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Predicting smartphone battery time-to-empty(TTE) on an open-source PinePhone platform: A modular electro-thermal
Yinda Long1, Shengjie Tian2, Xiaoying Liu1
1College of Information and Intelligence, Hunan Agricultural University, Changsha, Hunan, China.
Plos One
|August 10, 2026
Summary
A new electro-thermal model accurately predicts smartphone battery life (time-to-empty) by considering device workload and environmental factors. Optimization strategies can extend heavy usage time by over 150%.
Area of Science:
- Electrical Engineering
- Computational Science
- Materials Science
Background:
- Smartphone battery life varies significantly due to complex electrochemical, workload, and environmental interactions.
- Accurate prediction of battery state of charge (SOC) and time-to-empty (TTE) is crucial for user experience and device management.
Purpose of the Study:
- To develop a physically interpretable, continuous-time electro-thermal model for predicting smartphone battery SOC and TTE.
- To integrate major power-consuming subsystems and temperature dependence into the battery model.
- To validate the model using the open-source PinePhone platform and publicly available data.
Main Methods:
- A second-order Thevenin equivalent circuit model with two RC branches forms the core.
- A modular total power formulation integrates subsystems like display, processor, and radio-frequency communication.
- An Arrhenius-type internal resistance model and lumped thermal dynamics account for temperature effects.
Main Results:
- Simulations under six usage conditions show TTE ranging from 1.08 hours (heavy load) to over 28.42 hours (light load).
- Model accuracy shows less than 3% error against official specifications and within 10% of reference studies.
- CPU utilization and ambient temperature are identified as key factors for rapid battery depletion.
Conclusions:
- The developed model offers transparent prediction of smartphone battery endurance.
- Hierarchical optimization strategies, including workload and display/network adjustments, can significantly extend battery life under heavy usage.
- The model provides a foundation for designing effective energy-saving strategies in smartphones.
