# Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks
## 6 Conclusions  
In this paper, we proposed an SOC estimation method for each cell of a lithium battery pack using neural networks. Experiments were conducted at room temperature and high temperature. Due to the measurement accuracy of the data, there was a difference in the results for each cell. The proposed method was used to estimate the SOC of the lithium battery pack in real time. Moreover, the SOC of the lithium battery pack was estimated using the MNN and LSTM methods, using two types of datasets, the voltage dataset only and the voltage and temperature dataset together. After that, the results with the MNN and LSTM methods were compared and analyzed.  
When using two, six, and eight inputs with only voltage data, a low overall error rate was recorded when using 6 voltage data as input values for the MNN model, and six and eight voltage data for the LSTM method. With the LSTM method, a similar number of low error rates were recorded for the six and eight input values. However, the use of eight input values had the disadvantage of increasing the learning time due to the increase in the amount of data, so we conclude that using six input values is more efficient. The SOC estimation results of the lithium battery pack were also obtained with input value using only six voltage data, and six voltage data and one temperature variable data. For the MNN model, SOC estimates, using voltage data and temperature variable data, tended to show lower error rates, and lower error rates for all cells were also seen when using the LSTM method, except for the estimated SOC error rate of cell three under room temperature conditions. The comparison of SOC estimates with the MNN and LSTM methods showed that the input value was based on the input parameters of 6 voltage data and temperature variable data combined to estimate the SOC, and that each cell for the battery pack had a higher error rate when it was estimated using the LSTM method. Therefore, we can confirm that SOC estimation using the LSTM method is better than with the MNN method. In conclusion, it is confirmed that the LSTM method using six voltage as the input value and temperature variable data had the lowest error rate. In addition, the real-time SOC of lithium battery packs was estimated using LSTM method, which used six voltage data and temperature data, which had the lowest error rate. By comparing the error rates obtained from similar cases, an average error rate of about 1.5% was derived in this paper. Therefore, it can be confirmed that the proposed method performs very well.  
In future studies, we will collect more battery data, which can be used to conduct the simulation and real-time estimation of the SOC. We plan to reduce the error rate by studying neural network methods other than the MNN and LSTM method used in this paper.  
**Author Contributions:** Methodology, J.P. and I.L.; Investigation, J.P. J.L., S.K. and I.L.; Experiments, J.P., J.L. and S.K.; Simulations, J.P.; Data analysis, J.P., J.L. and I.L.; Writing-original draft, J.P.; Writing-review & editing, I.S.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported by the BK21 Plus project funded by the Ministry of Education, Korea (21A20131600011) This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2020R11A3A04036615).  
**Conflicts of Interest:** The authors declare no conflict of interest.