Review—Dynamic Models of Li-Ion Batteries for Diagnosis and Operation: A Review and Perspective
© 2018 The Electrochemical Society. This article discusses the options and challenges of dynamic models for the diagnosis and operation of Li-ion batteries. It provides a concise yet understandable overview on models and dynamics, and it discusses future developments needed to progress the field. Th...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
The Electrochemical Society
2021
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| Online Access: | https://hdl.handle.net/1721.1/134915 |
| _version_ | 1826212892012183552 |
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| author | Krewer, Ulrike Röder, Fridolin Harinath, Eranda Braatz, Richard D Bedürftig, Benjamin Findeisen, Rolf |
| author2 | Massachusetts Institute of Technology. Department of Chemical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Chemical Engineering Krewer, Ulrike Röder, Fridolin Harinath, Eranda Braatz, Richard D Bedürftig, Benjamin Findeisen, Rolf |
| author_sort | Krewer, Ulrike |
| collection | MIT |
| description | © 2018 The Electrochemical Society. This article discusses the options and challenges of dynamic models for the diagnosis and operation of Li-ion batteries. It provides a concise yet understandable overview on models and dynamics, and it discusses future developments needed to progress the field. The diagnosis and operation of batteries require an understanding of the main processes and their dynamics, parameters, and time constants. Processes with large time constants, such as thermal transport are equally important for safe high-performance operation as are processes with shorter time constants such as diffusion. Depending on the specific problem or operating condition, taking all of the scales into account is often unavoidable. Three separate, yet closely connected model classes are reviewed in terms of physical insight and their capabilities and limits: mechanistic models, equivalent circuit models, and data-driven models. We provide guidance for the selection of a suitable model for the particular diagnosis and operation problem of interest. The optimization of battery diagnosis and operation require versatile and simple models that span multiple time scales and allow physical insight and ease of parameterization. Fusing the existing modeling approaches may help to fully exploit their potential while integrating first-principles physical insight and measurement data. |
| first_indexed | 2024-09-23T15:39:47Z |
| format | Article |
| id | mit-1721.1/134915 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:39:47Z |
| publishDate | 2021 |
| publisher | The Electrochemical Society |
| record_format | dspace |
| spelling | mit-1721.1/1349152023-12-18T20:00:52Z Review—Dynamic Models of Li-Ion Batteries for Diagnosis and Operation: A Review and Perspective Krewer, Ulrike Röder, Fridolin Harinath, Eranda Braatz, Richard D Bedürftig, Benjamin Findeisen, Rolf Massachusetts Institute of Technology. Department of Chemical Engineering © 2018 The Electrochemical Society. This article discusses the options and challenges of dynamic models for the diagnosis and operation of Li-ion batteries. It provides a concise yet understandable overview on models and dynamics, and it discusses future developments needed to progress the field. The diagnosis and operation of batteries require an understanding of the main processes and their dynamics, parameters, and time constants. Processes with large time constants, such as thermal transport are equally important for safe high-performance operation as are processes with shorter time constants such as diffusion. Depending on the specific problem or operating condition, taking all of the scales into account is often unavoidable. Three separate, yet closely connected model classes are reviewed in terms of physical insight and their capabilities and limits: mechanistic models, equivalent circuit models, and data-driven models. We provide guidance for the selection of a suitable model for the particular diagnosis and operation problem of interest. The optimization of battery diagnosis and operation require versatile and simple models that span multiple time scales and allow physical insight and ease of parameterization. Fusing the existing modeling approaches may help to fully exploit their potential while integrating first-principles physical insight and measurement data. 2021-10-27T20:09:50Z 2021-10-27T20:09:50Z 2018 2019-08-14T18:44:40Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/134915 en 10.1149/2.1061814JES Journal of The Electrochemical Society Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf The Electrochemical Society MIT web domain |
| spellingShingle | Krewer, Ulrike Röder, Fridolin Harinath, Eranda Braatz, Richard D Bedürftig, Benjamin Findeisen, Rolf Review—Dynamic Models of Li-Ion Batteries for Diagnosis and Operation: A Review and Perspective |
| title | Review—Dynamic Models of Li-Ion Batteries for Diagnosis and Operation: A Review and Perspective |
| title_full | Review—Dynamic Models of Li-Ion Batteries for Diagnosis and Operation: A Review and Perspective |
| title_fullStr | Review—Dynamic Models of Li-Ion Batteries for Diagnosis and Operation: A Review and Perspective |
| title_full_unstemmed | Review—Dynamic Models of Li-Ion Batteries for Diagnosis and Operation: A Review and Perspective |
| title_short | Review—Dynamic Models of Li-Ion Batteries for Diagnosis and Operation: A Review and Perspective |
| title_sort | review dynamic models of li ion batteries for diagnosis and operation a review and perspective |
| url | https://hdl.handle.net/1721.1/134915 |
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