SOH Degradation Estimation of Thin Flexible Li-Ion Power Sources Subjected to Accelerated Life Cycling With Randomized Charge-Discharge and C-Rates

P. Lall, Ved Soni
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引用次数: 1

Abstract

The growing need for wearable devices, fitness accessories, and biomedical equipment has led to the upsurge in research and development of thin, flexible battery research and development. Studying degradation of such power sources used in consumer electronics devices is essential from multiple perspectives, as it allows the manufacturer to determine device warranty, affects the user purchasing decision, and can also be used to inform the user of their device’s battery health and remaining useful life in real-time. In order to achieve these goals via empirical methods, batteries are generally subjected to accelerated life cycling tests with various operating conditions, and their degradation data gathered is then used to model their SOH degradation. However, in the real world, the charge-discharge depth and charge-discharge rates for every cycle are hardly constant and vary greatly for the same user over time and for different users who use their devices differently. The real task for such developed battery models is to estimate the SOH of batteries being used in real-world scenarios with such random variations of charge-discharge depth and C-rates. To this end, the current work conducts accelerated life cycling tests of batteries with random variation in these two parameters, individually and simultaneously. Finally, multiple iterations of the SOH estimation models have been presented with different predictor variables to minimize the model validation error.
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随机充放电和c -倍率下加速寿命循环的柔性薄锂离子电源SOH降解评价
对可穿戴设备、健身配件和生物医学设备的需求日益增长,导致了轻薄、柔性电池研发的热潮。从多个角度来看,研究消费电子设备中使用的此类电源的退化是必不可少的,因为它允许制造商确定设备保修,影响用户的购买决策,还可以用来实时通知用户其设备的电池健康状况和剩余使用寿命。为了通过经验方法实现这些目标,通常会对电池进行各种操作条件下的加速寿命循环测试,然后使用收集到的电池降解数据对其SOH降解进行建模。然而,在现实世界中,每个周期的充放电深度和充放电速率几乎是恒定的,并且随着时间的推移,对于同一用户和使用不同设备的不同用户,充放电深度和充放电速率变化很大。对于这种已开发的电池模型,真正的任务是估计在这种随机变化的充放电深度和c率的现实场景中使用的电池的SOH。为此,本工作分别和同时对这两个参数随机变化的电池进行加速寿命循环试验。最后,利用不同的预测变量对SOH估计模型进行多次迭代,使模型验证误差最小化。
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