Fault Diagnosis Method of Lithium-Ion Batteries Based on OCSVM and RLMQD Algorithms Kunming University of Science and Technology

Format:

Book

Conference/Event

Author/Creator:

Wei, Fuxing, author.

Contributor:

Chen, Zheng

Shen, Jiangwei

Wang, Zonglei

Xia, Xuelei

Yang, Libing

Conference Name:

SAE 2025 International Conference on Battery Safety and Reliability (2025-10-23 : Shanghai, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

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

Notes:

Vendor supplied data

Publisher Number:

2026-01-7020

Access Restriction:

Restricted for use by site license