A Convolutional Neural Network for Estimation of Lithium-Ion Battery State-of-Health during Constant Current Operation

Accurate state-of-health (SOH) estimation is critical for lithium-ion batteries' safe and reliable operation. These batteries are widely used for commercial products, including smartphones, laptops, and electric vehicles. In this paper, we develop a convolutional neural network (CNN) based battery SOH estimation model trained to estimate SOH from constant current charge and discharge data. Aging data from four cells, each charged with a different fifteen-minute fast-charging current profile, is used to train and test the SOH estimation model. The model's accuracy is demonstrated by training with data from one fast-charging aging case and tested using the other three cases, which age at a considerably different rate. The results show that the method is quite robust when the tested cells have more than 80% SOH, with error typically within $\pm \mathbf{2}{\%}$ and not exceeding $\pm \mathbf{3}{\%}$. However, the proposed method has limitations when trying to predict battery health below 80% or when trying to predict battery health from curves with different C-rates. The datasets and the code for the algorithm in this paper are available to download.