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An analytical model for predicting the remaining battery capacity of lithium-ion batteries

机译:预测锂离子电池剩余电量的分析模型

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Predicting the residual energy of the battery source that powers a portable electronic device is imperative in designing and applying an effective dynamic power management policy for the device. This paper starts up by showing that a 30% error in predicting the battery capacity of a lithium-ion battery can result in up to 20% performance degradation for a dynamic voltage and frequency scaling algorithm. Next, this paper presents a closed form analytical expression for predicting the remaining capacity of a lithium-ion battery. The proposed high-level model, which relies on online current and voltage measurements, correctly accounts for the temperature and cycle aging effects. The accuracy of the high-level model is validated by comparing it with DUALFOIL simulation results, demonstrating a maximum of 5% error between simulated and predicted data.
机译:在为设备设计和应用有效的动态电源管理策略时,必须预测为便携式电子设备供电的电池电源的剩余能量。本文首先表明,对于动态电压和频率缩放算法,预测锂离子电池的电池容量时出现30%的误差会导致性能下降多达20%。接下来,本文提出了一种封闭形式的解析表达式,用于预测锂离子电池的剩余容量。所提出的高级模型依赖于在线电流和电压测量值,可以正确考虑温度和循环老化的影响。通过将其与DUALFOIL仿真结果进行比较来验证高级模型的准确性,这表明仿真数据与预测数据之间的最大误差为5%。

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