Knowledge-Guided Data-Driven Model With Transfer Concept for Battery Calendar Ageing Trajectory Prediction
Author:
Affiliation:
1. Warwick Manufacturing Group, University of War-wick,Coventry,UK,CV47AL
2. Queen's University Belfast,Belfast,UK,BT7 1NN
3. Aalborg University,Department of Energy Technology,Aalborg,Denmark,9220
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Artificial Intelligence,Information Systems,Control and Systems Engineering,Control and Optimization
Link
http://xplorestaging.ieee.org/ielx7/6570654/10007882/10007917.pdf?arnumber=10007917
Reference8 articles.
1. A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery
2. Multi-kernel Relevance Vector Machine with Parameter Optimization for Cycling Aging Prediction of Lithium-ion Batteries
3. Battery Lifetime Prognostics
4. Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review
5. Electrochemical-Theory-Guided Modeling of the Conditional Generative Adversarial Network for Battery Calendar Aging Forecast
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