Adaptive Model-Based Reinforcement Learning for Fast-Charging Optimization of Lithium-Ion Batteries
Author:
Affiliation:
1. Department of Automation, Tsinghua University, Beijing, China
2. Department of Chemical Engineering, Texas Tech University, Lubbock, TX, USA
Funder
Tsinghua–Toyota Joint Research Fund
National Natural Science Foundation of China
National Key Research and Development Program of China
Texas Tech University Startup Fund
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/ielx7/9424/10355676/10070849.pdf?arnumber=10070849
Reference32 articles.
1. State-of-the-Art and Energy Management System of Lithium-Ion Batteries in Electric Vehicle Applications: Issues and Recommendations
2. Batteries and fuel cells for emerging electric vehicle markets
3. Challenges and opportunities towards fast-charging battery materials
4. Fast charging of lithium-ion batteries at all temperatures
5. Data-driven prediction of battery cycle life before capacity degradation
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