## 4 Experiment and Result Analysis
### Working Condition Migration Analysis  
Table 4 in Section 4.2 divides the datasets used in the migration experiments. The experiments are conducted on Group A1, Group A2, Group A3 and Group A4. The RUL prediction results are shown in Figure 6.  
From the experimental results in Figure 6, all approaches reflect the overall downward trend of lithium-ion battery RUL. However, there are differences between the three approaches. In the initial stage of RUL decline, the CNN-TL approach tends to exhibit significant prediction errors, whereas AdaBoost and RT-TrAdaBoost demonstrate superior fitting capabilities. In the middle stage of the battery cycle, the CNN-TL approach exhibits some fluctuations and is comparatively less robust than the other two boosting approaches. However, around the 100 th and 400 th cycles, fluctuations in the AdaBoost approach highlight the limitations of non-transfer learning methods to adapt to changing working conditions. Figure 7 represents a more intuitive comparison of the error distribution between predicted and measured values. The yellow, pink and green parts represent the RUL prediction errors of the CNN-TL, AdaBoost and RT-TrAdaBoost, respectively. The error of the CNN-TL approach is much larger than that of AdaBoost and RT-TrAdaBoost, which confirms the prediction performance of the model in Figure 6. Further analysis shows that in GroupA1, GroupA3 and GroupA4, the RT-TrAdaBoost approach shows better performance than AdaBoost, the distribution of error data is more concentrated, and the error value is also smaller. But in GroupA2, the error performance of the RT-TrAdaBoost model is slightly worse than that of the AdaBoost approach, which may be caused by the fact that the sample size of the source domain in this group is smaller than that of the target domain. In the other three groups, the data size of source domains to battery aging migration is larger than that of the target domains. In comparison, the RT-TrAdaBoost model exhibits better predictive performance when a large number of historical samples are transferred to a smaller sample set. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Source + Target \(\rightarrow\) Test Dataset** & **Data Volume Ratio** \\ \hline \multirow{8}{*}{B} & 1 & g1c38 + Cell1 \(\rightarrow\) Cell1 & 615:61:17 \\  & 2 & g2c32 + Cell1 \(\rightarrow\) Cell1 & 988:61:17 \\  & 3 & g2c48 + Cell1 \(\rightarrow\) Cell1 & 108:56:11:7 \\  & 4 & g3c2 + Cell1 \(\rightarrow\) Cell1 & 773:61:17 \\  & 5 & g5c31 + Cell1 \(\rightarrow\) Cell1 & 1038:61:17 \\  & 6 & g7c47 + Cell1 \(\rightarrow\) Cell1 & 738:61:17 \\ \hline \multirow{8}{*}{C} & 1 & g1c38 + Cell7 \(\rightarrow\) Cell7 & 615:59:17 \\  & 2 & g2c32 + Cell7 \(\rightarrow\) Cell7 & 988:59:17 \\  & 3 & g2c48 + Cell7 \(\rightarrow\) Cell7 & 1085:59:17 \\  & 4 & g3c2 + Cell7 \(\rightarrow\) Cell7 & 773:59:17 \\  & 5 & g5c31 + Cell7 \(\rightarrow\) Cell7 & 1038:59:17 \\  & 6 & g7c47 + Cell7 \(\rightarrow\) Cell7 & 738:59:17 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Dataset division and data size ratios in battery type migration experiment. Figure 8 and Table 6 provide a comparative analysis of the evaluation results for each approach. The larger the criteria, the closer the color of the square approaches magenta; otherwise, it tends toward dark green. To highlight the differences in the evaluation criteria of each method in a single group, the color scale of each group is set independently. The MAE of the RT-TrAdaBoost model in the four groups reached 0.58%, 1.04%, 0.63% and 0.36%, respectively. The MAE of the AdaBoost algorithm is 0.91%, 0.99%, 0.94% and 0.58%. While the CNN-TL only performed 4.86%, 8.25%, 8.57% and 3.94%. Figure 6: Lithium-ion battery RUL prediction results. (**a**) Transfer g5c31 to g1c38. (**b**) Transfer g3c2 to g2c48. (**c**) Transfer g5c31 to g2c48. (**d**) Transfer g2c32 to g7c47. Figure 7: The error distribution of the different lithium-ion battery RUL prediction methods. Figures 7 and 8 jointly confirm the RUL prediction errors of the three methods.