Incremental Battery Model Using Wavelet-Based Neural Networks

Abstract

This paper presents a multi-resolution modeling approach using wavelet neural networks. A lithium battery model, which has three resolutions, is developed to depict the modeling approach. By combining the advantages of dyadic activation functions and the orthonormal property of wavelet functions, the developed battery model possesses two salient features. First, the model is built from a coarser approximation to a finer representation by adding more details incrementally. Second, the model at a low resolution is compatible with the model at a high resolution, which means that the parameters used in a low resolution can be directly incorporated into a high resolution without any modification. This paper's results show that this battery modeling provides great flexibility for users to choose a suitable resolution to meet their requirements for model accuracy and model execution speed.