# Machine Learning-Based Lithium Battery State of Health Prediction Research
## 5 Conclusions and Outlook  
With the rapid development of the new energy industry, lithium-ion batteries are being increasingly used in electric vehicles and energy storage systems [37]. Health prediction technology plays a crucial role in promoting the efficient use of lithium-ion batteries in these fields and in supporting the transition to sustainable energy. This study utilized battery aging data from the NASA PCoE laboratory (B0005, B0006, and B0007). By analyzing battery temperature, voltage, and current curves, 16 features related to battery capacity degradation were extracted, and 12 highly correlated health indicators were selected. Subsequently, the PSO algorithm was used to optimize the parameters of three models: LSTM, CNN, and SVR. The models' prediction performance was evaluated using RMSE, MAE, MAPE, and R\({}^{2}\). The following conclusions were drawn:  
(1) Effectiveness of the health factor. The health factor extracted by temperature, voltage, and current features can effectively characterize the battery capacity decline, which verifies the accuracy of the method. (2) The PSO strategy can improve the model prediction performance. The PSO algorithm significantly improves the prediction effect of the three models, verifying the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Dataset** & **MAE** & **RMSE** & **MAPE** & **R\({}^{2}\)** \\ \hline Before LSTM & B0005 & 0.012879 & 0.017659 & 1.059591 & 0.814509 \\ optimization & B0006 & 0.018616 & 0.020898 & 1.343736 & 0.805094 \\  & B0007 & 0.011469 & 0.015693 & 0.952507 & 0.829709 \\ \hline After LSTM & B0005 & 0.007413 & 0.010931 & 0.54555 & 0.921261 \\ optimization & B0006 & 0.012182 & 0.015197 & 0.961381 & 0.907357 \\  & B0007 & 0.006739 & 0.009433 & 0.458213 & 0.929801 \\ \hline Before CNN & B0005 & 0.01898 & 0.02042 & 1.74478 & 0.71724 \\ optimization & B0006 & 0.02186 & 0.02314 & 2.12921 & 0.70574 \\  & B0007 & 0.01665 & 0.01871 & 1.647 & 0.72381 \\ \hline After CNN & B0005 & 0.01062 & 0.0159 & 0.93036 & 0.8659 \\ optimization & B0006 & 0.01664 & 0.0187 & 1.45722 & 0.83391 \\  & B0007 & 0.00979 & 0.01317 & 0.89946 & 0.87973 \\ \hline Before SVR & B0005 & 0.01995 & 0.02116 & 1.72267 & 0.71014 \\ optimization & B0006 & 0.02442 & 0.02724 & 1.9643 & 0.69423 \\  & B0007 & 0.0289 & 0.03065 & 2.27799 & 0.68511 \\ \hline After SVR & B0005 & 0.01396 & 0.01508 & 1.04081 & 0.84554 \\ optimization & B0006 & 0.0188 & 0.02032 & 1.83563 & 0.82724 \\  & B0007 & 0.01212 & 0.01408 & 1.56972 & 0.83925 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of prediction errors before and after model optimization. effectiveness of its optimization parameters, especially in improving the model stability and accuracy. (3) The PSO-LSTM model has the best prediction effect. In the SOH prediction of B0005, B0006, and B0007 batteries, the PSO-LSTM model has the best performance in all evaluation indexes, especially in capturing the complex timing data trend and reducing the error, which provides an important reference for the accurate prediction of the SOH of batteries. Due to the limitations of research time and experimental conditions, this study still has some deficiencies. Future research can be carried out in the following aspects:  
(1) The dataset used in this paper is based on a single battery and is experimented on in an ideal environment, while the actual electric vehicle battery exists in the form of a battery pack which is affected by various factors. Therefore, charging and discharging data under actual working conditions can be collected for modeling studies in the future. (2) In this paper, only three models, LSTM, CNN, and SVR, are used, and there may exist machine learning algorithms that are more suitable for SOH prediction that have not been tried. In addition, there are some limitations in optimizing the performance of a single model, and the prediction performance can be improved using model fusion in the future.