@inproceedings{6ea37def65ec456bb5addb88242d6bfc,
title = "Machine Learning-based Heat Generation Rate Estimation and Diagnosis for Lithium-ion Batteries",
abstract = "Heat generation rate is a significant safety indicator for lithium-ion battery thermal management which need to be monitored in real time. A distributed fiber optic sensor embedded smart battery configuration is proposed in this paper to acquire the multi-point temperature measurements inside and outside the battery. Hence, a machine learning-based heat generation rate estimation and diagnosis method for Lithium-ion batteries is proposed in this paper to estimate the heat generation rate leveraging the multi-point temperature measurements and detect the abnormal heat generation in real time. The proposed heat generation rate estimation method and smart configuration are experimentally validated to be effective and accurate, and the proposed abnormal heat generation diagnosis method is verified by simulation.",
keywords = "Lithium-ion battery, battery thermal management, fault diagnosis, heat generation rate, smart battery",
author = "Jian Hu and Zhongbao Wei and Hongwen He",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 6th IEEE International Conference on Energy Internet, ICEI 2022 ; Conference date: 28-12-2022 Through 29-12-2022",
year = "2022",
doi = "10.1109/ICEI57064.2022.00024",
language = "English",
series = "Proceedings - 2022 IEEE International Conference on Energy Internet, ICEI 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "108--112",
booktitle = "Proceedings - 2022 IEEE International Conference on Energy Internet, ICEI 2022",
address = "United States",
}
