Continuous‐Time State‐Space Modeling of Smartphone Batteries: Evaluating the Impact of Concurrent Computational Workloads on Electro‐Thermal Dynamics

This study develops a physically interpretable, reduced‐order continuous‐time electro‐thermal state‐space model to accurately estimate the State of Charge ( SOC ) and endurance of smartphone lithium‐ion batteries, addressing the limitations of existing models in capturing nonlinear thermal dynamics and polarization effects. By integrating a first‐order RC (Thevenin) Equivalent Circuit Model with modular power consumption profiles for screen, CPU , and network components, the framework dynamically updates electrical parameters as functions of SOC and temperature. The bounded inverse‐identification problems are solved using a Genetic Algorithm ( GA ), and ODE45 is used only for forward state integration, while Global Sensitivity Analysis ( GSA ) is employed to quantify the impact of environmental and usage variables. Simulation results demonstrate the model's high fidelity, achieving a voltage RMSE of 0.0311 V and a temperature RMSE of 0.1010°C. The analysis identifies ambient temperature as an important contributor to endurance variability and also reveals a nonlinear sensitivity of endurance to processor‐load variations within the coupled electro‐thermal framework . This integrated electro‐thermal framework provides a practical balance between computational efficiency and predictive accuracy, while offering a quantitative basis for identifying environmental and workload‐related variables relevant to future power‐management design in mobile devices.