Fault Diagnosis Method of Lithium-Ion Batteries Based on OCSVM and RLMQD Algorithms

To address the challenges of recognizing abnormal states, detecting subtle early warning signs, and quantifying fault severity in scenarios involving simultaneous multiple faults in lithium-ion batteries, this study proposes a dual-layer fault diagnosis framework that integrates One-Class Support Vector Machine (OCSVM) and Robust Local Mahalanobis Distance Quantile (RLMQD) algorithm. First, a three-dimensional multi-scale feature space, incorporating voltage, kurtosis, and voltage change rate, is constructed to detect abnormal battery states via OCSVM and dynamically filter abnormal time periods with improved adaptability. Second, a computationally efficient RLMQD-based quantization algorithm is developed, which employs a small-scale sliding window and adaptively selects healthy cells to construct reference distributions. By incorporating low-quantile thresholds, the algorithm enhances early abnormality detection and significantly reduces false positives. Subsequently, fault severity is quantified through scale-weighted fusion and normalization, enabling accurate evaluation across diverse abnormal modes. Finally, The diagnostic performance of the proposed method is comprehensively validated through three sets of simulation experiments and real-vehicle data collected under realistic operating conditions. The results demonstrate that the proposed method accurately identifies both single-point and clustered anomalies, corresponds closely with actual fault conditions and exhibiting strong generalization capability. In real vehicle validation, the method achieves 95.79% accuracy, 100% recall, and a 93.3% F1 score in abnormal detection tasks. Furthermore, It demonstrates robustness and interpretability, enabling multi-type abnormal detection and fault severity evaluation without reliance on extensive fault datasets, thereby offering high suitablility for online monitoring and early warning in actual Battery Management Systems.