基于稀疏采样滑动窗口的RUL精确预测

Changhoon Song, Sukhan Lee
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引用次数: 0

摘要

准确预测电池的剩余使用寿命(RUL),在电池寿命结束(EOL)之前足够长的时间,对于其许多应用的安全和维护至关重要。问题是,随着预测期限或周期的增加,这种对RUL的长期预测在预测电池健康状态(SOH)方面的不确定性越来越大。传统的方法还不能有效地解决这个问题。提出了一种基于滑动窗口和稀疏采样相结合的电池RUL长期准确预测方法。所提出的方法通过定义有限周期的窗口来获得精度,在该窗口中单次预测soh,并将窗口进一步滑动到未来,以连续预测后续周期窗口的soh。此外,稀疏采样将被纳入到预测窗口的最佳大小的选择中,以使长期预测的准确性最大化。采用堆叠lstm网络对SOH周期窗口进行预测。实验基于先进生命周期工程中心(CALCE)数据集进行。结果表明,该方法在长期RUL预测精度方面优于传统方法。
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Accurate RUL Prediction Based on Sliding Window with Sparse Sampling
Accurate prediction of the Remaining-Useful-Life (RUL) of a battery, sufficiently long in advance from the End-of-Life (EOL), is essential for the safety and maintenance of its many applications. The problem is that such a long-term prediction of RUL suffers from the growing uncertainty in predicting the State-of-Health (SOH) of a battery as the terms or cycles of prediction increases. Conventional approaches are yet to address an effective solution to this problem. This paper presents an approach to the accurate long-term prediction of battery RUL based on the sliding window combined with sparse sampling. The proposed approach attains the accuracy by defining the window of a limited number of cycles at which SOHs are predicted single-shot and sliding the window further into the future to consecutively predict SOHs for the subsequent window of cycles. Furthermore, sparse sampling is to be incorporated into the selection of an optimal size of prediction window in such a way as to maximize the accuracy involved in a long-term prediction. A Stacked-LSTM network is adopted to carry out the prediction of a window of SOH cycles. Experiments are conducted based on the Center for Advanced Life Cycle Engineering (CALCE) dataset. The result verifies that the proposed approach tops the conventional approaches in terms of the accuracy of long-term RUL prediction.
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