@inproceedings{3d74399a6da84dac82e67d42fc1732ac,
title = "Predicting Remaining Useful Life of Lithium-Ion Battery Using Extended Belief Rule Base Model",
abstract = "The development of new energy vehicles is a key factor in the adjustment of China's energy structure and the decrease in carbon emissions. It is a frontier field for China to achieve high-quality development and construct a modern socialist nation fully. However, because lithium-ion battery used in new energy vehicles have a limited lifespan, it is likely to have a very significant security risk when the lithium-ion battery is not replaced in a timely manner. Predicting the lithium-ion battery's remaining useful life (RUL) is crucial for this reason. In order to forecast the RUL while taking health indicators (HI) into account, the extended belief rule base (EBRB) model is introduced in this paper. The EBRB model's capacity to handle complicated modeling issues helps to increase the RUL prediction's accuracy and interpretability. This study is of great significance for promoting the development of new energy vehicles, adjusting China's energy structure, and reducing carbon emissions.",
keywords = "data-driven, extended belief rule base, health indicators, lithium-ion battery, remaining useful life",
author = "Yang, {Long Hao} and Qian, {Bei Ya} and Huang, {Chen Xi} and Ye, {Fei Fei} and Haibo Hu and Wu, {Hai Dong}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 18th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2023 ; Conference date: 17-11-2023 Through 19-11-2023",
year = "2024",
month = apr,
doi = "10.1109/ISKE60036.2023.10480946",
language = "English",
series = "ISKE 2023 - 18th International Conference on Intelligent Systems and Knowledge Engineering",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "585--591",
booktitle = "ISKE 2023 - 18th International Conference on Intelligent Systems and Knowledge Engineering",
}