Transfer Learning With Deep Neural Network for Capacity Prediction of Li-Ion Batteries Using EIS Measurement
Author:
Affiliation:
1. Department of Electrical Engineering and Computer Science, York University, Toronto, ON, Canada
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology,Transportation,Automotive Engineering
Link
http://xplorestaging.ieee.org/ielx7/6687316/10050348/09762708.pdf?arnumber=9762708
Reference32 articles.
1. A Lithium-Ion Battery-in-the-Loop Approach to Test and Validate Multiscale Dual H Infinity Filters for State-of-Charge and Capacity Estimation
2. A novel framework for Lithium-ion battery modeling considering uncertainties of temperature and aging
3. A comparative study of model-based capacity estimation algorithms in dual estimation frameworks for lithium-ion batteries under an accelerated aging test
4. Co-Estimation of State of Charge and State of Health for Lithium-Ion Batteries Based on Fractional-Order Calculus
5. A Scalable Active Battery Management System With Embedded Real-Time Electrochemical Impedance Spectroscopy
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2. Predictive Approach for Age and RUL Estimation of Lithium-Ion Batteries in EVs based on Capacitance;2023 IEEE International Conference on Power Electronics, Smart Grid, and Renewable Energy (PESGRE);2023-12-17
3. Transfer Learning with Deep Neural Network toward the Prediction of Wake Flow Characteristics of Containerships;Journal of Marine Science and Engineering;2023-09-29
4. Application of Electrochemical Impedance Spectroscopy to Degradation and Aging Research of Lithium-Ion Batteries;The Journal of Physical Chemistry C;2023-03-01
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