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

Please use this identifier to cite or link to this item: http://repository.iiitd.edu.in/xmlui/handle/123456789/1579

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DC Field	Value	Language
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
Appears in Collections:	Year-2023

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