Enhancing safety of lithium-ion batteries in sustainable energy systems through intelligent minor short-circuits fault detection

The rapid growth of renewable energy integration and electric mobility has increased the demand for safe and reliable lithium-ion batteries, which are essential due to their high energy density, long lifespan, and efficiency. However, complex internal electrochemical reactions and external operational stress can induce minor short circuits (MSC) that are difficult to detect at early stages yet may escalate to thermal runaway, posing significant risks to large-scale energy storage systems. To address this challenge, this study proposes an unsupervised MSC fault diagnosis framework that integrates a hybrid feature extraction strategy with a deep support vector data description algorithm. The method employs two-dimensional correlation coefficients and two-dimensional wavelet transform to capture voltage consistency across cells and detect transient anomalies associated with fault development. These complementary features are fused into a multidimensional representation and processed by the deep model, which learns compact patterns of normal operating states and constructs a hypersphere for anomaly detection. The framework is validated using a laboratory module with six battery cells, demonstrating effective fault identification under varying operating conditions, fault severities, and battery chemistries, achieving a 94 % fault detection rate with a 3 % false alarm rate. Furthermore, the computational procedure relies on matrix-based feature construction and a lightweight feed-forward inference process, offering computational efficiency suitable for real-time deployment in battery management systems. Benefiting from its unsupervised and data-driven design, the framework exhibits strong generalizability under diverse conditions and provides a promising pathway for enhancing the safety and reliability of future energy storage applications.