Machine Learning to identify degradation mechanisms

Philipp, Micha and Horstmann, Birger and Latz, Arnulf (2023) Machine Learning to identify degradation mechanisms. HIU Biennial Metting, 2023-07-11 - 2023-07-12, Ulm, Deutschland.

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Abstract

Use of machine learning to investigate degradation of Li-ion batteries via SEI formation. Bayesian methods for parameterization of possible degradation models and consistent model selection.

Item URL in elib:

https://elib.dlr.de/201201/

Document Type:

Conference or Workshop Item (Poster)

Title:

Machine Learning to identify degradation mechanisms

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Philipp, Micha	micha.philipp (at) dlr.de	https://orcid.org/0009-0002-8705-2059	UNSPECIFIED
Horstmann, Birger	birger.horstmann (at) dlr.de	https://orcid.org/0000-0002-1500-0578	148960573
Latz, Arnulf	arnulf.latz (at) dlr.de	https://orcid.org/0000-0003-1449-8172	UNSPECIFIED

Date:

2023

Refereed publication:

Open Access:

Gold Open Access:

In SCOPUS:

In ISI Web of Science:

Status:

Published

Keywords:

Machine learning, Bayesian methods, degradation, Li-ion batteries

Event Title:

HIU Biennial Metting

Event Location:

Ulm, Deutschland

Event Type:

national Conference

Event Start Date:

11 July 2023

Event End Date:

12 July 2023

HGF - Research field:

Energy

HGF - Program:

Materials and Technologies for the Energy Transition

HGF - Program Themes:

Chemical Energy Carriers

DLR - Research area:

Energy

DLR - Program:

E SP - Energy Storage

DLR - Research theme (Project):

E - Electrochemical Processes, E - Electrochemical Storage

Location:

Ulm

Institutes and Institutions:

Institute of Engineering Thermodynamics > Computational Electrochemistry

Deposited By:

Philipp, Micha

Deposited On:

18 Dec 2023 18:01

Last Modified:

12 Jul 2024 08:17

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