Pil Batarya Degradasyonunun Tokenleştirilmesi: Sağlık Durumu Tahmini İçin Fizik Bilgili ve Belirsizlik Farkındalığı Olan Bir Çerçeve

Özet (abstract)

Accurate and trustworthy estimation of State of Health (SOH) is essential for the safe and efficient operation of modern battery management systems. While data-driven methods have achieved high accuracy, they often lack physical grounding and fail to provide reliable confidence metrics for their predictions, posing risks in safety-critical applications. In this work, we introduce TBD-SOH, an SOH estimation framework that combines physics-informed feature construction with uncertainty-aware regression. Specifically, each charging cycle is represented as a fused cycle-level token constructed from two complementary sources: statistical descriptors of charging behavior and a hardware-free pseudo-impedance representation derived from routine voltage and current measurements in the frequency domain. The pseudo-impedance branch is introduced as a degradation-sensitive proxy rather than a direct electrochemical impedance spectroscopy measurement. In addition, an MC dropout-based regression head is adopted to provide confidence intervals together with point estimates. Comprehensive experiments on the public MIT and HUST battery datasets demonstrate that TBD-SOH achieves high accuracy, outperforming conventional deep learning models. More importantly, its uncertainty estimates are shown to be dynamically responsive to prediction difficulty, and provide a new reference point for developing trustworthy and reliable battery health monitoring systems.

Yazarların özeti; kaynağından alınmıştır. Applied Energy, 2026 · DOI ↗