Fast Remaining Capacity Estimation for Lithium‐ion Batteries Based on Short‐time Pulse Test and Gaussian Process Regression-Reference-Cited by-同舟云学术

Fast Remaining Capacity Estimation for Lithium‐ion Batteries Based on Short‐time Pulse Test and Gaussian Process Regression

Published:2022-06-28 Issue: Volume: Page:
ISSN:2575-0356
Container-title:ENERGY & ENVIRONMENTAL MATERIALS
language:en
Short-container-title:Energy & Environ Materials

Author:

Ran Aihua¹²,Cheng Ming¹²,Chen Shuxiao¹²,Liang Zheng¹²,Zhou Zihao¹²,Zhou Guangmin¹²^ORCID,Kang Feiyu¹²,Zhang Xuan¹²,Li Baohua²,Wei Guodan¹²

Affiliation:

1. Tsinghua‐Berkeley Shenzhen Institute (TBSI) Tsinghua University Shenzhen 518055 China

2. Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen 518055 China

Funder

Development and Reform Commission of Shenzhen Municipality

Innovationsfonden

Shenzhen Graduate School, Peking University

Publisher

Wiley

Subject

Energy (miscellaneous),Waste Management and Disposal,Environmental Science (miscellaneous),Water Science and Technology,General Materials Science,Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/eem2.12386

Reference53 articles.

1. Sorting, regrouping, and echelon utilization of the large-scale retired lithium batteries: A critical review

2. Battery state of charge estimation using a load-classifying neural network

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4. A deep learning method for online capacity estimation of lithium-ion batteries

5. Analysis of ageing inhomogeneities in lithium-ion battery systems

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