A Transferable Prediction Approach for the Remaining Useful Life of Lithium-Ion Batteries Based on Small Samples

Abstract

Predicting the remaining useful life (RUL) of batteries can help users optimize battery management strategies for better usage planning. However, the RUL prediction accuracy of lithium-ion batteries will face challenges due to fewer data samples available for the new type of battery. This paper proposed a transferable prediction approach for the RUL of lithium-ion batteries based on small samples to reduce time in preparing battery aging data and improve prediction accuracy. This approach, based on improvements from the adaptive boosting algorithm, is called regression tree transfer adaptive boosting (RT-TrAdaBoost). It combines the advantages of ensemble learning and transfer learning and achieves high computational efficiency. The RT-TrAdaBoost approach takes the charging voltage and temperature curve as input and utilizes the classification and regression tree (CART) as the base learner, which has better feature capture ability. In the experiment, the working condition migration experiment and battery type migration experiment are conducted on non-overlapping datasets. The verified results revealed that the RT-TrAdaBoost approach could transfer not only the battery aging knowledge between various working conditions but also realize the RUL migration prediction from lithium iron phosphate battery to lithium cobalt oxide battery. The analysis of error and computation time demonstrates the proposed method’s high efficiency and speed.

Method for Evaluating Degradation of Battery Capacity Based on Partial Charging Segments for Multi-Type Batteries

“…In this section, the MMD-based feature transfer learning algorithm, the instance-based transfer learning algorithm, and the parameter-based transfer learning algorithm are compared on the same GateCNN-BiLSTM model to verify the effectiveness of the MMD feature transfer learning algorithm. For the instance-based transfer learning method, the classic TrAdaBoost algorithm [53] is selected. The main idea of this method is to select samples similar to the target domain from the source domain, re-weight them, and then add the weighted samples to the target domain training set to assist in the training of the target domain model.…”