An Overview of Data-Driven Methods for the State of Health Estimation-Reference-Cited by-同舟云学术

An Overview of Data-Driven Methods for the State of Health Estimation

Published:2023-06-06 Issue: Volume: Page:
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Container-title:2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
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Author:

Eleftheriadis Panagiotis¹,Giazitzis Spyridon¹,Ozmalatyalilar Cemay¹,Leva Sonia¹,Zich Ricardo¹

Affiliation:

1. Politecnico di Milano,Department of Energy,Milano,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10194576/10194600/10194864.pdf?arnumber=10194864

Reference32 articles.

1. Online State-of-Health Estimation for Li-Ion Battery Using Partial Charging Segment Based on Support Vector Machine

2. General Discharge Voltage Information Enabled Health Evaluation for Lithium-Ion Batteries

3. State of health prediction for lithiumion batteries using multiple-view feature fusion and support vector regression ensemble;ma;International Journal of Machine Learning and Cybernetics,2018

4. Online state-of-health estimation of lithium-ion battery based on dynamic parameter identification at multi timescale and support vector regression

5. A Dynamic Spatial-Temporal Attention-Based GRU Model With Healthy Features for State-of-Health Estimation of Lithium-Ion Batteries

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