Abstract
Abstract
Accurate Time-to-Empty (TTE) prediction is a cornerstone for optimizing system energy efficiency and enhancing user experience in modern mobile computing. This paper presents a continuous-time multi-physics coupled framework that integrates second-order RC battery dynamics, temperature-dependent thermal feedback, and user-driven modular workloads. Unlike conventional Coulomb-counting or static equivalent-circuit approaches, the proposed framework explicitly captures transient polarization dynamics and temperature-induced impedance growth under realistic burst workloads and sub-zero environments. Validated against Panasonic 18650PF datasets (UW-Madison) at -20℃, the results demonstrate that the proposed framework achieves a Mean Absolute Percentage Error (MAPE) of 1.42% under dynamic UDDS workloads. This work demonstrates that accurate smartphone runtime prediction is strongly influenced by voltage-stability dynamics, providing a scalable and physically interpretable foundation for next-generation intelligent battery-management systems.