Abstract
Prediction of Remaining Useful Life (RUL) of lithium-ion batteries plays a significant role in battery health management. Battery capacity is often chosen as the Health Indicator (HI) in research on lithium-ion battery RUL prediction. In the rest time of batteries, capacity will produce a certain degree of regeneration phenomenon, which exists in the use of each battery. Therefore, considering the capacity regeneration phenomenon in RUL prediction of lithium-ion batteries is helpful to improve the prediction performance of the model. In this paper, a novel method fusing the wavelet decomposition technology (WDT) and the Nonlinear Auto Regressive neural network (NARNN) model for predicting the RUL of a lithium-ion battery is proposed. Firstly, the multi-scale WDT is used to separate the global degradation and local regeneration of a battery capacity series. Then, the RUL prediction framework based on the NARNN model is constructed for the extracted global degradation and local regeneration. Finally, the two parts of the prediction results are combined to obtain the final RUL prediction result. Experiments show that the proposed method can not only effectively capture the capacity regeneration phenomenon, but also has high prediction accuracy and is less affected by different prediction starting points.
Highlights
Lithium-ion batteries are considered to be the best energy storage devices for many applications because of their light weight, high energy density, and long life [1,2,3]
Driven by the desire to capture capacity regeneration effectively and improve prediction accuracy, a novel wavelet decomposition technology (WDT)–Nonlinear Auto Regressive neural network (NARNN) method for lithium-ion battery Remaining Useful Life (RUL) prediction is proposed based on a combination of WDT and NARNN
I=T are reconstructed from 1 to l levels by Equation (6) to obtain the fusing predicted series corresponding to capacity series, and RUL value can be calculated by Equation (8); (6) Evaluate the prediction results: The evaluation is given with original testing data and prediction results through some criteria to evaluate the performance of the integrated method WDT–NARNN
Summary
Lithium-ion batteries are considered to be the best energy storage devices for many applications because of their light weight, high energy density, and long life [1,2,3]. In order to predict RUL better, it is necessary to establish an appropriate model that can effectively deal with capacity degradation data with the characteristics of nonlinear time series. Driven by the desire to capture capacity regeneration effectively and improve prediction accuracy, a novel WDT–NARNN method for lithium-ion battery RUL prediction is proposed based on a combination of WDT and NARNN. The main contributions and innovations of this paper include the following: (1) The global degradation and local regeneration in battery capacity time series can be separated effectively by WDT, which will be helpful to improve the prediction performance of the prediction model; (2) A combined model based on WDT and NARNN is established to model the local and global tendency of the battery capacity changes, which enables the prediction model to capture the actual capacity decay tendency of batteries effectively
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