Random health indicator and shallow neural network based robust capacity estimation for lithium-ion batteries with different fast charging protocols-Reference-Cited by-同舟云学术

Random health indicator and shallow neural network based robust capacity estimation for lithium-ion batteries with different fast charging protocols

Published:2023-05 Issue: Volume:271 Page:127029
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Wang Qiao^ORCID,Ye Min,Wei Meng,Lian Gaoqi,Li Yan

Funder

Fundamental Research Funds for the Central Universities

Shanxi Provincial Key Research and Development Project

Publisher

Elsevier BV

Subject

General Energy,Pollution,Mechanical Engineering,Building and Construction,Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Civil and Structural Engineering

Reference44 articles.

1. Safety aspects of graphite negative electrode materials for lithium-ion batteries;Joho;J Electrochem Soc,2002

2. Intelligent algorithms and control strategies for battery management system in electric vehicles: progress, challenges and future outlook;Lipu;J Clean Prod,2021

3. Techno-economic analysis of a hybrid solar wind electric vehicle charging station in highway roads;Nishanthy;Int J Energy Res,2022

4. Fast self-heating battery with anti-aging awareness for freezing climates application;Xiong;Appl Energy,2022

5. Predicting the impact of formation protocols on battery lifetime immediately after manufacturing;Weng;Joule,2021

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Co-estimation of state of charge and capacity for battery packs in real electric vehicles with few representative cells and physics-informed machine learning;Energy;2024-10

2. Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering;Journal of Energy Chemistry;2024-05

3. Enhanced robust capacity estimation of lithium-ion batteries with unlabeled dataset and semi-supervised machine learning;Expert Systems with Applications;2024-03

4. Comparing Experimental Designs for Parameterizing Semi-Empirical and Deep Learning-Based Lithium-Ion Battery Aging Models;2024

5. Transferable data-driven capacity estimation for lithium-ion batteries with deep learning: A case study from laboratory to field applications;Applied Energy;2023-11