Battery State Prediction for Mobile Devices Based on Coupled CETM and ALE-SOH Models
DOI:
https://doi.org/10.54097/885mcy58Keywords:
Coupled Electro-Thermal Model, ALE-SOH Model, Battery Aging ModelingAbstract
This paper proposes a two-layer coupled model for mobile device battery capacity prediction, integrating the CETM electrothermal model with the ALE-SOH aging model to achieve a coordinated estimation of SOC, temperature, and battery degradation status. The inner layer uses state-space estimation to track real-time load changes, while the outer layer adjusts capacity decay and internal resistance growth based on cycle count and temperature, thereby balancing short-term prediction with long-term degradation characterization. Validated using data from the Samsung Galaxy S8, the model achieved an SOC prediction MAE of 4.231%, an RMSE of 5.673%, and an R² of 0.952, with an average absolute error of 0.592°C for temperature. The results indicate that this model is applicable to various scenarios, including web browsing, navigation, 5G data transmission, and gaming, demonstrating good prediction accuracy, stability, and practical value for widespread application.
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[1] Wu, C. L., Fu, J. C., Xu, X. F., et al. (2024). SOC estimation of lithium ion batteries based on multi input least squares and multi input extended Kalman filter algorithms. Journal of South China University of Technology (Natural Science Edition), 52(2), 74 83.
[2] Qing, C. Y., Chen, S. H., Li, R. P., et al. (2024). A study on joint SOC SOH estimation of lithium ion batteries based on FFRLS DEKF. Information Technology and Informatization, (3), 8 12.
[3] Zhao, J. Y., Hu, J., Zhang, X. H., et al. (2023). Joint SOC SOH estimation based on lithium ion battery models and fractional order theory. Journal of Electrical Engineering, 38(17), 4551 4563. https://doi.org/10.19595/j.cnki.1000 6753.tces.221092
[4] Lun, H. X., & Zhang, Z. (2025). A study on short term electricity load forecasting models based on VMD PSO LSTM. Computer Programming Techniques and Maintenance, (9), 43 45. https://doi.org/10.16184/j.cnki.comprg.2025.09.044
[5] Xu, L. Y., Ma, K., Yang, Q. X., et al. (2024). SOC estimation of power batteries based on Kalman filtering. Journal of Jiangsu University (Natural Science Edition), 45(1), 24 29.
[6] Liu, X., Li, L., Cao, J., et al. (2021). Cooperative online prediction of SOC and SOH for lithium batteries based on a joint algorithm. Journal of Terahertz Science and Electronic Information, 19(4), 739 746.
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Copyright (c) 2026 International Journal of Advanced Engineering and Technology Research
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