@inproceedings{9156bbd83b254e5a842cb0d700d6bfad,
title = "A Data-Driven Algorithm for Short Circuit Fault Diagnosis of Power Batteries",
abstract = "As a core component of electric vehicles, the power battery is susceptible to various failures with a complex coupling mechanism, making it difficult to achieve precise battery fault diagnosis during real-world driving conditions. To address this issue, this paper proposes a fault diagnosis algorithm based on a sparse data observer. Firstly, the sparse data observer algorithm is utilized to calculate the abnormal degree of the power battery voltage based on actual vehicle data. Secondly, appropriate thresholds are set by combining the existing healthy vehicle data using the 3σ-rule. Finally, the abnormal cell in the battery pack is rapidly identified in different segments. The complexity and variability of the actual operation of the power battery system are considered in the design of this model. The proposed method can accurately identify the abnormal battery cell in the battery pack and diagnose lithium battery faults.",
keywords = "Data Drive, Fault Diagnosis, Power Battery, Sparse Data Observer",
author = "Jian Sun and Peng Liu and Zhenyu Sun and Yiwen Zhao and Jinquan Pan and Cheng Liu and Zhenpo Wang and Zhaosheng Zhang",
note = "Publisher Copyright: {\textcopyright} Beijing Paike Culture Commu. Co., Ltd. 2024.; 18th Annual Conference of China Electrotechnical Society, ACCES 2023 ; Conference date: 15-09-2023 Through 17-09-2023",
year = "2024",
doi = "10.1007/978-981-97-1068-3_18",
language = "English",
isbn = "9789819710676",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "164--173",
editor = "Qingxin Yang and Zewen Li and An Luo",
booktitle = "The Proceedings of the 18th Annual Conference of China Electrotechnical Society - Volume VI",
address = "Germany",
}