# 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
### Comparison and Verification of Different Models  
In order to verify the stability and excellence of this model and to better interpret the characteristics of this model, we used the processed NASA battery data to predict and estimate using other GRU models, BILSTM models, CNN models, and CNN-LSTM-Attention models. These results were then compared and verified with the CNN-LSTM-Attention-FVIM optimization prediction model proposed in this paper. For this comparison and verification, we used B0005, B0006, B0007, and B0018 batteries from NASA. Under the same conditions as the other parameters, we used 70% of the battery data as the training set for each model and the remaining 30% as the test set for estimation and comparison. The SOH prediction comparison chart of each model is shown in Figure 9a-d, and a comparison of the MAE and RMSE indicators for each model is shown in Figure 10. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Battery** & **MAE** & **RMSE** \\ \hline B0005 & 0.0051 & 0.0067 \\ B0007 & 0.0062 & 0.0075 \\ B0018 & 0.0086 & 0.0118 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of various evaluation indicators when the training data is 60%. Figure 6: _Cont._  
Figure 6: Prediction graph and error graph of each battery when the training data is 50%. From Figure 9, the model SOH prediction estimation comparison chart shows that CNN-LSTM-Attention-FVIM has a strong prediction ability for experimental battery data, especially for the B0005 battery and B0007 battery. The prediction curve and the true curve are almost completely overlapping, demonstrating the superiority of the CNN-LSTM-Attention-FVIM model prediction. In contrast, the prediction accuracy of the GRU model, BILSTM model, CNN model, and CNN-LSTM-Attention model is poor. Their prediction curves are mostly above the true value, and the curve following is also poor, with large fluctuations resulting in large amplitudes and poor stability. However, CNN-LSTM-Attention-FVIM has multiple performances that are significantly better than other models. In addition, it also shows excellent fitting in the battery capacity regeneration stage, meaning it still has good predictability for battery SOH, showing good robustness and stability, providing a new solution for battery SOH prediction. The RMSE and MAE Index data of different datasets and different prediction models are shown in Table 5. It can be seen from Table 5 that the prediction accuracy of the single CNN model and the CNN-LSTM-Attention model is poor, and the error is large. The maximum MAE and RMSE of the two models reached 3.78% and 3.98%, respectively. At the same time, the GRU model and the BILSTM model are still not ideal for the prediction of battery SOH. With the increase in battery aging times, the linearity of the aging curve becomes weaker, and the deviation between the prediction result and the actual value increases in the later period. The RMSE and MAE of the CNN-LSTM-Attention-FVIM model we developed are 1.33% and 0.99% at the maximum. Among them, the evaluation index MAE of the B0018 battery is 64%, 68.27%, 67.96%, and 68.47% lower than that of the GRU model, BILSTM model, CNN model, and CNN-LSTM-Attention model, respectively. It can be seen that the CNN-LSTM-Attention-FVIM model performs well in most cases and has better stability. This also shows that the model proposed in this paper is more feasible for the prediction and estimation of lithium-ion battery SOH. The above results show the predicted effects of multiple batteries, but batteries B0006 and B0018 are slightly inferior to the other two, which may be related to the following situations. First, CNN has two characteristics: translation invariance and local feature extraction. It captures the local morphological features of the charge and discharge curves (such as voltage platform fluctuations) to adapt to charging segments with different starting points, thereby achieving the predictive advantage of identifying early degradation features (such as shortening the constant current stage).