# State of Health Prediction for Lithium-Ion Batteries Using Transformer-LSTM Fusion Model
## 4 Results and Discussion
### Prediction of the Different Batteries  
Predicting the SOH of different batteries presents two main challenges: feature selection effectiveness and model generalization capability. First, whether the selected features effectively reflect changes in SOH directly impacts prediction accuracy. Therefore, selected features must strongly correlate with the battery's degradation process and be representative enough to capture the dynamics of its SOH. Second, the model's generalization ability is of critical importance. Factors such as operating environment and cycle conditions may lead to varying performance across batteries during SOH degradation. Thus, the prediction model must generalize well, adapt to the characteristics of different batteries, and accurately infer their SOH trends.  
For different batteries, we used the training set and two test sets in Section 4.1 for SOH prediction. The training set contains the complete feature data of 41 batteries from the first two batches, and the test set is divided into the primary test set (containing 43 batteries) and the secondary test set (containing 40 batteries). In order to ensure the representativeness of the features while avoiding increasing the computational burden of the model, we chose to predict battery SOH based on 10 features. In the two test sets, six batteries were randomly selected for prediction performance evaluation: batteries with channel numbers 27 and 36 from the \"2017-05-12\" batch, batteries with channel numbers 11 and 17 from the \"2017-06-30\" batch, and batteries with channel numbers 20 and 30 from the \"2018-04-12\" batch.  
The SOH prediction results for different batteries are shown in Figure 11. Although the cycles of different batteries vary from 463 to 874, their SOH change trends show similar decay patterns. Even so, the model demonstrates excellent predictive performance on the test set. The predicted value (red curve) is highly consistent with the true value (blue curve), especially in the critical stage of battery decay, and the prediction model can accurately capture the change in SOH. In particular, for the battery with channel number 36 in the batch \"2017-05-12\" and the battery with channel number 20 in the batch \"2018-04-12\", the predicted _RMSE_ and _MAE_ values are both less than 0.002, as shown in Table 2, showing extremely low prediction errors and the high-precision prediction ability of the model on these batteries. For the battery with channel number 17 in the batch \"2017-06-30\", the figure shows that the SOH is interfered with by noise, but the model can still accurately reflect the overall decay trend of the battery. As for the battery with channel number 30 in the batch of \"2018-04-12\", unlike other batteries, its SOH maintained a similar downward trend throughout the cycle. Although there was a certain deviation between the predicted value and the true value at the beginning and end of the data, the overall performance remained consistent. These results show that the model performs stably on different batteries and can effectively handle the complexity and differences in battery SOH prediction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Battery** & _RMSE_ & _MAE_ \\ \hline
2017-05-12\_27 & 0.00339 & 0.00275 \\
2017-05-12\_36 & 0.00159 & 0.00124 \\
2017-06-30\_11 & 0.00487 & 0.00394 \\
2017-06-30\_17 & 0.00341 & 0.00248 \\
2018-04-12\_20 & 0.00134 & 0.00099 \\
2018-04-12\_30 & 0.00329 & 0.00192 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Prediction evaluation indicators on the different batteries.