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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:

Early prediction of remaining useful life for Lithium-ion batteries based on a hybrid machine learning method
Zheming Tong, Jiazhi Miao, Shuiguang Tong, et al.
Journal of Cleaner Production (2021) Vol. 317, pp. 128265-128265
Closed Access | Times Cited: 120

Showing 1-25 of 120 citing articles:

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: 172
Prognostics and health management of Lithium-ion battery using deep learning methods: A review
Ying Zhang, Yan‐Fu Li
Renewable and Sustainable Energy Reviews (2022) Vol. 161, pp. 112282-112282
Closed Access | Times Cited: 156
The development of machine learning-based remaining useful life prediction for lithium-ion batteries
Xingjun Li, Dan Yu, Søren Byg Vilsen, et al.
Journal of Energy Chemistry (2023) Vol. 82, pp. 103-121
Open Access | Times Cited: 85
State of charge estimation method by using a simplified electrochemical model in deep learning framework for lithium-ion batteries
Hanqing Yu, Lisheng Zhang, Wentao Wang, et al.
Energy (2023) Vol. 278, pp. 127846-127846
Closed Access | Times Cited: 44
Accurate capacity and remaining useful life prediction of lithium-ion batteries based on improved particle swarm optimization and particle filter
Hui Pang, Kaiqiang Chen, Yuanfei Geng, et al.
Energy (2024) Vol. 293, pp. 130555-130555
Closed Access | Times Cited: 43
A review of expert hybrid and co-estimation techniques for SOH and RUL estimation in battery management system with electric vehicle application
Turki Alsuwian, Shaheer Ansari, Muhammad Ammirrul Atiqi Mohd Zainuri, et al.
Expert Systems with Applications (2024) Vol. 246, pp. 123123-123123
Closed Access | Times Cited: 34
A hybrid deep learning approach for remaining useful life prediction of lithium-ion batteries based on discharging fragments
Yunpeng Liu, Bo Hou, Moin Ahmed, et al.
Applied Energy (2024) Vol. 358, pp. 122555-122555
Closed Access | Times Cited: 16
Machine Learning-Based Lithium Battery State of Health Prediction Research
Kun Li, Xinling Chen
Applied Sciences (2025) Vol. 15, Iss. 2, pp. 516-516
Open Access | Times Cited: 1
A data-driven interval forecasting model for building energy prediction using attention-based LSTM and fuzzy information granulation
Yue Li, Zheming Tong, Shuiguang Tong, et al.
Sustainable Cities and Society (2021) Vol. 76, pp. 103481-103481
Closed Access | Times Cited: 99
Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review
Alan G. Li, Alan C. West, Matthias Preindl
Applied Energy (2022) Vol. 316, pp. 119030-119030
Closed Access | Times Cited: 62
Remaining useful life prediction for lithium-ion battery storage system: A comprehensive review of methods, key factors, issues and future outlook
Shaheer Ansari, Afida Ayob, Molla Shahadat Hossain Lipu, et al.
Energy Reports (2022) Vol. 8, pp. 12153-12185
Open Access | Times Cited: 57
Machine learning-assisted multi-objective optimization of battery manufacturing from synthetic data generated by physics-based simulations
Marc Duquesnoy, Chaoyue Liu, Diana Zapata Dominguez, et al.
Energy storage materials (2022) Vol. 56, pp. 50-61
Open Access | Times Cited: 55
Degradation Curve Prediction of Lithium-Ion Batteries Based on Knee Point Detection Algorithm and Convolutional Neural Network
Muhammad Haris, Muhammad Noman Hasan, Shiyin Qin
IEEE Transactions on Instrumentation and Measurement (2022) Vol. 71, pp. 1-10
Closed Access | Times Cited: 53
State of charge, remaining useful life and knee point estimation based on artificial intelligence and Machine learning in lithium-ion EV batteries: A comprehensive review
Aryan Shah, Khushi Shah, Charmi Shah, et al.
Renewable energy focus (2022) Vol. 42, pp. 146-164
Closed Access | Times Cited: 52
Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization
Liqianyun Xu, Feng Wu, Renjie Chen, et al.
Energy storage materials (2023) Vol. 59, pp. 102785-102785
Closed Access | Times Cited: 39
Deep feature extraction in lifetime prognostics of lithium-ion batteries: Advances, challenges and perspectives
Chuan Li, Huahua Zhang, Ping Ding, et al.
Renewable and Sustainable Energy Reviews (2023) Vol. 184, pp. 113576-113576
Closed Access | Times Cited: 39
Advancements in Artificial Neural Networks for health management of energy storage lithium-ion batteries: A comprehensive review
Yuntao Zou, Zihui Lin, Dagang Li, et al.
Journal of Energy Storage (2023) Vol. 73, pp. 109069-109069
Closed Access | Times Cited: 37
Remaining useful life prediction via a deep adaptive transformer framework enhanced by graph attention network
Pengfei Liang, Ying Li, Bin Wang, et al.
International Journal of Fatigue (2023) Vol. 174, pp. 107722-107722
Closed Access | Times Cited: 36
IoB: Internet-of-batteries for electric Vehicles–Architectures, opportunities, and challenges
Heng Li, Muaaz Bin Kaleem, Zhijun Liu, et al.
Green Energy and Intelligent Transportation (2023) Vol. 2, Iss. 6, pp. 100128-100128
Open Access | Times Cited: 34
Early prediction of lithium-ion battery cycle life based on voltage-capacity discharge curves
Xiong Wei, Gang Xu, Yumei Li, et al.
Journal of Energy Storage (2023) Vol. 62, pp. 106790-106790
Open Access | Times Cited: 33
Early prediction of battery lifetime based on graphical features and convolutional neural networks
Ning He, Qiqi Wang, LU Zhen-feng, et al.
Applied Energy (2023) Vol. 353, pp. 122048-122048
Closed Access | Times Cited: 32
Lithium-ion battery state of health estimation using a hybrid model based on a convolutional neural network and bidirectional gated recurrent unit
Yahia Mazzi, Hicham Ben Sassi, Fatima Errahimi
Engineering Applications of Artificial Intelligence (2023) Vol. 127, pp. 107199-107199
Closed Access | Times Cited: 30
A Remaining Useful Life Prediction Method for Lithium-ion Battery Based on Temporal Transformer Network
Wenbin Song, Di Wu, Weiming Shen, et al.
Procedia Computer Science (2023) Vol. 217, pp. 1830-1838
Open Access | Times Cited: 28
Critical review on recently developed lithium and non-lithium anode-based solid-state lithium-ion batteries
Albina Jetybayeva, Douglas Aaron, Ilias Belharouak, et al.
Journal of Power Sources (2023) Vol. 566, pp. 232914-232914
Open Access | Times Cited: 24
A Physics-Constrained Bayesian neural network for battery remaining useful life prediction
David Najera-Flores, Zhen Hu, Mayank Chadha, et al.
Applied Mathematical Modelling (2023) Vol. 122, pp. 42-59
Open Access | Times Cited: 23
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