Battery Remaining Useful Life Prediction Based on a Combination of ARMA and Degradation Model

R. Jiao, X. Ma, L. Li, J. Xiao
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Abstract

The remaining useful life (RUL) of batteries is an important and helpful reference for battery management system. Since autoregressive moving average (ARMA) model is a relatively mature time series analysis method for prognostics, the long-term prediction results are not reliable due to dynamic noise and constantly cumulative system errors. In order to improve the accuracy of long-term prediction for battery RUL, a method combining ARMA and exponential degradation model is proposed in this paper. A case study using battery dataset from CALCE is performed to demonstrate the effectiveness of the proposed method, and the results show that the proposed method gives better prediction accuracy.
基于ARMA和退化模型的电池剩余使用寿命预测
电池剩余使用寿命(RUL)是电池管理系统的重要参考。由于自回归移动平均(ARMA)模型是一种相对成熟的预测时间序列分析方法,由于动态噪声和不断累积的系统误差,长期预测结果不可靠。为了提高电池RUL的长期预测精度,本文提出了一种将ARMA与指数退化模型相结合的方法。以CALCE电池数据为例,验证了该方法的有效性,结果表明该方法具有较好的预测精度。
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