## 4 Experiment and Result Analysis
### Battery Type Migration Analysis  
After verifying the migration prediction effect of the RT-TrAdaBoost model in different working conditions, this paper further expands the migration prediction experiment between different types of batteries. In addition to the inconsistency of battery types, other factors, such as working conditions and temperature, also exhibit significant variations. This experiment will use six LFP batteries in the Stanford-MIT-Toyota dataset and two LCO batteries in the Oxford dataset, a total of 12 groups. As shown in Table 5, the sample size ratio of the source domain and the target domain in each group ranges from about 10:1 to 17.4:1. Similarly, the RUL prediction results on the three models are shown in Figures 9 and 10. Figure 9: Transferable lithium-ion battery RUL prediction results from MIT–Stanford–Toyota dataset to Cell1. (**a**) g1c38. (**b**) g2c32. (**c**) g2c48. (**d**) g3c2. (**e**) g5c31. (**f**) g7c47. In Figures 9 and 10, the RT-TrAdaBoost approach has excellent fitting performance and is more robust than the AdaBoost method. As for the CNN-TL, although the overall trend is consistent with the measured values, it exhibits fluctuations locally. Especially at the 4000 th-7000 th cycle, the fluctuation of the RUL prediction result curve is particularly severe. A similar situation also appears in Figure 10. However, although Cell1 and Cell7 are the same type of battery and their datasets are obtained under the same working condition, in contrast, the fluctuation of the CNN-TL approach in predicting Cell7 extends to around the 8000 th cycle. This indicates that the CNN-TL also exhibits variances in its adaptability towards identical battery types, whereas the RT-TrAdaBoost approach is well-suited to address such discrepancies. By comparison, our proposed method has demonstrated  
Figure 10: Transferable lithium-ion battery RUL prediction results from MIT–Stanford–Toyota dataset to Cell7. (**a**) g1c38. (**b**) g2c32. (**c**) g2c48. (**d**) g3c2. (**e**) g5c31. (**f**) g7c47. favorable RUL prediction outcomes across multiple batteries, showcasing both stability and precision. From Figure 11, compared with the other two methods, the error distribution of the RT-TrAdaBoost method is more concentrated around 0. The finer model performance is reflected in Figures 12 and 13 through evaluation criteria. Tables 7 and 8 record the specific values. On the six batteries in Group B, the MAE of RUL prediction by the RT-TrAdaBoost method is 1.48% to 1.80%. In Group C, it ranges from 1.24% to 1.92%. The AdaBoost method and the CNN-TL approach are only 1.75% to 2.30% and 3.93% to 7.21% in Group B, respectively. In Group C, they are 1.78% to 2.24% and 4.64% to 6.13%, respectively. Regardless of the magnitude or scope of the error, it can be inferred that the RUL prediction outcome obtained through the RT-TrAdaBoost approach is more precise. From the evaluation of the RMSE index, for the Group B and Group C experiments, the RT-TrAdaBoost method is 1.50% to 2.24%, which is smaller than the AdaBoost method and the CNN-TL approach. Figure 12: The evaluation criteria of lithium-ion battery RUL prediction results. (**a**) Transfer g1c38 to Cell1. (**b**) Transfer g2c32 to Cell1. (**c**) Transfer g2c48 to Cell1. (**d**) Transfer g3c2 to Cell1. (**e**) Transfer g5c31 to Cell1. (**f**) Transfer g7c47 to Cell1.