Physics-Informed Recurrent Neural Network With Fractional-Order Gradients for State-of-Charge Estimation of Lithium-Ion Battery
Author:
Affiliation:
1. School of Vehicle and Mobility, Tsinghua University, Beijing, China
2. Department of Engineering, University of California at Merced, Merced, CA, USA
Funder
National Natural Science Foundation of China
Beijing Natural Science Foundation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Networks and Communications,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7433271/9663041/09910576.pdf?arnumber=9910576
Reference11 articles.
1. A review of key issues for control and management in battery and ultra-capacitor hybrid energy storage systems
2. Advanced diagnostics to evaluate heterogeneity in lithium-ion battery modules
3. Multistability of Fractional-Order Recurrent Neural Networks With Discontinuous and Nonmonotonic Activation Functions
4. The challenge and opportunity of battery lifetime prediction from field data
5. Physics-informed machine learning
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