Anode Potential Estimation in Lithium-Ion Batteries Using Data-Driven Models for Online Applications. Issue 3 (22nd March 2021)

• Record Type: Journal Article
• Authors: Hamar, Jacob C.; Erhard, Simon V.; Zoerr, Christoph; Jossen, Andreas
• Is Part Of: Journal of the Electrochemical Society. Volume 168:Issue 3(2021)
• Publication Date: 2021-03-22
• Subjects: Batteries—Li-ion -- Electrochemical Engineering -- Energy Storage; Electrochemistry -- Periodicals; 541.3705
• DOI: 10.1149/1945-7111/abe721
• Languages: English
• ISSNs: 0013-4651

Abstract

Abstract : Three anode estimation methods are presented and evaluated for their accuracy and storage requirements. After generating training data using a Pseudo-2D Physiochemical model, these models are fit and trained to estimate the anode potential during fast charge events. A simplified linear and non-linear model show an estimationerror of ca. 13 mV and the lowest memory demand, however, a novel random forest model reduces the error to 2.6 mV. The empirical methods are suitable for a lithium plating warning detection system during fast charging and are further evaluated for over-fitting and robustness using an out-of-sample dataset.