智能手机电池动态的随机混合自动机：电热耦合与首次通过时间到空估计
A Stochastic Hybrid Automaton for Smartphone Battery Dynamics: Electro-Thermal Coupling and First-Passage Time-to-Empty Estimation
AI总结：
本文提出一种随机混合自动机模型，结合等效电路模型与热模型，用于预测智能手机电池剩余时间，考虑电热耦合和用户行为影响，通过蒙特卡洛模拟量化电池提前终止风险。
摘要：
智能手机时间到空（TTE）预测困难，因为关机不仅由剩余电量决定，还受温度、老化和负载依赖的电压骤降影响。本文开发了一种随机混合自动机，将一阶Thevenin等效电路模型与集中热模型及随机用户行为过程耦合。连续状态包括电量、极化电压和电池温度；用户行为表示为在空闲、社交/网页、视频、游戏和弱信号模式间切换的分段确定性马尔可夫过程。关机定义为终端电压跨越截止阈值或请求功率超过瞬时可行性范围时的首次通过事件。该模型捕捉到简单库仑计数或线性放电模型所忽略的电压崩溃机制：低温或电池老化会增加内部电阻，因此高功率脉冲可使终端电压低于截止点，即使仍有大量电量剩余。蒙特卡洛模拟得到完整的TTE分布而非单一倒计时，允许通过第5百分位数量化低尾风险。敏感性分析识别出环境温度、内部电阻、弱信号无线电惩罚和屏幕亮度是提前关机风险的主要驱动因素。这些结果推动了实际用户指导和操作系统级别的电阻感知节流策略，以限制功率受限区域的峰值功率。该框架提供了一种基于物理、风险意识的方法，用于在现实世界不确定性下解释和扩展智能手机电池的可用寿命。
英文摘要：
Smartphone time-to-empty (TTE) is difficult to predict because shutdown is governed not only by remaining charge, but also by instantaneous power capability under temperature-, aging-, and load-dependent voltage sag. We develop a stochastic hybrid automaton for smartphone battery dynamics that couples a first-order Thevenin equivalent-circuit model with a lumped thermal model and a stochastic user-activity process. The continuous state includes state of charge, polarization voltage, and battery temperature; user behavior is represented as a piecewise deterministic Markov process switching among idle, social/web, video, gaming, and weak-signal modes. Shutdown is formulated as a first-passage event when terminal voltage crosses a cutoff threshold or when requested power exceeds the instantaneous feasibility envelope. The model captures a voltage-collapse mechanism that simple Coulomb-counting or linear discharge models miss: cold temperature or battery aging increases internal resistance, so high-power bursts can drive terminal voltage below cutoff even when substantial charge remains. Monte Carlo simulation yields a full TTE distribution rather than a single countdown, allowing lower-tail risk to be quantified by the 5th percentile. Sensitivity analysis identifies ambient temperature, internal resistance, weak-signal radio penalty, and screen brightness as major drivers of premature shutdown risk. These results motivate practical user guidance and an operating-system-level resistance-aware throttling policy that limits peak power in the power-limited regime. The framework provides a physically grounded, risk-aware approach for explaining and extending usable smartphone battery life under real-world uncertainty.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
