Data-driven systematic parameter identification of an electrochemical model for lithium-ion batteries with artificial intelligence (English)
- New search for: Li, Weihan
- New search for: Demir, Iskender
- New search for: Cao, Decheng
- New search for: Jöst, Dominik
- New search for: Ringbeck, Florian
- New search for: Junker, Mark
- Further information on Junker, Mark:
- https://orcid.org/0000-0001-5842-3290
- New search for: Sauer, Dirk Uwe
- Further information on Sauer, Dirk Uwe:
- https://orcid.org/0000-0002-5622-3591
- New search for: Li, Weihan
- New search for: Demir, Iskender
- New search for: Cao, Decheng
- New search for: Jöst, Dominik
- New search for: Ringbeck, Florian
- New search for: Junker, Mark
- Further information on Junker, Mark:
- https://orcid.org/0000-0001-5842-3290
- New search for: Sauer, Dirk Uwe
- Further information on Sauer, Dirk Uwe:
- https://orcid.org/0000-0002-5622-3591
2022
- Miscellaneous / Electronic Resource
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Title:Data-driven systematic parameter identification of an electrochemical model for lithium-ion batteries with artificial intelligence
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Contributors:Li, Weihan ( author ) / Demir, Iskender ( author ) / Cao, Decheng ( author ) / Jöst, Dominik ( author ) / Ringbeck, Florian ( author ) / Junker, Mark ( author ) / Sauer, Dirk Uwe ( author )
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Publisher:
- New search for: RWTH Aachen University
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Publication date:2022-01-01
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Size:557-570 pages
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Remarks:Energy storage materials 44, 557-570 (2022). doi:10.1016/j.ensm.2021.10.023
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ISSN:
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DOI:
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Type of media:Miscellaneous
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Type of material:Electronic Resource
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Language:English
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Keywords:
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Source: