Phison Electronics

Xiangpeng Liang, Jianshi Tang, Yanan Zhong, B. Gao, H. Qian, Huaqiang Wu

Deyin Tao, Ping Su, Aiping Chen, Dawei Gu, Mustafa Eginligil, Wei Huang

Electro-Spun nanofibers (ESNs), with their design flexibility, tailorable morphologies, and high surface area, are well-favored as triboelectric nanogenerator (TENG) materials for wearable electronics. Here, various aspects of ESNs-based wearable TENGs were examined. After introducing the most common TENG operating modes, an insightful overview of wearable TENG applications based on ESNs was presented. In this survey, a special attention is paid to wearable sensing, human-machine interaction, self-powered devices, and amplified energy harvesting. Efforts towards improving energy conversion efficiency, material durability, and compatibility with diverse wearable platforms were visited. Finally, a perspective based on particularly material aspect of ESNs is given, which could be insightful in tackling prevailing challenges and giving birth to new directions.

Chenhao Wu, Zhexin Cui, Qian Xia, Jiguang Yue, Feng Lyu

Power electronic systems (PESs) are pivotal in power generation, transmission, and diverse industrial applications. With the increasing shift toward digitalization, digital twin (DT) technology has emerged as a transformative enabler, enhancing the informatization and intelligence of PESs. This article offers a comprehensive overview of DT applications for PESs, highlighting core features of real-time synchronization, accurate mapping, seamless data interaction, and high-fidelity modeling. Four prevalent DT modeling approaches are reviewed, with a detailed discussion of their respective strengths and challenges in the context of PESs. This article further examines the role of DT across three critical lifecycle phases—design, control, and maintenance—illustrating how DT optimizes and drives innovation at each phase. By reviewing more than 170 publications, this study identifies key trends, implementation challenges, and research gaps, offering valuable insights into the state-of-the-art and future directions for DT in PESs. This work aims to serve as a foundational reference for researchers and practitioners, fostering a deep understanding of the potential of DT and providing a comprehensive grasp of the application in academics and the industry.

Peng Gao, M. Adnan

Fanfan Lin, Xinze Li, Weihao Lei, J. Rodríguez-Andina, Josep M. Guerrero, Changyun Wen, Xin Zhang, Hao Ma

Large language models (LLMs) have shown exciting potential in powering the growth of many industries, yet their adoption in the power electronics (PE) sector is hindered by a lack of specialized PE technical expertise and challenges in processing PE-specific data. This study presents a pioneering approach to establish a multimodal LLM tailored for PE design applications, named PE-GPT. The methodology involves enhancing PE-GPT with retrieval augmented generation from a PE knowledge base, and proposes a hybrid framework that integrates an LLM agent with metaheuristic algorithms, Model Zoo, and Simulation Repository. This enhances its multimodal processing capabilities and enables integration into the existing design workflow. The PE-GPT methodology is demonstrated with two case studies: modulation design of the dual-active bridge (DAB) converter and circuit parameter design of the buck converter. PE-GPT demonstrates a 22.2% increase in correctness compared to human experts. Against other leading LLMs, PE-GPT shows a 35.6% improvement in correctness and a 15.4% enhancement in consistency, reducing hallucination. Hardware experiments validate PE-GPT’s multimodal capabilities in optimizing a five-degree-of-freedom modulation strategy for the DAB converter. The generalization of PE-GPT to other PE design applications and associated AI ethical considerations are also discussed. This research concludes by outlining inspiring future research directions, encouraging researchers to expand the boundaries of the PE industry and advance toward a more intelligent era.