## 5 Conclusions  
In this study, to improve the SOH estimation accuracy of a LIB, we introduced wavelet (CWT and DWT)-based preprocessing methods, which overcame the limitations of conventional studies when processing nonlinear characteristic data, and proposed a learning optimization method based on these. To confirm the usefulness of the proposed algorithm, we compared the SOH estimation results of the LIB using the wavelet technique and Normalization, which are existing data preprocessing techniques.  
When the temperature data were used, which is a variable related to the SOH estimation of a LIB, the SOH accuracy was 93.49% for the technique using normalization and LSTM, 94.38% for the technique using CWT and CNN, and 96.45% for the technique using DWT and LSTM. When the relative errors were compared, the relative error variance of the DWT and LSTM was relatively narrow and the maximum value relatively low. Therefore, the accuracy was excellent when the wavelet was used compared to the existing data  
Figure 10: Relative error boxplot for each SOH estimation.  
preprocessing technique (normalization). When CNN and LSTM were compared, the estimation accuracy of the LSTM was excellent.  
When voltage data were used, the SOH accuracy was 94.85% for the using normalization and LSTM, 97.71% for using CWT and CNN, and 98.92% for the using DWT and LSTM method. When the relative errors were compared, the relative error variances of DWT and LSTM were the narrowest and minimum values low, similar to the temperature data. Therefore, as with the temperature data, it was found that the accuracy was excellent and the estimation accuracy of the LSTM was excellent when the wavelet was used compared with the conventional pretreatment.  
By combing all of the aforementioned results, the SOH estimation accuracy using voltage data was greater than that when temperature data were used, and the SOH estimation accuracy was higher when DWT and LSTM were used. This confirmed the usefulness of the algorithm proposed in this study and it was demonstrated that the voltage is the most important factor of the LIB SOH accuracy estimation related variables. Therefore, it was confirmed that our proposed wavelet-based data processing and LSTM training methods, which showed excellent characteristics reflecting the time-series data, were useful in improving the SOH estimation accuracy.  
In the future, we aim to establish a method for further improving the SOH estimation accuracy by adding the effects of data for characteristics other than the voltage and temperature of a LIB that was the focus of this study.  
Conceptualization, J.-L.; formal analysis, J.-k.L.; methodology, M.-S.P. and J.-k.L.; project administration, B.-W.K.; software, J.-k.L.; supervision, B.-W.K.; validation, M.-S.P.; visualization, M.-S.P.; writing--original draft, M.-S.P.; writing--review & editing, B.-W.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Industrial Strategic Technology Development Program (20010132, \"Development of the systematization technology of e-powertrain core parts development platform for expending the industry of xEV parts\") and the Korea Institute for Advancement of Technology (KIAT) (P0018434, \"Building an open platform ecosystem for future technology innovation in the automotive industry\") funded by the Ministry of Trade, Industry & Energy (MOITE, Korea).  
The authors declare no conflict of interest.