Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries (Hardcover)

Remus Teodorescu

ISBN 10: 3036598758 / ISBN 13: 9783036598758

Editore: Mdpi AG, 2024

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Hardcover. This reprint aims to showcase manuscripts presenting efficient SOH estimation methods using AI which exhibit good performance such as high accuracy, high robustness against the changes in working conditions, and good generalization, etc. Lithium-ion batteries have a wide range of applications, but one of their biggest problems is their limited lifetime due to performance degradation during usage. It is, therefore, essential to determine the battery's state of health (SOH) so that the battery management system can control the battery, enabling it to run in the best state and thus prolonging its lifetime. Artificial intelligence (AI) technologies possess immense potential in inferring battery SOH and can extract aging information (i.e., SOH features) from measurements and relate them to battery performance parameters, avoiding a complex battery modeling process. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Codice articolo 9783036598758

Dati bibliografici

• Titolo: Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries (Hardcover)
• Editore: Mdpi AG
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 3036598758
• ISBN 13: 9783036598758
• Rilegatura: Hardcover
• Condizione: new

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Riassunto

This reprint aims to showcase manuscripts presenting efficient SOH estimation methods using AI which exhibit good performance such as high accuracy, high robustness against the changes in working conditions, and good generalization, etc. Lithium-ion batteries have a wide range of applications, but one of their biggest problems is their limited lifetime due to performance degradation during usage. It is, therefore, essential to determine the battery's state of health (SOH) so that the battery management system can control the battery, enabling it to run in the best state and thus prolonging its lifetime. Artificial intelligence (AI) technologies possess immense potential in inferring battery SOH and can extract aging information (i.e., SOH features) from measurements and relate them to battery performance parameters, avoiding a complex battery modeling process.