NDLI: SOH Estimation of Li-Ion Battery Using Discrete Wavelet Transform and Long Short-Term Memory Neural Network

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SOH Estimation of Li-Ion Battery Using Discrete Wavelet Transform and Long Short-Term Memory Neural Network

Content Provider	MDPI
Author	Park, Min-Sick Lee, Jong-Kyu Kim, Byeong-Woo
Copyright Year	2022
Description	The state of health (SOH) of a lithium-ion battery (LIB) should be accurately estimated to ensure its safe operation under various driving conditions for electric vehicles. To estimate the SOH of LIBs, it is necessary to develop a technique that can properly process data with nonlinear characteristics related to the voltage and temperature of various electrochemical reactions in the batteries. To this end, we adopted wavelet transform methods to facilitate feature extraction for the preprocessing of nonlinear characteristic data from LIBs. Convolutional neural network (CNN) and long short-term memory (LSTM) techniques were used for the lithium-ion SOH estimation using the wavelet transform method. SOH estimation models using either the conventional data preprocessing technology normalization or wavelet transform method were compared. The SOH estimation accuracy of the model in which the wavelet transform was applied to the LSTM learning technology was 98.92%, and it was confirmed that the performance was improved compared with that of the normalization technology. In summary, this paper proposes a method that can improve the SOH estimation accuracy of LIBs compared to conventional methods by using data with nonlinear characteristics related to intrinsic changes in the voltage and temperature of LIBs.
Starting Page	3996
e-ISSN	20763417
DOI	10.3390/app12083996
Journal	Applied Sciences
Issue Number	8
Volume Number	12
Language	English
Publisher	MDPI
Publisher Date	2022-04-14
Access Restriction	Open
Subject Keyword	Applied Sciences Industrial Engineering Li-ion Battery Soh Data-driven Methods Wavelet Transform Lstm Cnn
Content Type	Text
Resource Type	Article

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