@inproceedings{0df84de8e9744765b4dd8fe3287546c8,
title = "Research on internal temperature modeling of power battery based on fiber optic sensor",
abstract = "To meet the demand for real-time monitoring of the internal temperature field of lithium-ion batteries, this study proposes a modeling method for battery internal temperature field reconstruction based on fiber Bragg grating (FBG) sensors. According to the parameters of the battery used in the experiment and the internal temperature measurement requirements of the battery, an embedded FBG optical fiber sensor was designed. Then, based on the battery with the optical fiber sensor implanted, a multi-rate cycle charge and discharge experiment was carried out, and a data set of internal and external temperature of the battery was obtained. Based on correlation analysis, the relationship between internal temperature and different working condition parameters was studied, and a power battery internal temperature prediction model based on long short-term memory (LSTM) neural network was constructed to achieve accurate reconstruction modeling of internal temperature.",
keywords = "Fiber optic sensor, Machine Learning, Power Battery, Temperature Reconstruction",
author = "Mengyang Ma and Hongwen He and Haoyu Wang and Ziqi Wang",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; 3rd International Conference on Power Electronics and Artificial Intelligence, PEAI 2026 ; Conference date: 16-01-2026 Through 18-01-2026",
year = "2026",
month = apr,
day = "17",
doi = "10.1117/12.3111395",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Jianqi Liu and Parikshit Mahalle",
booktitle = "Third International Conference on Power Electronics and Artificial Intelligence, PEAI 2026",
address = "United States",
}