# 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
### Discussion on Ablation Experiments and Computational Training Cost  
In order to further verify the feasibility of the model proposed in this paper and the impact of a single model in the CNN-LSTM-Attention-FVIM model on the prediction effect, we conducted an ablation experiment and discussed the computational cost and training time. The ablation experiment and the recorded running time were run on the same computer configuration and under the same parameters using MATLAB R2024a. The \begin{table}
\begin{tabular}{c c c} \hline \hline
**Battery** & **MAE** & **RMSE** \\ \hline CS2-35 & 0.0070 & 0.0087 \\ CS2-37 & 0.0043 & 0.0058 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation metrics for the University of Maryland dataset. Figure 11: University of Maryland single dataset. Figure 10: Comparison of evaluation indicators of different models. By analyzing the battery charging curve and related health factors, and using the Pearson and Spearman correlations to analyze the correlation with its SOH, the health factors with lower correlation were eliminated, and the input data quality was optimized. 2. In order to solve the problems of prediction fluctuation and large error caused by the randomness of the parameters of the traditional CNN-LSTM-Attention model and the inability to find the optimal parameters, the FVIM algorithm was introduced to globally optimize its input weights and biases, and key parameters such as learning rate, regularization coefficient, and number of hidden layer nodes were dynamically adjusted to achieve the best prediction effect. 3. By comparing the advantages and disadvantages of the proposed model with four deep learning network models, namely GRU, BILSTM, CNN, and CNN-LSTM-Attention, the proposed method had the smallest SOH estimation error. 4. When the proposed model as used to predict and estimate battery SOH for different batteries and different training set ratios, its RMSE and MAE were less than 0.99% and 1.33%, respectively. This method has been verified using the NASA and University of Maryland battery aging datasets, showing strong fitting characteristics, multi-condition compatibility, and excellent prediction capabilities. However, faced with the challenge of random fragmented charging data (such as 20-80% partial charging) in practical applications, in the future, we will focus on developing lightweight optimization strategies such as feature reconstruction technology based on generative adversarial networks to improve real-time performance and generalization across battery models, promoting the sustainable development of electric vehicles and energy storage systems. G.L. : conceptualization, experiment, validation, writing--original draft. Z.D. : resources, methodology, writing--review and editing, supervision. L.L. : methodology, validation, resources. Y.X. : validation, supervision, writing--review and editing. G.G. : methodology, validation, supervision. H.Z. : experiment, software, writing--review and editing. L.T. : resources, conceptualization. Y.L. : experiment, software. M.G. : Writing--review and editing. Z.Y. : Experiment, methodology. M.Y. : validation, supervision. All authors have read and agreed to the published version of the manuscript. This work was sponsored by the Beijing Natural Science Foundation (Grant 3244039), \"R&D Program of Beijing Municipal Education Commission\" (Grant KM202411232021), Collaborative Innovation Center of Ningde Normal University (Grant 2022 ZX02). Not applicable. The data provided in this study are available on NASA's official website and the University of Maryland's official website.