## 3 LIB SOH Prediction Case Study and Analysis
### Analysis of Prediction Results  
To validate the feasibility of the RF model in LIB SOH prediction, the DT model, CNN model, LSTM model, and Class RF model were employed to predict the SOH of three different LIBs. The comparative results of the prediction are shown in Figure 8. From Figure 8, it can be observed that among the aforementioned traditional machine learning models, the SOH predictions of the Class RF model are consistently the closest to the true values for the three different LIB experimental datasets. To validate the effectiveness of the RF improved model proposed in this paper for LIB SOH prediction, RF improved model 1 and RF improved model 2 were further employed to predict the SOH of three different LIBs. These predictions were compared with the Class RF model. The comparative results of the different RF models are shown in Figure 9. From Figure 9, it can be observed that among the different RF models, the RF improved models with optimized parameters provide SOH predictions that are closer to the true values compared to the Class RF model. Additionally, RF improved model 2, which optimizes five parameters, consistently provides the prediction closest to the true values. To further analyze the relationship between the prediction and true values of different models, a simple linear analysis was conducted on the prediction of the six models mentioned above, as shown in Table 7.  
Figure 8: Comparison chart of SOH prediction for the four traditional machine learning models. (a) #1 LIB. (b) #6 LIB. (c) #26 LIB. Regarding the linear analysis of prediction results, when the slope of the simple linear equation is closer to 1 and the intercept is closer to 0, it indicates higher prediction accuracy. Therefore, Table 7 reveals that among the three different LIB SOH prediction results, the Class RF model consistently exhibits higher prediction accuracy than the other three traditional machine learning models. Among the different RF models, RF improved model \begin{table}
\begin{tabular}{c c c c} \hline \hline
**LIB** & **Ranking** & **Model** & **Simple Linear Equation** \\ \hline \multirow{8}{*}{\#1} & 1 & RF improved model 2 & y = 0.994x + 0.373 \\  & 2 & RF improved model 1 & y = 0.987x + 0.947 \\  & 3 & Class RF model & y = 1.021x \(-\) 1.246 \\  & 4 & LSTM model & y = 1.029x \(-\) 1.860 \\  & 5 & CNN model & y = 1.036x \(-\) 2.595 \\  & 6 & DT model & y = 0.956x + 4.098 \\  & 1 & RF improved model 2 & y = 0.995x \(-\) 0.407 \\  & 2 & RF improved model 1 & y = 0.995x \(+\) 1.454 \\  & 3 & Class RF model & y = 1.015x \(-\) 2.532 \\  & 4 & LSTM model & y = 1.062x \(-\) 4.144 \\  & 5 & DT model & y = 1.089x \(-\) 6.526 \\  & 6 & CNN model & y = 1.100x \(-\) 9.996 \\  & 1 & RF improved model 2 & y = 1.009x \(-\) 0.683 \\  & 2 & RF improved model 1 & y = 0.980x + 2.495 \\  & 3 & Class RF model & y = 0.977x \(+\) 2.832 \\  & 4 & CNN model & y = 0.948x \(+\) 3.863 \\  & 5 & LSTM model & y = 1.054x \(-\) 4.135 \\  & 6 & DT model & y = 1.067x \(-\) 5.104 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Simple linear analysis of LIB SOH prediction from different models. Figure 9: Comparison chart of SOH prediction for different RF models. (**a**) #1 LIB. (**b**) #6 LIB. (**c**) #26 LIB. 2 consistently exhibits the highest prediction accuracy. To further analyze the evaluation index of the model, the evaluation index for the six prediction models were put into the following Table 8.  
Since smaller _RMSE_ and _MAPE_ values indicate smaller model errors, and a larger \(R^{2}\) value signifies a higher degree of model fit, Table 8 reveals that different LIB charge-discharge cycle experimental data yield varying values of evaluation index, leading to different ranking of model prediction results. However, among the four traditional machine learning models, namely the DT model, CNN model, LSTM model, and Class RF model, the Class RF model consistently exhibits better evaluation index. Furthermore, the evaluation index of the two optimized RF improved models are superior to the Class RF model.