Real-Time Anomaly Detection in Lithium-Ion Battery Systems utilizing Stream Computing

In the crucial field of lithium-ion battery safety, the existing anomaly detection algorithms encounter notable limitations. Specifically, their real-time performance and efficiency leave much to be desired, especially considering the escalating demands of modern application scenarios that necessitate swift response capabilities. In light of this, our study pioneers a revolutionary anomaly feature extraction algorithm underpinned by stream computing. This innovative approach incorporates a sophisticated multi-level warning mechanism, which is meticulously designed to execute differentiated warning strategies corresponding to distinct anomaly characteristics. The algorithm's efficacy was validated with six months of operational data from several battery packs in the energy storage systems. The proposed method not only accurately identifies inconsistent packs but also provides a timely and staged early warning system, progressing from initial primary warnings to intermediate cautions and ultimately to advanced alarms. This advancement substantially bolsters the real-time anomaly detection prowess, slashes decision-making delays, and lays a solid groundwork for ensuring the seamless and stable operation of energy storage systems, thus making a significant contribution to the development of lithium-ion battery technology.