## 1 Introduction
Although these studies have yielded favorable outcomes, there remain two aspects that require further investigation. 1. In some existing methods for predicting the RUL, there is a degree of data overlap between the source and target domains. However, in the actual situation, the target domain data reflects the aging degree of the new battery under the current working conditions. The source domain data are often from old batteries. Data between these two domains should not overlap. Although setting up experiments under partial overlap will reduce the difficulty of transfer learning and help to obtain better experimental results, it does not meet the needs of actual engineering. 2. RUL migration prediction is limited to the same type of battery under different working conditions and does not extend to migration between different types of batteries. To provide users with accurate battery life information, it often takes a lot of time to perform cycle aging experiments to predict the life of new battery models. However, this will delay the sale of new type battery, so data from older batteries with different materials can be considered for migration prediction. For example, the migration from LFP batteries aging data to lithium cobalt oxide (LCO) batteries aging data can be explored. To address the aforementioned limitations, this paper proposes a transfer learning approach based on small sample sizes that leverages the strengths of both ensemble learning and transfer learning. The approach called regression tree transfer adaptive boosting (RT-TrAdaBoost) utilizes the directly measurable charging voltage and temperature as input variables, facilitating accurate prediction of the RUL of lithium-ion batteries. To enhance the ability to capture intrinsic features, we have innovatively employed classification and regression tree (CART) as the base learner of the RT-TrAdaBoost model for learning the mapping relationship between aging data and remaining life. Subsequently, non-overlapping target and source domains are created from the battery dataset, followed by conducting experiments under various working conditions and different types of batteries. Finally, the proposed approach is validated on both a personal computer and an edge intelligent computing module. Through analysis of errors and calculation times, it has been demonstrated that this approach is both fast and effective. The main contributions of this paper are summarized as follows:To accurately transfer battery knowledge from historical data to small samples, this paper utilizes the advantages of ensemble learning and transfer learning to establish the RT-TrAdaBoost model for predicting the RUL of lithium-ion batteries. 2. Considering the deployment of the approach on BMS, the directly measurable charging voltage and temperature curves are used as the input of the model. Utilizing CART as the base learner of the established model improves the model's ability to capture battery data features. 3. This paper establishes non-overlapping target and source domains in order to make the research more practical. We not only evaluate the adaptability of the RT-TraAdaBoost model under varying working conditions for a single battery type but also investigate its transferability from LFP batteries to LCO batteries. The experimental results demonstrate satisfactory performance in terms of prediction accuracy and processing time. The subsequent chapters are structured as follows: Section 2 introduces the proposed RUL prediction approach for lithium-ion batteries. Section 3 presents data preparation. Section 4 focuses on the analysis of the experimental results. Section 5 summarizes the conclusions drawn from this study.