Degradation of lithium-ion cells with respect to increases of internal resistance (IR) has negative implications for rapid charging protocols, thermal management of cells and power output. Despite this, IR receivesmuch less attention than capacity degradation in Li-ion cell research. Building on recent developments on ‘knee’ identification for capacity degradation curves we propose the new concept of ‘elbow-point’ and ‘elbow-onset’ for IR rise curves, and a robust identification algorithm for those variables. We report on the relations between capacity’s knees, IR’s elbows and end of life (EOL) for the large dataset of the study. We enhance our discussion with two applications. We use Neural Network techniques to build independent State of Health capacity and IR predictor models achieving a MAPE of 0.4% and 1.6%, respectively, and an overall RMSE below 0.0061. A relevance vector machine (RVM) using the first 50-cycles of life data is employed for the early prediction of elbow-points and elbow-onsets achieving a MAPE of 11.5% and 14.0% respectively.
|Number of pages||16|
|Journal||Advanced Energy Materials|
|Publication status||Accepted/In press - 9 Jan 2021|