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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Machine learning pipeline for battery state-of-health estimation
Darius Roman, Saurabh Saxena, Valentin Robu, et al.
Nature Machine Intelligence (2021) Vol. 3, Iss. 5, pp. 447-456
Closed Access | Times Cited: 386

Showing 1-25 of 386 citing articles:

Overview of batteries and battery management for electric vehicles
Wei Liu, Tobias Placke, K. T. Chau
Energy Reports (2022) Vol. 8, pp. 4058-4084
Open Access | Times Cited: 407
Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation
Jiangong Zhu, Yixiu Wang, Yuan Huang, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 392
Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries
Chade Lv, Xin Zhou, Lixiang Zhong, et al.
Advanced Materials (2021) Vol. 34, Iss. 25
Open Access | Times Cited: 258
Lithium-ion battery data and where to find it
Gonçalo dos Reis, Calum Strange, Mohit Yadav, et al.
Energy and AI (2021) Vol. 5, pp. 100081-100081
Open Access | Times Cited: 249
A comparative study of different features extracted from electrochemical impedance spectroscopy in state of health estimation for lithium-ion batteries
Bo Jiang, Jiangong Zhu, Xueyuan Wang, et al.
Applied Energy (2022) Vol. 322, pp. 119502-119502
Closed Access | Times Cited: 205
Data-Driven Battery State of Health Estimation Based on Random Partial Charging Data
Zhongwei Deng, Xiao Hu, Penghua Li, et al.
IEEE Transactions on Power Electronics (2021) Vol. 37, Iss. 5, pp. 5021-5031
Closed Access | Times Cited: 196
Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects
Yunhong Che, Xiaosong Hu, Xianke Lin, et al.
Energy & Environmental Science (2023) Vol. 16, Iss. 2, pp. 338-371
Open Access | Times Cited: 175
Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling
Huzaifa Rauf, Muhammad Khalid, Naveed Arshad
Renewable and Sustainable Energy Reviews (2021) Vol. 156, pp. 111903-111903
Closed Access | Times Cited: 173
Flexible battery state of health and state of charge estimation using partial charging data and deep learning
Jinpeng Tian, Rui Xiong, Weixiang Shen, et al.
Energy storage materials (2022) Vol. 51, pp. 372-381
Closed Access | Times Cited: 156
A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries
Kai Luo, Xiang Chen, Huiru Zheng, et al.
Journal of Energy Chemistry (2022) Vol. 74, pp. 159-173
Open Access | Times Cited: 153
A review of the recent progress in battery informatics
Chen Ling
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 134
Impedance-based forecasting of lithium-ion battery performance amid uneven usage
Penelope Jones, Ulrich Stimming, Alpha A. Lee
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 131
State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives
Xing Shu, Shuhang Shen, Jiangwei Shen, et al.
iScience (2021) Vol. 24, Iss. 11, pp. 103265-103265
Open Access | Times Cited: 127
Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale
Xiang Chen, Xinyan Liu, Xin Shen, et al.
Angewandte Chemie International Edition (2021) Vol. 60, Iss. 46, pp. 24354-24366
Closed Access | Times Cited: 122
Battery technologies and functionality of battery management system for EVs: Current status, key challenges, and future prospectives
Mohammad Waseem, Mumtaz Ahmad, Aasiya Parveen, et al.
Journal of Power Sources (2023) Vol. 580, pp. 233349-233349
Closed Access | Times Cited: 119
Machine learning for predicting battery capacity for electric vehicles
Jingyuan Zhao, Heping Ling, Jin Liu, et al.
eTransportation (2022) Vol. 15, pp. 100214-100214
Closed Access | Times Cited: 112
A machine learning-based framework for online prediction of battery ageing trajectory and lifetime using histogram data
Yizhou Zhang, Torsten Wik, John C. Bergstrom, et al.
Journal of Power Sources (2022) Vol. 526, pp. 231110-231110
Open Access | Times Cited: 107
Constant current charging time based fast state-of-health estimation for lithium-ion batteries
Chuanping Lin, Jun Xu, Mingjie Shi, et al.
Energy (2022) Vol. 247, pp. 123556-123556
Closed Access | Times Cited: 92
Battery health management using physics-informed machine learning: Online degradation modeling and remaining useful life prediction
Junchuan Shi, Alexis V. Rivera, Dazhong Wu
Mechanical Systems and Signal Processing (2022) Vol. 179, pp. 109347-109347
Open Access | Times Cited: 89
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
Venkat Pavan Nemani, Luca Biggio, Xun Huan, et al.
Mechanical Systems and Signal Processing (2023) Vol. 205, pp. 110796-110796
Open Access | Times Cited: 79
Integrating physics-based modeling and machine learning for degradation diagnostics of lithium-ion batteries
Adam Thelen, Yu Hui Lui, Sheng Shen, et al.
Energy storage materials (2022) Vol. 50, pp. 668-695
Closed Access | Times Cited: 76
State of health estimation of lithium-ion batteries with a temporal convolutional neural network using partial load profiles
Steffen Bockrath, Vincent Lorentz, Marco Pruckner
Applied Energy (2022) Vol. 329, pp. 120307-120307
Open Access | Times Cited: 76
State of health estimation for lithium-ion battery based on energy features
Dongliang Gong, Ying Gao, Yalin Kou, et al.
Energy (2022) Vol. 257, pp. 124812-124812
Closed Access | Times Cited: 74
An overview of data-driven battery health estimation technology for battery management system
Minzhi Chen, Guijun Ma, Weibo Liu, et al.
Neurocomputing (2023) Vol. 532, pp. 152-169
Closed Access | Times Cited: 73
An adaptive capacity estimation approach for lithium-ion battery using 10-min relaxation voltage within high state of charge range
Bo Jiang, Yuli Zhu, Jiangong Zhu, et al.
Energy (2022) Vol. 263, pp. 125802-125802
Closed Access | Times Cited: 70
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