Data-driven systematic parameter identification of an electrochemical model for lithium-ion batteries with artificial intelligence-Reference-Cited by-同舟云学术

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Data-driven systematic parameter identification of an electrochemical model for lithium-ion batteries with artificial intelligence

Published:2022-01 Issue: Volume:44 Page:557-570
ISSN:2405-8297
Container-title:Energy Storage Materials
language:en
Short-container-title:Energy Storage Materials

Author:

Li Weihan,Demir Iskender,Cao Decheng,Jöst Dominik,Ringbeck Florian,Junker Mark,Sauer Dirk Uwe

Funder

Bundesministerium fur Bildung und Forschung Dienststelle Berlin

Horizon 2020 Framework Programme

Horizon 2020

European Commission

Bundesministerium für Bildung und Forschung

Publisher

Elsevier BV

Subject

Energy Engineering and Power Technology,General Materials Science,Renewable Energy, Sustainability and the Environment

Reference67 articles.

1. Performance and cost of materials for lithium-based rechargeable automotive batteries;Schmuch;Nature Energy,2018

2. Electrochemical model-based state estimation for lithium-ion batteries with adaptive unscented Kalman filter;Li;Journal of Power Sources,2020

3. Estimation of potentials in lithium-ion batteries using machine learning models;Li;IEEE Transactions on Control Systems Technology,2021

4. Physics-informed neural networks for electrode-level state estimation in lithium-ion batteries;Li;Journal of Power Sources,2021

5. Aging modes analysis and physical parameter identification based on a simplified electrochemical model for lithium-ion batteries;Li;Journal of Energy Storage,2020

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2. Unlocking the potential of unlabeled data: Self-supervised machine learning for battery aging diagnosis with real-world field data;Journal of Energy Chemistry;2024-12

3. Estimation of lithium-ion battery electrochemical properties from equivalent circuit model parameters using machine learning;Journal of Energy Storage;2024-10

4. Electrochemical model-based aging-adaptive fast charging of automotive lithium-ion cells;Applied Energy;2024-10

5. An adaptive semi-supervised self-learning method for online state of health estimation of lithium-ion batteries;Energy;2024-10

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