Framework predicts smartphone and battery discharge times accurately
A Framework for Discharge Time Prediction of Energy Storage Units Based on Coupled Dynamics and Multi-Factor Aging Models
Computational Engineering, Finance, and Science
Summary
Batteries in portable devices like smartphones lose charge over time, but predicting exactly when they will run out can be difficult. The authors created a framework that uses phone usage data, battery chemistry, temperature, and aging effects to better estimate how long a battery will last before it needs recharging. They tested this on real smartphone and lab battery data and found their predictions were quite close to actual battery behavior. This approach helps understand battery life in a way that connects physical processes with everyday usage.
What this means in practice
- •For mobile app developers: Estimate battery life more accurately for apps by modeling power use based on device telemetry and battery aging.
- •For embedded system engineers: Design embedded systems with improved runtime predictions under varying temperatures and usage by integrating coupled battery and load models.
Authors
Jiaye Yang, Hansheng Su, Wangzi Zhu
Abstract
This paper presents a physically interpretable framework for predicting time to empty (TTE) in portable embedded systems. The framework couples usage-driven load-power decomposition, electrical power-voltage-current closure, a semi-empirical aging model, and SOC-temperature dynamics. Smartphone telemetry is mapped to battery current through an interpretable load model and conversion-efficiency correction. Battery capacity loss is modeled by combining Arrhenius temperature dependence, SEI diffusion behavior, and cycle-related power-law degradation. The coupled dynamic model then predicts TTE under different initial SOC values, ambient temperatures, and usage profiles. Chronological hold-out evaluation on a 6.9-h smartphone discharge session yielded a current RMSE of 0.0095 $\pm$ 0.0006 A, a temperature RMSE of 2.93 $\pm$ 0.24$^\circ$C, and a TTE MAPE of 4.81 $\pm$ 0.61%. Evaluation on NASA cell B0005 produced a capacity-loss RMSE of 0.031 Ah. Baseline, ablation, and counterfactual analyses further illustrate the contributions of thermal and aging corrections and the relative influence of load features. The results demonstrate the feasibility and interpretability of the proposed framework, while broader validation across devices and batteries remains necessary.