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Enhanced state of health prediction of battery using machine learning and digital twin

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dc.contributor.author Sharma, Raghav
dc.contributor.author Kumar, Shobhit
dc.contributor.author Shankhwar, Kalpana (Advisor)
dc.date.accessioned 2024-05-22T12:25:12Z
dc.date.available 2024-05-22T12:25:12Z
dc.date.issued 2023-11-29
dc.identifier.uri http://repository.iiitd.edu.in/xmlui/handle/123456789/1579
dc.description.abstract In the digital age, use of batteries has increased manifold and thus, our dependence on them. It becomes critical that we are able to accurately predict the remaining charge and the state of health of the battery for safe and efficient operation. We can achieve this using Machine Learning models and technologies such as Digital Twin. en_US
dc.language.iso en_US en_US
dc.publisher IIIT-Delhi en_US
dc.subject NASA Li-Ion Study and Datasets en_US
dc.subject Randomised Battery Usage Data Set en_US
dc.subject Digital Twin and Prediction methods en_US
dc.subject Predicting Lithium-Ion Battery Degradation Using Machine Learning en_US
dc.title Enhanced state of health prediction of battery using machine learning and digital twin en_US
dc.type Other en_US


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