A Hierarchical Fault Detection Method for Lithium-Ion Batteries in Real-World Vehicle Applications

With the rapid expansion of the electric vehicle market, the safety of lithium-ion batteries, which serve as the main power source, has become a critical concern. Current mainstream methods for battery fault detection generally face a technical bottleneck of struggling to balance high accuracy with a low false alarm rate. Furthermore, constrained by algorithmic complexity and data processing efficiency, detection speeds often fail to meet the practical demands of real-time monitoring. As a result, developing more efficient and accurate fault detection technologies has emerged as a key challenge urgently needing to be addressed in the industry. This paper proposes a hierarchical fault detection framework for lithium-ion batteries that integrates voltage change characteristics with a Local Outlier Factor (LOF) scoring mechanism. The framework aims to achieve early identification and accurate diagnosis of abnormal battery states through multi-dimensional feature extraction and algorithmic fusion. In the first layer, decentralized voltage data are standardized using the 3σ rule to identify potentially anomalous batteries. In the secondary analysis phase, a sliding time-window mechanism is adopted to dynamically capture voltage sequences. Within each window, voltage variations are calculated along both vertical and horizontal directions, and statistical metrics, including mean, standard deviation, range, and increment are derived. Principal component analysis is then applied to extract key features, and battery anomalies are evaluated and confirmed based on the maximum LOF score. Experimental validation using datasets from vehicles that experienced thermal runaway events, along with data from 1,000 normal vehicles, demonstrates that the proposed method significantly improves the accuracy of battery fault detection. It also provides early warnings up to 17 days prior to the occurrence of thermal runaway.