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2023 | OriginalPaper | Chapter

Combination of Data-Driven and Physics-Based Models for Thick Sintered Electrode Lithium-Ion Batteries

Authors : Chen Cai, Shengyu Liu, Ziyang Nie, Kyoung-Yun Kim, Gary M. Koenig Jr.

Published in: Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

Publisher: Springer International Publishing

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Abstract

This work explored the use of physics-based and machine learning models in the context of a promising battery material system. The battery materials system was electrodes comprised of only electroactive material, which provides increased energy density at the cell level. The overall target of the modeling platform is to develop tools to aid in accelerating the experimental material discovery process. Machine learning models provide a route to more accurately predict the experimental electrochemical capacity of the materials in battery cells, although appropriate data training sets are needed. The combined application of the physics-based model and machine learning model resulted in the most accurate prediction of electrochemical cell outcomes.

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Title: Combination of Data-Driven and Physics-Based Models for Thick Sintered Electrode Lithium-Ion Batteries
Authors: Chen Cai
Shengyu Liu
Ziyang Nie
Kyoung-Yun Kim
Gary M. Koenig Jr.
Publisher: Springer International Publishing
Book: Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus
Print ISBN: 978-3-031-17628-9

Electronic ISBN: 978-3-031-17629-6

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-3-031-17629-6_54

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