Deep neural network-driven in-situ detection and quantification of lithium plating on anodes in commercial lithium-ion batteries

Abstract

Lithium plating seriously threatens the life of lithium-ion batteries at low temperatures charging conditions, but the onboard detection and quantification of lithium plating are severely hampered by the limited available signals and volatile operating conditions in real scenarios. Herein, we propose a detection method to predict the occurrence and quantification of lithium plating under uncertain conditions by only using constant-current curves during charging based on deep learning. A deep neural network (DNN) is developed to extract data-driven features induced by lithium plating from the charge curves, avoiding the challenge of manual feature selection. Only using the most common voltage and current signals as inputs, the network exhibits superior adaptability and accuracy. The detection accuracy of the proposed method is 98.64%, while the quantity of the lithium plating can be accurately predicted with a root-mean-square error <4.1712 mg. Moreover, the generalization ability of the proposed method is verified by its reliable detection accuracy under conditions that are not used in the training dataset. The detection accuracy is 92.39% for brand new charging conditions and 95.53% for brand new aging states. This method shortens the detection time that currently takes more than several hours (the widely used differential curve analysis) to milliseconds and eliminates the need for a rigorous testing environment, showing great potential for onboard application in future battery management systems. (Figure presented.).