Academic Journal of Computing & Information Science, 2026, 9(7); doi: 10.25236/AJCIS.2026.090710.
Xi Cao1, Weihao Dong1, Lihao Zhang1
1Brunel College, North China University of Technology, Beijing, 100144, China
Accurate smartphone time-to-empty (TTE) prediction cannot be obtained by extrapolating state of charge (SOC) alone. Near low SOC, under high load, at low temperature, or after battery aging, terminal-voltage sag and increased internal resistance may make demanded power infeasible before stored charge is depleted. This paper presents a compact protection-aware electro-thermal framework for TTE prediction from smartphone usage logs. The framework follows one engineering chain: usage logs are reconstructed as a continuous demanded-power trajectory, demanded power is closed through an electro-thermal battery model, the deliverability margin drives runtime throttling and undervoltage protection, and TTE is computed as the first infeasible operating time. The model addresses two research questions: how usage logs can be transformed into Pdem(t), and how deliverability constraints and operating-system protection improve realistic TTE prediction. The formulation combines interpretable demand reconstruction, OCV/internal-resistance power closure, SOC and thermal dynamics, aging-dependent capacity and resistance, soft power clipping, and UVLO-based shutdown. It explains non-zero SOC shutdown and provides a physics-constrained basis for evaluating runtime under different loads, temperatures, and aging states.
time-to-empty prediction; electro-thermal battery model; deliverability limit; protection-aware modeling; voltage sag; runtime throttling; non-zero SOC shutdown; battery feasibility
Xi Cao, Weihao Dong, Lihao Zhang. Protection-Aware Electro-Thermal Modeling for Smartphone Battery Time-to-Empty Prediction. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 7: 75-83. https://doi.org/10.25236/AJCIS.2026.090710.
[1] Bernardi, D., Pawlikowski, E. and Newman, J. (1985) A General Energy Balance for Battery Systems. Journal of The Electrochemical Society, 132, 5-12.
[2] Plett, G.L. (2004). Extended Kalman Filtering for Battery Management Systems of LiPB-Based HEV Battery Packs. Part 1. Background. Journal of Power Sources, 134, 252-261.
[3] Plett, G.L. (2004). Extended Kalman Filtering for Battery Management Systems of LiPB-Based HEV Battery Packs. Part 2. Modeling and Identification. Journal of Power Sources, 134, 262-276.
[4] Plett, G.L. (2004). Extended Kalman Filtering for Battery Management Systems of LiPB-Based HEV Battery Packs. Part 3. State and Parameter Estimation. Journal of Power Sources, 134, 277-292.
[5] Hu, X., Li, S. and Peng, H. (2012) A Comparative Study of Equivalent Circuit Models for Li-Ion Batteries. Journal of Power Sources, 198, 359-367.
[6] Forgez, C., Vinh Do, D., Friedrich, G., Morcrette, M. and Delacourt, C. (2010) Thermal Modeling of a Cylindrical LiFePO4/Graphite Lithium-Ion Battery. Journal of Power Sources, 195, 2961-2968.
[7] Lin, X., Perez, H.E., Mohan, S., Siegel, J.B., Stefanopoulou, A.G., Ding, Y. and Castanier, M.P. (2014) A Lumped-Parameter Electro-Thermal Model for Cylindrical Batteries. Journal of Power Sources, 257, 1-11.
[8] Ecker, M., Nieto, N., Kaebitz, S., Schmalstieg, J., Blanke, H., Warnecke, A. and Sauer, D.U. (2014) Calendar and Cycle Life Study of Li(NiMnCo)O2-Based 18650 Lithium-Ion Batteries. Journal of Power Sources, 248, 839-851.
[9] Schmalstieg, J., Kaebitz, S., Ecker, M. and Sauer, D.U. (2014) A Holistic Aging Model for Li(NiMnCo)O2-Based 18650 Lithium-Ion Batteries. Journal of Power Sources, 257, 325-334.
[10] Lu, L., Han, X., Li, J., Hua, J. and Ouyang, M. (2013) A Review on the Key Issues for Lithium-Ion Battery Management in Electric Vehicles. Journal of Power Sources, 226, 272-288.
[11] Waag, W., Fleischer, C. and Sauer, D.U. (2014) Critical Review of the Methods for Monitoring of Lithium-Ion Batteries in Electric and Hybrid Vehicles. Journal of Power Sources, 258, 321-339.
[12] Berecibar, M., Gandiaga, I., Villarreal, I., Omar, N., Van Mierlo, J. and Van den Bossche, P. (2016) Critical Review of State of Health Estimation Methods of Li-Ion Batteries for Real Applications. Renewable and Sustainable Energy Reviews, 56, 572-587.
[13] Farmann, A. and Sauer, D.U. (2016) A Comprehensive Review of On-Board State-of-Available-Power Prediction Techniques for Lithium-Ion Batteries in Electric Vehicles. Journal of Power Sources, 329, 123-137.
[14] Zou, Y., Hu, X., Ma, H. and Li, S.E. (2015) Combined State of Charge and State of Health Estimation over Lithium-Ion Battery Cell Cycle Lifespan for Electric Vehicles. Journal of Power Sources, 273, 793-803.
[15] Jaguemont, J., Boulon, L. and Dube, Y. (2016) A Comprehensive Review of Lithium-Ion Batteries Used in Hybrid and Electric Vehicles at Cold Temperatures. Applied Energy, 164, 99-114.