Resumen
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.