State-of-health estimation and remaining useful life prediction of lithium-ion batteries using DnCNN-CNN

Abstract

Accurate evaluation of state-of-health (SoH) and prediction of remaining useful life (RUL) are crucial to sustain the reliability of lithium-ion batteries (LIBs) via timely maintenance actions. However, ambient noises under various operating conditions hinder accurate diagnosis of dynamic status for LIBs in real-world applications. To overcome this difficulty, an allied denoising convolutional neural network (DnCNN) and convolutional neural network (CNN) model is proposed as a new framework for estimating SoH and predicting RUL of LIBs under various operating environments. In the presence of unknown ambient noises, DnCNN is applied to improve prediction accuracy of SoH to eliminate the noises using a residual learning technique. To verify denoising abilities and resulting SoH prediction performance under real-life scenarios, multi-physics feature degradation testing data collected from custom test benches are used to evaluate its performance over competitive denoising techniques. Results from the experiments under various operating environments demonstrate that the proposed allied framework results in a higher accuracy and robustness than other state-of-the-art denoising methods in estimating SoH and predicting RUL of LIBs.