An Early Minor-Fault Diagnosis Method for Lithium-Ion Battery Packs Based on Unsupervised Learning
Xin Gu, Yunlong Shang, Yongzhe Kang, Jinglun Li, Ziheng Mao, Chenghui Zhang
Abstract
Dear Editor, Any fault of a battery system that is not handled timely can cause catastrophic consequences. Therefore, it is significant to diagnose battery faults early and accurately. Due to the complex nonlinear features and inconsistency of lithium batteries, traditional fault diagnosis methods usually fail to detect battery minor faults in the early stages. Therefore, this letter proposes a real-time unsupervised learning diagnosis approach for early battery faults based on improved principal component analysis. The technique rotates the battery pack voltage sequence into a new coordinate space through linear combination, while the detection metrics of square prediction errors and modified contribution plots are employed to achieve minor fault traceability. In addition, the training sample relies on the voltage sequence of the battery health state instead of the fault data, which is difficult to collect. Moreover, this approach can not only locate the battery cell where the fault occurs but also diagnose battery open-circuit and short-circuit faults as well as the occurrence and duration of the fault in real-time. Furthermore, the feasibility and stability of the proposed method are verified by applying different experimental data. In summary, the presented approach provides an easy-to-implement option that does not require accurate mathematical modeling, expert understanding, and complex computational processes.