Smartphone Battery Life Prediction and Sensitivity Analysis Integrating Electrothermal Coupling Dynamics and Stochastic Processes
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Abstract
Addressing the highly nonlinear and stochastic nature of smartphone battery depletion, this study establishes a comprehensive dynamic modeling and prediction framework. First, by incorporating the modified Nernst equation and Kirchhoff’s voltage law, a fundamental model of nonlinear discharge is established. Building upon this, thermodynamic equilibrium equations are coupled to quantify the feedback mechanisms among Joule heating, internal resistance, and temperature. To replicate real-world complex usage environments, the study innovatively introduced the “Tail Effect” to characterize the high-power-consumption idle state of network modules following data transmission, and utilized Gaussian white noise and Brownian motion to model user interaction behavior as a stochastic process. Experimental results demonstrate that the model achieves extremely high prediction accuracy in typical scenarios such as gaming and social media, with a coefficient of variation consistently below 6% in low-load scenarios. Furthermore, the study thoroughly examined the impact of environmental factors, finding that extreme temperatures significantly accelerate battery capacity degradation; the cycle life at 65 ◦C is only about one-fifth of that at 35 ◦C. Through multidimensional sensitivity analysis, the study further identified processor load as the primary energy-consuming factor during high-intensity tasks, while communication traces are a significant source of energy loss in sporadic interaction scenarios. This study provides scientific and quantitative support for energy management and battery life prediction in mobile devices.
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