## 1 Introduction
Additionally, the machine learning method is widely adopted for RUL prediction. The literature [24] analyzed the different characteristics of the early and long-term aging paths of lithium-ion batteries and utilized random logistic regression to classify batteries with different aging paths to perform accurate RUL predictions. The ensemble learning method is a kind of machine learning method that can embody collective intelligence and has the advantages of high calculation accuracy and low time cost [25]. It exhibits strong feature recognition capabilities, obviating the need for manual feature extraction and rendering it highly suitable for deployment in BMS applications aimed at predicting battery RUL. Ensemble learning techniques have been employed in studies aimed at addressing the problem of predicting RUL. [PERSON] et al. used the adaptive boosting (AdaBoost) algorithm to mine data features and fused it with LSTM to build a model to realize the RUL prediction of lithium-ion batteries [26]. The aforementioned study employs AdaBoost as a feature extraction approach, but it is not restricted to this, and the AdaBoost approach can also be used to construct prediction models directly [27]. In summary, the data-driven method offers advantages over the model-based method by avoiding establishing complex battery aging mechanism models and providing capabilities to solve nonlinear problems such as RUL prediction. Typically, the data-driven approach necessitates a substantial volume of battery-related data to ensure the precision of the prediction [28]. However, the available samples of battery aging data for new-type batteries are typically limited in scale. Therefore, the limited data samples are insufficient to fully describe the entire degradation process of the battery. It will require a significant amount of time to gather adequate aging data for the tested battery. For instance, in the Oxford dataset on battery degradation, lithium iron phosphate (LFP) batteries undergo cyclic charging and discharging at a rate of 1C until they reach the EOL. This entire process spans over 8000 cycles and lasts for nearly 300 days [29]. Hence, the utilization of data from pre-existing batteries for prognosticating novel battery types is being contemplated. Although the public datasets for lithium-ion batteries are abundant, the data on different types of batteries often fail to accurately reflect the nuances of aging between each other. Furthermore, the degradation of lithium-ion batteries varies under different operating conditions, posing challenges for accurate predictions in practical applications. Hence, it is crucial to establish a transferable predictive methodology for the remaining useful life (RUL) of lithium-ion batteries based on small data samples. This approach should effectively apply valuable information from historical data to a small number of battery samples under test, thereby significantly reducing the overall development cycle of the battery life prediction algorithm. To accomplish the aforementioned objectives, transfer learning (TL) can be employed to facilitate the transfer of battery aging knowledge across disparate battery datasets. The contributor and receiver of the transfer are called the source domain and the target domain, respectively [30]. For lithium-ion batteries, there is a difference in the distribution between the source domain and the target domain due to different operating conditions or battery types, and this difference is called domain shift [31]. To solve the domain shift between different distribution datasets, literature [32] established a battery RUL prediction model using the migration method from the perspective of cycle consistency of degradation trend. Similarly, the literature [33] combined the particle filter and LSTM network and proposed a TL-LSTM-PF model with transfer learning ability, which better realized the transfer prediction between datasets of different charging conditions on the LFP battery.