Semi-Supervised Self-Learning-Based Lifetime Prediction for Batteries
Author:
Affiliation:
1. Department of Energy Technology, Aalborg University, Aalborg, Denmark
2. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, China
Funder
Villum Fonden
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/9424/10116046/09893350.pdf?arnumber=9893350
Reference29 articles.
1. Li-Ion Battery State of Health Estimation and Remaining Useful Life Prediction Through a Model-Data-Fusion Method
2. Future Ageing Trajectory Prediction for Lithium-ion Battery Considering the Knee Point Effect
3. An Automatic Weak Learner Formulation for Lithium-Ion Battery State of Health Estimation
4. Lifetime and Aging Degradation Prognostics for Lithium-ion Battery Packs Based on a Cell to Pack Method
5. A Transferred Recurrent Neural Network for Battery Calendar Health Prognostics of Energy-Transportation Systems
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