A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery

• Published in: IEEE transactions on industrial electronics (1982) Vol. 68; no. 4; pp. 3170 - 3180
• Main Authors: Liu, Kailong, Shang, Yunlong, Ouyang, Quan, Widanage, Widanalage Dhammika
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Battery cycles; data-driven approach; Decomposition; Degradation; Diagnosis; Electric vehicles (EVs); Gaussian process; Ground penetrating radar; Lithium; lithium-ion (Li-ion) batteries; Lithium-ion batteries; Machine learning; Predictions; Predictive models; Rechargeable batteries; Reliability; remaining useful life (RUL); Uncertainty; uncertainty management; Useful life

Predicting future capacities and remaining useful life (RUL) with uncertainty quantification is a key but challenging issue in the applications of battery health diagnosis and management. This article applies advanced machine-learning techniques to achieve effective future capacities and RUL predict...