Remaining useful life prediction of lithium-ion batteries based on KS agglomeration function integrating multi-expert knowledge

Abstract

In recent years, lithium-ion batteries have been widely used in the field of new energy, and the prediction of the remaining useful life (RUL) of lithium-ion batteries has become the focus in this field. There are few literatures about the fusion of multi-expert knowledge, integrated into the training process of the BP Neural Network, in RUL prediction based on small sample data, but it can improve the performance of the BP Neural Network and require less training sample data in non-linear regression model, which is the innovation of this paper. When there is a lot of expert knowledge, there is no general framework that can simultaneously integrate multiple pieces of expert knowledge. Therefore, this paper proposes a multi-expert knowledge processing method based on the Kreisselmeier-Steinhauser (KS) agglomeration function and applies it to the prediction of the remaining life of lithium-ion batteries. First, indirect health factors are extracted based on the NASA lithium-ion battery aging dataset. Secondly, the initial weights and thresholds of the Back-Propagation (BP) Neural Network are optimized by a genetic algorithm. The KS agglomeration function is used again to condense multiple pieces of expert knowledge into one. Finally, the expert knowledge is integrated into the training process of the BP Neural Network by the Augmented Lagrange multiplier method. The simulation results show that, compared with the mainstream algorithms, the improved algorithm proposed in this paper reduces the relative error (RE) and mean absolute error (MAE) obviously and has better prediction results. It means that the improved algorithm can the effectively improve the RUL prediction accuracy of lithium-ion batteries when there are many expert knowledges.