Open AccessAnode Potential Estimation in Lithium-Ion Batteries Using Data-Driven Models for Online Applications
Author(s) -
Jacob Hamar,
Simon V. Erhard,
Christoph Zoerr,
Andreas Jossen
Publication year - 2021
Publication title -
journal of the electrochemical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.258
H-Index - 271
eISSN - 1945-7111
pISSN - 0013-4651
DOI - 10.1149/1945-7111/abe721
Subject(s) - anode , robustness (evolution) , ion , lithium (medication) , computer science , materials science , analytical chemistry (journal) , algorithm , chemistry , electrode , medicine , biochemistry , organic chemistry , gene , endocrinology , chromatography
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.