An optimal relevance vector machine with a modified degradation model for remaining useful lifetime prediction of lithium-ion batteries

Abstract

Predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs) is vital to ensure the safety and reliability of battery-powered systems. Owing to its ability to represent uncertainties in predictions, relevance vector machine (RVM) regression is effective for short-term predictions, but it has low accuracy for long-term predictions. To improve its performance, an integrated RUL prediction method is proposed based on optimal relevance vectors (RVs) and a modified degradation model (MDM) with the Hausdorff distance (HD), i.e., ORV-MDMHD. First, phase space reconstruction is introduced to produce the inputs to the RVM, which can enhance the long-range dependence of the capacity data for prediction. Then, for RVM regression, its critical parameter, the kernel width, is set to a numerical range rather than a single value. As a result, a series of RVM models is trained, and different sets of RVs are then obtained to represent the original large-scale training data. Moreover, an MDM is designed to fit each set of RVs, which generates one of degradation curves to simulate the possible battery aging process. Then, a curve similarity measure, the HD, is used to select the optimal fitted curve that is most similar to the actual degradation curve. Finally, the predicted RUL of the battery can be calculated by extrapolating the optimal curve to the failure threshold. The experimental results for two cases of batteries indicate that the proposed prediction method can provide more stable prediction with higher accuracy, especially for long-term prediction of the LIB RUL.