Useful life prediction based on wavelet packet decomposition and two-dimensional convolutional neural network for lithium-ion batteries

It is important to know the replace time for reducing the lithium-ion battery risk and assessing its reliability. For this purpose, the remaining useful life (RUL) can play an important role in the prognostics and health management of battery to solve the inaccurate prediction issue. The existing RUL prediction techniques for lithiumion batteries are inefficient for learning long-term dependencies among capacity degradations. In this work, a new forecasting approach is proposed based on wavelet packet decomposition, two-dimensional convolutional neural network, and adaptive multiple error corrections. In this model, the bivariate Dirichlet mixture model is considered to make the heteroscedasticity of the unpredictable residuals signal based non-parametric distribution. To show the validity of the proposed model, the experimental data are considered based on Continental Europe and NASA Ames Prognostics Center of Excellence battery datasets. The obtained numerical analysis presents an accurate forecasting model. Different comparisons with the well-known models are made to show the validity of the suggested approach, which proves the superiority and forecasting stability of the proposed model.

Automated summary

A new forecasting approach based on wavelet packet decomposition, two-dimensional convolutional neural network, and adaptive multiple error corrections is proposed for lithium-ion battery RUL prediction. The model considers a bivariate Dirichlet mixture model to address the heteroscedasticity of unpredictable residuals. Numerical analysis using experimental data demonstrates the accuracy and superiority of the proposed model over existing techniques, showing its forecasting stability.