Real-Time Quantum Machine Learning-Based Anomaly Detection for Lithium-Ion Battery Packs and Identification of Defective Cell

Lithium-ion batteries are widely used in applications ranging from electric vehicles to renewable energy storage, making real-time monitoring essential for ensuring safety and efficiency. Anomalies such as cell degradation, thermal runaway, and overcurrent conditions can lead to severe performance issues and potential hazards. Clustering-based anomaly detection is a common approach for identifying such irregularities, but traditional methods like K-Means face challenges in efficiently processing high-dimensional battery monitoring data, adapting to dynamic changes, and distinguishing between transient fluctuations and genuine anomalies. Quantum K-Means, which utilizes quantum state encoding and entanglement, offers a different approach by accelerating distance computations between data points and centroids, improving performance in real-time applications. This study focuses on detecting defective battery cells through anomaly detection while also ensuring cell balancing to enhance battery longevity. By integrating real-time clustering with historical trend analysis, the detection framework can effectively distinguish between persistent faults and temporary fluctuations, reducing false positives. A comparative analysis is conducted between Hybrid Quantum K-Means clustering and various classical clustering algorithms to evaluate their effectiveness in identifying anomalies and optimizing battery performance by finding the defective cell. The findings provide insights into the advantages and limitations of quantum-enhanced clustering techniques in real-time battery pack monitoring and cell balancing.