Accurate prediction of the remaining useful life of a lithium–ion battery (LiB) is of paramount importance for ensuring its durable operation. To achieve more accurate prediction with limited data, this paper proposes an RVM-GM algorithm based on dynamic window size. The method combines the advantages of the relevance vector machine (RVM) algorithm and grey predictive model (GM). The RVM is applied to provide the relevance vectors of fitting function and output probability prediction, and the GM is used to obtain the trend prediction with limited data information. The algorithm is further verified by the NASA PCoE lithium–ion battery data repository. The experimental prediction results of different batteries data show that the proposed algorithm has less error while applying a dynamic window size compared with a fixed window size, while it has higher prediction accuracy than particle filter algorithm (PF) and convolutional neural network (CNN), which has verified the effectiveness of the proposed algorithm.