IEEE IECON2025 SYPA Winner - Physics-Gated AI for Battery SOC Estimation скачать в хорошем качестве

IEEE IECON2025 SYPA Winner - Physics-Gated AI for Battery SOC Estimation

This video presentation describes the work in the paper titled: SocGate: Physics-Gated Neural Network for Multi-Cycle Battery State-of-Charge Estimation Authors' names: Xilin Dai, Ruidi Zhou, Jinhao Zhang, Fanfan Lin, Weifeng Zhang, Hao Ma Affiliations: Zhejiang University Paper abstract: The growing demand for energy storage systems has highlighted the importance of accurate battery State-of-Charge (SOC) estimation, which directly affects energy availability and operational safety. Existing deep learning-based methods focus on single-cycle charge or discharge estimation, and their performance degrades when applied to continuous multi-cycle scenarios. To address this, a physics-gated neural network, named SocGate, is proposed for multi-cycle battery SOC estimation. The model incorporates physical knowledge through specially designed gate functions, which constrain the estimation within realistic physical boundaries while retaining the flexibility of data-driven modeling. Experiments conducted on a public lithium-ion battery dataset demonstrate that SocGate effectively estimates SOC across sequences involving three discharge cycles, two charge cycles, and two plateau periods. The model achieves a low mean absolute error of 0.46% and root mean squared error of 0.70%.