Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries

Xin Sui, Remus Teodorescu

ISBN 10: 3036598758 / ISBN 13: 9783036598758

Edité par Mdpi AG, 2024

A propos de cet article

N° de réf. du vendeur LU-9783036598758

Détails bibliographiques

• Titre: Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries
• Éditeur: Mdpi AG
• Date de parution: 2024
• Langue: anglais
• ISBN à 10 chiffres: 3036598758
• ISBN à 13 chiffres: 9783036598758
• Reliure: Hardback
• État de l'article: New

A propos de ce titre

Synopsis

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.