Bayesian Optimization for Parameterization of Battery Cell Models

Horstmann, Birger and Kuhn, Yannick and Latz, Arnulf (2022) Bayesian Optimization for Parameterization of Battery Cell Models. 31st Topical Meeting of the International Society of Electrochemistry, 2022-05-15 - 2022-05-19, Aachen, Deutschland.

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Item URL in elib:

https://elib.dlr.de/191243/

Document Type:

Conference or Workshop Item (Speech)

Title:

Bayesian Optimization for Parameterization of Battery Cell Models

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Horstmann, Birger	birger.horstmann (at) dlr.de	https://orcid.org/0000-0002-1500-0578	UNSPECIFIED
Kuhn, Yannick	yannick.kuhn (at) dlr.de	https://orcid.org/0000-0002-9019-2290	UNSPECIFIED
Latz, Arnulf	arnulf.latz (at) dlr.de	https://orcid.org/0000-0003-1449-8172	UNSPECIFIED

Date:

2022

Refereed publication:

Yes

Open Access:

Yes

Gold Open Access:

In SCOPUS:

In ISI Web of Science:

Status:

Published

Keywords:

machine learning, battery modelling

Event Title:

31st Topical Meeting of the International Society of Electrochemistry

Event Location:

Aachen, Deutschland

Event Type:

international Conference

Event Start Date:

15 May 2022

Event End Date:

19 May 2022

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

Location:

Ulm

Institutes and Institutions:

Institute of Engineering Thermodynamics > Computational Electrochemistry

Deposited By:

Horstmann, Birger

Deposited On:

12 Dec 2022 18:51

Last Modified:

12 Jun 2024 15:13

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