基于累积概率分布的锂离子电池组快速准确故障诊断方法

The iterative innovation and development of fault diagnosis methods have attracted more and more attention as a crucial technology in battery management systems. Nevertheless, the anomalous characteristics associated with early faults are not obvious, which are challenging to identify through conventional diagnosis techniques. For this reason, this article proposes a rapid-accurate fault diagnosis method based on cumulative probability distribution (CPD) for lithium-ion battery packs. The CPD algorithm can transform the battery voltage sequence into a nontime series. Two fault diagnosis submethods are designed based on the CPD algorithm, including rapid predetection method A with long-term voltage data as input, and accurate diagnosis method B with short-term voltage as input. Thereafter, these proposed methods maintain a balance between diagnosis efficiency and accuracy. The experimental results demonstrate that the proposed method can detect battery early faults and estimate the occurrence time. More importantly, the high fault detection rate (99%) and detection accuracy rate (98.02%) validate the effectiveness and universality. The research findings in this article have excellent development prospects and highlight the application potential of probability distribution for battery fault diagnosis.