Real-time state-of-health monitoring of lithium-ion battery with anomaly detection, Levenberg–Marquardt algorithm, and multiphase exponential regression model

The state of health (SOH) of lithium-ion (Li + ) battery prediction plays significant roles in battery management and the determination of the durability of the battery in service. This study used segmentation-type anomaly detection, the Levenberg–Marquardt (LM) algorithm, and multiphase exponential...

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Vydané v:	Neural computing & applications Ročník 33; číslo 4; s. 1193 - 1206
Hlavní autori:	Ossai, Chinedu I., Egwutuoha, Ifeanyi P.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.02.2021 Springer Nature B.V
Predmet:	Algorithms Anomalies Artificial Intelligence Battery cycles Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Electrical measurement Image Processing and Computer Vision Life cycle assessment Lithium Lithium-ion batteries Model testing Multiphase Original Article Probability and Statistics in Computer Science Real time Rechargeable batteries Regression models Segmentation Transition probabilities Lithium-ion battery Temperature Levenberg–Marquardt algorithm State of health Voltage Multiphase exponential regression Anomaly detection
ISSN:	0941-0643, 1433-3058
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