Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method-Reference-Cited by-同舟云学术

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Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method

Published:2024-01 Issue: Volume:353 Page:121991
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Fu Shiyi,Tao Shengyu,Fan Hongtao,He Kun,Liu Xutao,Tao Yulin,Zuo Junxiong,Zhang Xuan,Wang Yu,Sun Yaojie

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference47 articles.

1. The development and future of lithium ion batteries;Blomgren;J Electrochem Soc,2017

2. Experimental investigation and visualization on thermal runaway of hard prismatic lithium-ion batteries used in smart phones;Duh;J Therm Anal Calorim,2018

3. A review of lithium-ion battery for electric vehicle applications and beyond;Chen;Energy Procedia,2019

4. Modeling of lithium-ion battery for energy storage system simulation;Chen,2009

5. A review of lithium-ion battery safety concerns: the issues, strategies, and testing standards;Chen;J Energy Chem,2021

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2. Refined lithium-ion battery state of health estimation with charging segment adjustment;Applied Energy;2024-12

3. Enhancing capacity estimation of retired electric vehicle lithium-ion batteries through transfer learning from electrochemical impedance spectroscopy;eTransportation;2024-12

4. Adaptable capacity estimation of lithium-ion battery based on short-duration random constant-current charging voltages and convolutional neural networks;Energy;2024-10

5. Feature-enhanced deep learning method for electric vehicle charging demand probabilistic forecasting of charging station;Applied Energy;2024-10

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