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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:

Developing an online data-driven approach for prognostics and health management of lithium-ion batteries
Sahar Khaleghi, Md Sazzad Hosen, Danial Karimi, et al.
Applied Energy (2021) Vol. 308, pp. 118348-118348
Closed Access | Times Cited: 119

Showing 1-25 of 119 citing articles:

A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries
Shunli Wang, Pu Ren, Paul Takyi‐Aninakwa, et al.
Energies (2022) Vol. 15, Iss. 14, pp. 5053-5053
Open Access | Times Cited: 105
Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms
Yupeng Wei, Dazhong Wu
Reliability Engineering & System Safety (2022) Vol. 230, pp. 108947-108947
Open Access | Times Cited: 86
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
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
Remaining useful life prediction and state of health diagnosis for lithium-ion batteries based on improved grey wolf optimization algorithm-deep extreme learning machine algorithm
Yifei Zhou, Shunli Wang, Yanxing Xie, et al.
Energy (2023) Vol. 285, pp. 128761-128761
Open Access | Times Cited: 42
A review on rapid state of health estimation of lithium-ion batteries in electric vehicles
Zuolu Wang, Xiaoyu Zhao, Lei Fu, et al.
Sustainable Energy Technologies and Assessments (2023) Vol. 60, pp. 103457-103457
Open Access | Times Cited: 40
Cloud-Based Artificial Intelligence Framework for Battery Management System
Dapai Shi, Jingyuan Zhao, Chika Eze, et al.
Energies (2023) Vol. 16, Iss. 11, pp. 4403-4403
Open Access | Times Cited: 39
SOH estimation for lithium-ion batteries: An improved GPR optimization method based on the developed feature extraction
Ye He, Wenyuan Bai, Lulu Wang, et al.
Journal of Energy Storage (2024) Vol. 83, pp. 110678-110678
Closed Access | Times Cited: 37
A CNN-SAM-LSTM hybrid neural network for multi-state estimation of lithium-ion batteries under dynamical operating conditions
Cheng Qian, Hongsheng Guan, Binghui Xu, et al.
Energy (2024) Vol. 294, pp. 130764-130764
Closed Access | Times Cited: 29
SOH early prediction of lithium-ion batteries based on voltage interval selection and features fusion
Simin Peng, Junchao Zhu, Tiezhou Wu, et al.
Energy (2024) Vol. 308, pp. 132993-132993
Closed Access | Times Cited: 27
Robust state of charge estimation of LiFePO4 batteries based on Sage_Husa adaptive Kalman filter and dynamic neural network
Meng Wei, Min Ye, C. Zhang, et al.
Electrochimica Acta (2024) Vol. 477, pp. 143778-143778
Closed Access | Times Cited: 24
Progress and challenges in ultrasonic technology for state estimation and defect detection of lithium-ion batteries
Yiyu Wang, Xin Lai, Quanwei Chen, et al.
Energy storage materials (2024) Vol. 69, pp. 103430-103430
Closed Access | Times Cited: 23
Lithium battery prognostics and health management for electric vehicle application – A perspective review
Roushan Kumar, Kaushik Das
Sustainable Energy Technologies and Assessments (2024) Vol. 65, pp. 103766-103766
Closed Access | Times Cited: 16
Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering
Xingjun Li, Dan Yu, Søren Byg Vilsen, et al.
Journal of Energy Chemistry (2024) Vol. 92, pp. 591-604
Open Access | Times Cited: 14
Risk analysis of lithium-ion battery accidents based on physics-informed data-driven Bayesian networks
Huixing Meng, Mengqian Hu, Zihan Kong, et al.
Reliability Engineering & System Safety (2024) Vol. 251, pp. 110294-110294
Closed Access | Times Cited: 14
Comparative study on thermal and gas characteristics of 26700 sodium-ion and lithium-ion batteries
Xu Huang, Hongling Jing, Ming Yang, et al.
Journal of Power Sources (2025) Vol. 631, pp. 236270-236270
Closed Access | Times Cited: 1
Explainability-driven model improvement for SOH estimation of lithium-ion battery
Fujin Wang, Zhibin Zhao, Zhi Zhai, et al.
Reliability Engineering & System Safety (2022) Vol. 232, pp. 109046-109046
Closed Access | Times Cited: 66
Comprehensive review of battery state estimation strategies using machine learning for battery Management Systems of Aircraft Propulsion Batteries
Tahmineh Raoofi, Melih Yıldız
Journal of Energy Storage (2022) Vol. 59, pp. 106486-106486
Closed Access | Times Cited: 48
A fast state-of-health estimation method using single linear feature for lithium-ion batteries
Mingjie Shi, Jun Xu, Chuanping Lin, et al.
Energy (2022) Vol. 256, pp. 124652-124652
Closed Access | Times Cited: 42
A two-stage data-driven approach to remaining useful life prediction via long short-term memory networks
Huixin Zhang, Xiaopeng Xi, Rong Pan
Reliability Engineering & System Safety (2023) Vol. 237, pp. 109332-109332
Closed Access | Times Cited: 37
Towards machine-learning driven prognostics and health management of Li-ion batteries. A comprehensive review
Sahar Khaleghi, Md Sazzad Hosen, Joeri Van Mierlo, et al.
Renewable and Sustainable Energy Reviews (2023) Vol. 192, pp. 114224-114224
Closed Access | Times Cited: 34
A multi-scale learning approach for remaining useful life prediction of lithium-ion batteries based on variational mode decomposition and Monte Carlo sampling
Meng Wei, Min Ye, Chuanwei Zhang, et al.
Energy (2023) Vol. 283, pp. 129086-129086
Closed Access | Times Cited: 24
State of health and remaining useful life prediction of lithium-ion batteries with conditional graph convolutional network
Yupeng Wei, Dazhong Wu
Expert Systems with Applications (2023) Vol. 238, pp. 122041-122041
Open Access | Times Cited: 22
Extending the electric vehicle battery first life: Performance beyond the current end of life threshold
Maite Etxandi-Santolaya, Lluc Canals Casals, Cristina Corchero
Heliyon (2024) Vol. 10, Iss. 4, pp. e26066-e26066
Open Access | Times Cited: 10
Batteries boost the internet of everything: technologies and potential orientations in renewable energy sources, new energy vehicles, energy interconnection and transmission
Wei Li, Rongguo Cheng, Akhil Garg, et al.
Sustainable Energy Grids and Networks (2024) Vol. 37, pp. 101273-101273
Closed Access | Times Cited: 7
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