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We proposed PE-GPT in early 2024, the first generative AI agent specifically designed for power electronics, advancing AI-aided workflows toward an AI-native autonomous paradigm. Through collective efforts across the power electronics community, PE-GPT has been developed and validated across a broad range of design problems, including flyback, DAB, buck, and boost converter design; magnetics design including inductors and PCB transformers; and power semiconductor characterization and design.
Keeping "AI-for-Good" as our mission, PE-GPT strives to revolutionize the paradigm for diverse power electronics tasks.
For more information and collaborations, please visit the official website of PE-GPT.
Fig. 1. The hybrid framework of PE-GPT.
Demo videos of using PE-GPT for the power electronics design tasks.
Demo Case 1:
- Flyback Converter Design with Component Selection
Media1.mp4
Demo Case 2:
- Modulation Optimization for DAB Converters - 1
Design.case.-.DAB1.mp4
Demo Case 3:
- Modulation Optimization for DAB Converters - 2
Design.case.-.DAB2.mp4
Demo Case 4:
- Circuit Parameter Design for Buck Converters
Design.case.-.buck.mp4
- To deploy PE-GPT on your PC, the first step is to setup your API call to OpenAI models, please see core/llm/llm.py for more details.
- If you want to interact with Plecs software to simulate the designed modulation for DAB, you need to enable the xml-rpc interface in Plecs settings,
and to add the directory "core/simulation/devices" in the device library searching path in plecs.
# clone the github repository
git clone https://github.com/XinzeLee/PE-GPT
# change the current working directory
cd PE-GPT
# install all required dependencies
pip install -r requirements_specific.txt
# run the GUI and chat with PE-GPT
streamlit run main.py
@reference: Fanfan Lin, Xinze Li, Weihao Lei, Juan J. Rodriguez-Andina, Josep M. Guerrero, Changyun Wen, Xin Zhang, and Hao Ma, "PE-GPT: a New Paradigm for Power Electronics Design", IEEE Transactions on Industrial Electronics.
@code-author: Xinze Li (email: xinzeli831@gmail.com), Fanfan Lin (email: fanfanlin31@gmail.com)
- This repository provides a simplified version of the PE-GPT methodology presented in our journal paper. Despite the simplifications, the released code preserves the overall core architecture of the proposed PE-GPT.
- This repository currently includes the following functions/blocks: Retrieval augmented generation, LLM agents, Model Zoo (with a physics-in-architecture neural network, PANN, for modeling DAB converters), metaheuristic algorithm for optimization, simulation verification, graphical user interface, and knowledge base. Please note that the current knowledge base is a simplified version for illustration.
This project is licensed under the GNU AGPLv3 with additional terms restricting commercial training.
While the code may be used freely in open source projects and in academic teaching/research, it may not be used to provide paid courses or commercial training services. See the GNU AGPLv3 file for full terms.