Abstract
To address inaccurate prediction in remaining useful life (RUL) in current Lithium-ion batteries, this paper develops a Long Short-Term Memory Network, Sliding Time Window (LSTM-STW) and Gaussian or Sine function, Levenberg-Marquardt algorithm (GS-LM) fusion batteries RUL prediction method based on ensemble empirical mode decomposition (EEMD). Firstly, EEMD is used to decompose the original data into high-frequency and low-frequency components. Secondly, LSTM-STW and GS-LM are used to predict the high-frequency and low-frequency components, respectively. Finally, the LSTM-STW and GS-LM prediction results are effectively integrated in order to obtain the final prediction of the lithium-ion battery RUL results. This article takes the lithium-ion battery data published by NASA as input. The experimental results show that the method has higher accuracy, including the phenomenon of sudden capacity increase, and is less affected by the prediction starting point. The performance of the proposed method is better than other typical battery RUL prediction methods.
Highlights
Electric energy, as a clean secondary energy, is used in all aspects of our life
The fusion methods effectively improve the accuracy of remaining useful life (RUL) prediction, but increase the complexity of the model
This paper develops a multi-scale fusion prediction method based on ensemble empirical mode decomposition (EEMD) and the long short-term memory (LSTM)-STW-GS-LM mixture model
Summary
As a clean secondary energy, is used in all aspects of our life. Lithium-ion batteries, as a device for storing electrical energy, have been widely used in transportation, aerospace and military defense applications due to their high energy density, low self-discharge rate, recyclability, high safety, and high voltage [1,2,3]. Data-driven lithium-ion battery RUL prediction methods have the advantages of a simple process, small amount of calculations, and there being no need to consider the complicated internal mechanism of the battery. Pang et al [18] proposed a novel method fusing the wavelet decomposition technology (WDT) and the Nonlinear Auto Regressive neural network (NARNN) model to predict the lithium-ion battery RUL. The main innovations of this paper are as follows: (1) Decompose the battery raw data by EEMD method, and use the appropriate LSTM-STW and GS-LM model to separately train and predict through the sequence characteristics after decomposition to avoid the complexity of hybrid neural network under ensuring the prediction accuracy and improve the prediction efficiency.
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