A Convolutional Neural Network for Estimation of Lithium-Ion Battery State-of-Health during Constant Current Operation
Junran Chen, Manjula Manivanan, Josimar Duque, Phillip J. Kollmeyer, Satyam Panchal, Oliver Groß, Ali Emadi
Abstract
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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm \mathbf{2}{\%}$</tex> and not exceeding <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm \mathbf{3}{\%}$</tex> . 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.