Battery lifetime prediction and performance assessment of different modeling approaches
Summary: Lithium-ion battery technologies have conquered the current energy storage market as the most preferred choice thanks to their development in a longer lifetime. However, choosing the most suitable battery aging modeling methodology based on investigated lifetime characterization is still a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-02-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004221000286 |
| _version_ | 1819169469558161408 |
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| author | Md Sazzad Hosen Joris Jaguemont Joeri Van Mierlo Maitane Berecibar |
| author_facet | Md Sazzad Hosen Joris Jaguemont Joeri Van Mierlo Maitane Berecibar |
| author_sort | Md Sazzad Hosen |
| collection | DOAJ |
| description | Summary: Lithium-ion battery technologies have conquered the current energy storage market as the most preferred choice thanks to their development in a longer lifetime. However, choosing the most suitable battery aging modeling methodology based on investigated lifetime characterization is still a challenge. In this work, a comprehensive aging dataset of nickel-manganese-cobalt oxide (NMC) cell is used to develop and/or train different capacity fade models to compare output responses. The assessment is conducted for semi-empirical modeling (SeM) approach against a machine learning model and an artificial neural network model. Among all, the nonlinear autoregressive network (NARXnet) can predict the capacity degradation most precisely minimizing the computational effort as well. This research work signifies the importance of lifetime methodological choice and model performance in understanding the complex and nonlinear Li-ion battery aging behavior. |
| first_indexed | 2024-12-22T19:20:00Z |
| format | Article |
| id | doaj.art-c3b90c86ce2c4056ac4c98c80ff2dca0 |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-12-22T19:20:00Z |
| publishDate | 2021-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-c3b90c86ce2c4056ac4c98c80ff2dca02022-12-21T18:15:24ZengElsevieriScience2589-00422021-02-01242102060Battery lifetime prediction and performance assessment of different modeling approachesMd Sazzad Hosen0Joris Jaguemont1Joeri Van Mierlo2Maitane Berecibar3Battery Innovation Center, MOBI Research Group, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium; Flanders Make, 3001 Heverlee, Belgium; Corresponding authorBattery Innovation Center, MOBI Research Group, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium; Flanders Make, 3001 Heverlee, BelgiumBattery Innovation Center, MOBI Research Group, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium; Flanders Make, 3001 Heverlee, BelgiumBattery Innovation Center, MOBI Research Group, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium; Flanders Make, 3001 Heverlee, BelgiumSummary: Lithium-ion battery technologies have conquered the current energy storage market as the most preferred choice thanks to their development in a longer lifetime. However, choosing the most suitable battery aging modeling methodology based on investigated lifetime characterization is still a challenge. In this work, a comprehensive aging dataset of nickel-manganese-cobalt oxide (NMC) cell is used to develop and/or train different capacity fade models to compare output responses. The assessment is conducted for semi-empirical modeling (SeM) approach against a machine learning model and an artificial neural network model. Among all, the nonlinear autoregressive network (NARXnet) can predict the capacity degradation most precisely minimizing the computational effort as well. This research work signifies the importance of lifetime methodological choice and model performance in understanding the complex and nonlinear Li-ion battery aging behavior.http://www.sciencedirect.com/science/article/pii/S2589004221000286ElectrochemistryElectrochemical Energy StorageEnergy EngineeringEnergy StorageEnergy SystemsEnergy Materials |
| spellingShingle | Md Sazzad Hosen Joris Jaguemont Joeri Van Mierlo Maitane Berecibar Battery lifetime prediction and performance assessment of different modeling approaches iScience Electrochemistry Electrochemical Energy Storage Energy Engineering Energy Storage Energy Systems Energy Materials |
| title | Battery lifetime prediction and performance assessment of different modeling approaches |
| title_full | Battery lifetime prediction and performance assessment of different modeling approaches |
| title_fullStr | Battery lifetime prediction and performance assessment of different modeling approaches |
| title_full_unstemmed | Battery lifetime prediction and performance assessment of different modeling approaches |
| title_short | Battery lifetime prediction and performance assessment of different modeling approaches |
| title_sort | battery lifetime prediction and performance assessment of different modeling approaches |
| topic | Electrochemistry Electrochemical Energy Storage Energy Engineering Energy Storage Energy Systems Energy Materials |
| url | http://www.sciencedirect.com/science/article/pii/S2589004221000286 |
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