# Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Health Features
## 5 Verify the Feasibility of the Model and Analyze the Relevant Results  
### Description of Results Related to a Single Dataset  
After completing the above preparations, the processed dataset is input into the CNN-LSTM-Attention-FVIM prediction model. In this part, we use the battery aging data of B0005, B0007, and B0018 for prediction, and we train 60% and 50% of the battery data before predicting the battery life to achieve multi-faceted evaluation of the model. First, Figure 6a-c represent the prediction graphs of 50% of the training data of the three batteries, B0005, B0007, and B0018, Figure 7a-c represent the prediction graphs of 60% of the training data of the three batteries, Figure 6d-f represent the error graphs of 50% of the training data of the three batteries, and Figure 7d-f represent the error graphs of 60% of the training data of the three batteries. First, it can be seen from the prediction graphs for 50% and 60% of the training data that the model has good prediction ability and can accurately fit the capacity decay trajectory of the B0005, B0007, and B0018 batteries. Regardless of the number of training samples, the prediction curve is still highly consistent with the true value. The CNN-LSTM-Attention-FVIM regression prediction model can effectively capture the battery aging characteristics and stabilize the prediction accuracy within a certain range through adaptive functions and dynamic adjustment of key parameters. In addition, to show the size of the prediction error of this model more intuitively, compass charts are used to characterize other performance indicators such as MAE and RMSE. As shown in Figures 6d-f and 7d-f, the test set axes of the two graphs of RMSE and MAE are close to the center, forming a small circle. In addition, the axis lengths of MAE and RMSE of a single battery are close, indicating that the error distribution is uniform and there is no large outlier error. At the same time, it can be clearly seen from the data shown in the figure that MAE and RMSE are around 1%. The error is stable without obvious fluctuations. At the same time, in order to more intuitively show the model's ability to fit the data, we selected the B0018 battery with relatively large error and showed its data fitting graph and relative error graph, as shown in Figure 8a,b. From the two figures, it can be clearly seen that most of the data are near the baseline, and the relative error distribution is concentrated, mostly distributed around 0.01 to \(-\)0.01, indicating that the model is robust. At the same time, from the data in Table 3, it can be intuitively seen that the MAE and RMSE of the proposed model are significantly better than other models. Among them, the range of MAE of this model is stable at about 0.7%, and the range of RMSE is stable at around 1%. Compared with other models, the CNN-LSTM-Attention-FVIM prediction model has good adaptability and robustness for the three groups of batteries, which also proves that the model proposed in this paper has good estimation ability in the face of SOH changes. From Table 4, it can be seen that when the proportion of the training set increases to 60%, the MAE and RMSE of the three groups of batteries are still small, and the prediction accuracy is still high, proving that the model has excellent degradation feature learning ability. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Battery** & **MAE** & **RMSE** \\ \hline B0005 & 0.0063 & 0.0078 \\ B0007 & 0.0075 & 0.0091 \\ B0018 & 0.0071 & 0.0109 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of various evaluation indicators when the training data is 50%. These experimental results fully verify the outstanding performance and stability of the CNN-LSTM-Attention-FVIM model in SOH prediction.