A Stochastic Hybrid Automaton for Smartphone Battery Dynamics: Electro-Thermal Coupling and First-Passage Time-to-Empty Estimation

TL;DR

This paper introduces a stochastic hybrid automaton model for smartphone battery life, capturing electro-thermal effects and user behavior to predict time-to-empty and shutdown risks more accurately.

Contribution

It develops a novel coupled electro-thermal stochastic model that predicts battery shutdown events considering temperature, aging, and user activity, providing a risk-aware framework.

Findings

Voltage-collapse mechanism explains premature shutdowns.

Monte Carlo simulation provides TTE distribution and risk quantification.

Sensitivity analysis identifies key factors affecting shutdown risk.

Abstract

Smartphone time-to-empty (TTE) is difficult to predict because shutdown is governed not only by remaining charge, but also by instantaneous power capability under temperature-, aging-, and load-dependent voltage sag. We develop a stochastic hybrid automaton for smartphone battery dynamics that couples a first-order Thevenin equivalent-circuit model with a lumped thermal model and a stochastic user-activity process. The continuous state includes state of charge, polarization voltage, and battery temperature; user behavior is represented as a piecewise deterministic Markov process switching among idle, social/web, video, gaming, and weak-signal modes. Shutdown is formulated as a first-passage event when terminal voltage crosses a cutoff threshold or when requested power exceeds the instantaneous feasibility envelope. The model captures a voltage-collapse mechanism that simple Coulomb-counting or linear discharge models miss: cold temperature or battery aging increases internal resistance, so high-power bursts can drive terminal voltage below cutoff even when substantial charge remains. Monte Carlo simulation yields a full TTE distribution rather than a single countdown, allowing lower-tail risk to be quantified by the 5th percentile. Sensitivity analysis identifies ambient temperature, internal resistance, weak-signal radio penalty, and screen brightness as major drivers of premature shutdown risk. These results motivate practical user guidance and an operating-system-level resistance-aware throttling policy that limits peak power in the power-limited regime. The framework provides a physically grounded, risk-aware approach for explaining and extending usable smartphone battery life under real-world uncertainty.