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dc.contributor.authorAmiri, Mahshid N.
dc.contributor.authorHåkansson, Anne Eva Margareta
dc.contributor.authorBurheim, Odne Stokke
dc.contributor.authorLamb, Jacob Joseph
dc.date.accessioned2024-05-22T10:58:23Z
dc.date.available2024-05-22T10:58:23Z
dc.date.issued2024-05-21
dc.description.abstractDigitalization of lithium-ion batteries can significantly advance the performance improvement of lithium-ion batteries by enabling smarter controlling strategies during operation and reducing risk and expenses in the design and development phase. Accurate physics-based models play a crucial role in the digitalization of lithium-ion batteries by providing an in-depth understanding of the system. Unfortunately, the high accuracy comes at the cost of increased computational cost preventing the employment of these models in real-time applications and for parametric design. Machine learning models have emerged as powerful tools that are increasingly being used in lithium-ion battery studies. Hybrid models can be developed by integrating physics-based models and machine learning algorithms providing high accuracy as well as computational efficiency. Therefore, this paper presents a comprehensive review of the current trends in integration of physics-based models and machine learning algorithms to accelerate the digitalization of lithium-ion batteries. Firstly, the current direction in explicit modeling methods and machine learning algorithms used in battery research are reviewed. Then a thorough investigation of contemporary hybrid models is presented addressing both battery design and development as well as real-time monitoring and control. The objective of this work is to provide details of hybrid methods including the various applications, type of employed models and machine learning algorithms, the architecture of hybrid models, and the outcome of the proposed models. The challenges and research gaps are discussed aiming to provide inspiration for future works in this field.en_US
dc.identifier.citationAmiri, Håkansson A, Burheim O., Lamb JJ. Lithium-ion battery digitalization: Combining physics-based models and machine learning. Renewable and Sustainable Energy Reviews. 2024;200en_US
dc.identifier.cristinIDFRIDAID 2269633
dc.identifier.doihttps://doi.org/10.1016/j.rser.2024.114577
dc.identifier.issn1364-0321
dc.identifier.issn1879-0690
dc.identifier.urihttps://hdl.handle.net/10037/33594
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.journalRenewable and Sustainable Energy Reviews
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2024 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleLithium-ion battery digitalization: Combining physics-based models and machine learningen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Med mindre det står noe annet, er denne innførselens lisens beskrevet som Attribution 4.0 International (CC BY 4.0)