Physics-based inverse modeling of battery degradation with Bayesian methods

Philipp, Micha and Kuhn, Yannick and Latz, Arnulf and Horstmann, Birger (2025) Physics-based inverse modeling of battery degradation with Bayesian methods. ChemSusChem. Wiley. doi: 10.1002/cssc.202402336. ISSN 1864-5631.

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Item URL in elib:

https://elib.dlr.de/207950/

Document Type:

Article

Title:

Physics-based inverse modeling of battery degradation with Bayesian methods

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	198011363
Kuhn, Yannick	Yannick.Kuhn (at) dlr.de	https://orcid.org/0000-0002-9019-2290	198011364
Latz, Arnulf	arnulf.latz (at) dlr.de	https://orcid.org/0000-0003-1449-8172	UNSPECIFIED
Horstmann, Birger	birger.horstmann (at) dlr.de	https://orcid.org/0000-0002-1500-0578	198011365

Date:

28 May 2025

Journal or Publication Title:

ChemSusChem

Refereed publication:

Yes

Open Access:

Yes

Gold Open Access:

In SCOPUS:

Yes

In ISI Web of Science:

Yes

DOI:

10.1002/cssc.202402336

Publisher:

Wiley

ISSN:

1864-5631

Status:

Published

Keywords:

Machine Learning, Bayessche Methoden, Batteriemodellierung, Degradationsmodelle

HGF - Research field:

Energy

HGF - Program:

Materials and Technologies for the Energy Transition

HGF - Program Themes:

Electrochemical Energy Storage

DLR - Research area:

Energy

DLR - Program:

E SP - Energy Storage

DLR - Research theme (Project):

E - Electrochemical Storage, E - Electrochemical Processes

Location:

Ulm

Institutes and Institutions:

Institute of Engineering Thermodynamics > Computational Electrochemistry

Deposited By:

Philipp, Micha

Deposited On:

31 Oct 2024 14:04

Last Modified:

26 Nov 2025 13:29

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