are also discussed. The proposed LLMs-AI is used to design parameters of high-frequency wireless power transfer (HFWPT) using MATLAB as a first case study, and high-frequency alternating current (HFAC) inverter using PSIM as a second case study. The proposed LLM-AI driven design is verified based on a similar design reported in the literature and Wilcoxon signed-rank test was conducted to further validate the result. Results show that the LLM-AI driven design based on the OpenAI foundation model has the potential to streamline the design process of power electronics. These findings provide a good reference on the feasibility of LLMs-AI on power electronic design.
Keywords: 46 Information and Computing Sciences; 40 Engineering; 4009 Electronics, Sensors and Digital Hardware; 4010 Engineering Practice and Education; Networking and Information Technology R&D (NITRD); Machine Learning and Artificial Intelligence; 7 Affordable and Clean Energy; Electrical & Electronic Engineering; 4008 Electrical engineering; 4602 Artificial intelligence; 4606 Distributed computing and systems software; Power electronics design; AI driven design; Large language model; High frequency AC (HFAC); Wireless power transfer; Generative pre-train transformer (GPT)