Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression

Accurate remaining useful life (RUL) prediction and state-of-health (SOH) diagnosis are of extreme importance for safety, durability, and cost of energy storage systems based on lithium-ion batteries. It is also a crucial challenge for energy storage systems to predict RUL and diagnose SOH of batter...

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Veröffentlicht in:	IEEE transactions on industrial electronics (1982) Jg. 65; H. 7; S. 5634 - 5643
Hauptverfasser:	Wei, Jingwen, Dong, Guangzhong, Chen, Zonghai
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.07.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Aging Batteries Charge transfer Computer simulation Degradation Diagnosis Electric potential Energy storage Estimation health monitoring Impedance Life prediction Lithium Lithium-ion batteries Parameter estimation Rechargeable batteries Resistance state estimation State space models State variable Storage batteries Storage systems Support vector machines support vector regression (SVR) Useful life
ISSN:	0278-0046, 1557-9948
Online-Zugang:	Volltext
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