Early diagnosis of battery faults through an unsupervised health scoring method for real-world applications
Artikel i vetenskaplig tidskrift, 2024

Battery fault diagnosis is critical to ensure the safe and reliable operation of electric vehicles or energy storage systems. Early diagnosis of battery faults can enable timely maintenance and reduce potential accidents. However, the lead time for detection is still relatively insufficient, and the identification of target vehicle with unidentified fault type has generally been neglected. To fill the gap, an unsupervised health scoring method for early diagnosis of battery faults is proposed in this paper. First, considering the properties of field data, new features and four types of feature sets related to battery health and fault status are derived for each cell. Then, a novel strategy is proposed to transform a typical classification problem into a quantitative scoring problem by performing multiple clustering. To produce ample clustering results, three cluster algorithms based on different principles are used and the features are randomly divided into feature subsets. By coupling temperature information, early determination of thermal runaway faults can be achieved. Finally, the real-world cloud data of three typical accidents are employed for verification, the results indicate that the proposed approach can innovatively achieve the detection of the abnormal cells at the level of days in advance, demonstrating excellent performance.

health scoring

thermal runaway

Maintenance engineering

Fault diagnosis

Cloud computing

unsupervised learning

Feature extraction

Circuit faults

early diagnosis

Batteries

Lithium-ion battery

Clouds

Författare

Wenchao Guo

Shanghai Jiao Tong University

Lin Yang

Shanghai Jiao Tong University

Zhongwei Deng

Shanghai Jiao Tong University

Bing Xiao

Carbon Technology Group Co. Ltd.

Xiaolei Bian

Chalmers, Elektroteknik, System- och reglerteknik

IEEE Transactions on Transportation Electrification

2332-7782 (eISSN)

Vol. 10 2 2521-2532

Ämneskategorier

Energiteknik

Beräkningsmatematik

DOI

10.1109/TTE.2023.3300302

Mer information

Senast uppdaterat

2024-07-27