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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:

End-cloud collaboration method enables accurate state of health and remaining useful life online estimation in lithium-ion batteries
Bin Ma, Lisheng Zhang, Hanqing Yu, et al.
Journal of Energy Chemistry (2023) Vol. 82, pp. 1-17
Closed Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

A review of data-driven whole-life state of health prediction for lithium-ion batteries: Data preprocessing, aging characteristics, algorithms, and future challenges
Yanxin Xie, Shunli Wang, Gexiang Zhang, et al.
Journal of Energy Chemistry (2024) Vol. 97, pp. 630-649
Closed Access | Times Cited: 25
Insights and reviews on battery lifetime prediction from research to practice
Xudong Qu, Dapai Shi, Jingyuan Zhao, et al.
Journal of Energy Chemistry (2024) Vol. 94, pp. 716-739
Closed Access | Times Cited: 24
Battery pack capacity estimation for electric vehicles based on enhanced machine learning and field data
Qingguang Qi, Wenxue Liu, Zhongwei Deng, et al.
Journal of Energy Chemistry (2024) Vol. 92, pp. 605-618
Closed Access | Times Cited: 17
Lithium-ion battery degradation trajectory early prediction with synthetic dataset and deep learning
Mingqiang Lin, Yuqiang You, Jinhao Meng, et al.
Journal of Energy Chemistry (2023) Vol. 85, pp. 534-546
Closed Access | Times Cited: 31
A self‐adaptive, data‐driven method to predict the cycling life of lithium‐ion batteries
Chao Han, Yuchen Gao, Xiang Chen, et al.
InfoMat (2024) Vol. 6, Iss. 4
Open Access | Times Cited: 13
An end-cloud collaboration approach for online state-of-health estimation of lithium-ion batteries based on multi-feature and transformer
Wentao Wang, Kaiyi Yang, Lisheng Zhang, et al.
Journal of Power Sources (2024) Vol. 608, pp. 234669-234669
Closed Access | Times Cited: 12
Efficient interaction of indeno carbazole and alkoxy side chains with enormous differences in polarity achieve highly conductive and longevous anion exchange membranes
Na Li, Jialin Zhao, Yan Wang, et al.
Journal of Membrane Science (2024) Vol. 701, pp. 122761-122761
Closed Access | Times Cited: 9
A machine learning framework for remaining useful lifetime prediction of li-ion batteries using diverse neural networks
Junghwan Lee, Huanli Sun, Yongshan Liu, et al.
Energy and AI (2023) Vol. 15, pp. 100319-100319
Open Access | Times Cited: 17
Stacking integrated learning model via ELM and GRU with mixture correntropy loss for robust state of health estimation of lithium-ion batteries
Jingsong Xue, Wentao Ma, Xiaoyang Feng, et al.
Energy (2023) Vol. 284, pp. 129279-129279
Closed Access | Times Cited: 16
An end-cloud collaboration approach for state-of-health estimation of lithium-ion batteries based on bi-LSTM with collaboration of multi-feature and attention mechanism
Pengchang Jiang, Tianyi Zhang, Guangjie Huang, et al.
International Journal of Green Energy (2024) Vol. 21, Iss. 10, pp. 2205-2217
Open Access | Times Cited: 5
Multiscale modeling for enhanced battery health analysis: Pathways to longevity
Kaiyi Yang, Lisheng Zhang, Wentao Wang, et al.
Carbon Neutralization (2024) Vol. 3, Iss. 3, pp. 348-385
Open Access | Times Cited: 5
Multi-model ensemble learning for battery state-of-health estimation: Recent advances and perspectives
Chuanping Lin, Jun Xu, Delong Jiang, et al.
Journal of Energy Chemistry (2024)
Closed Access | Times Cited: 5
Hybrid HGRN-SCSO technique for enhanced prediction of remaining useful life in EV batteries
C. N. Pratheeba, S. Praveen Kumar
Electrical Engineering (2025)
Closed Access
State of the Art in Electric Batteries’ State-of-Health (SoH) Estimation with Machine Learning: A Review
Giovane Ronei Sylvestrin, Joylan Nunes Maciel, Márcio Luís Munhoz Amorim, et al.
Energies (2025) Vol. 18, Iss. 3, pp. 746-746
Open Access
Advanced health state intelligent diagnosis of lithium-ion batteries based on CNN-WNN-WBiLSTM model with attention mechanism
Walid Mchara, Mohamed Abdellatif Khalfa, Lazhar Manai
Automatika (2025) Vol. 66, Iss. 2, pp. 154-173
Open Access
Stochastic state of health estimation for lithium-ion batteries with automated feature fusion using quantum convolutional neural network
Liang Chen, Shengyu Tao, Xinghao Huang, et al.
Journal of Energy Chemistry (2025)
Closed Access
Physics-informed ensemble deep learning framework for improving state of charge estimation of lithium-ion batteries
Hanqing Yu, Zhengjie Zhang, Kaiyi Yang, et al.
Journal of Energy Storage (2023) Vol. 73, pp. 108915-108915
Closed Access | Times Cited: 14
Deep learning to predict battery voltage behavior after uncertain cycling-induced degradation
Jiahuan Lu, Rui Xiong, Jinpeng Tian, et al.
Journal of Power Sources (2023) Vol. 581, pp. 233473-233473
Closed Access | Times Cited: 11
Enhanced vision-transformer integrating with semi-supervised transfer learning for state of health and remaining useful life estimation of lithium-ion batteries
Ya‐Xiong Wang, Shangyu Zhao, Shiquan Wang, et al.
Energy and AI (2024) Vol. 17, pp. 100405-100405
Open Access | Times Cited: 3
Parameter identification and state of charge estimation for lithium-ion batteries under wide temperature range
Kaiqiang Chen, Hui Pang, Wenzhi Nan, et al.
Ionics (2023) Vol. 30, Iss. 2, pp. 847-862
Closed Access | Times Cited: 9
Multi-scenario surface temperature estimation in lithium-ion batteries with transfer learning and LGT augmentation
Yuqiang You, Mingqiang Lin, Jinhao Meng, et al.
Energy (2024) Vol. 304, pp. 132065-132065
Closed Access | Times Cited: 2
An end-cloud collaboration for state-of-charge estimation of lithium-ion batteries based on extended Kalman filter and convolutional neural network (CNN)—long short-term memory (LSTM)—attention mechanism (AM)
Pengchang Jiang, Hongxiang Wang, Guangjie Huang, et al.
Journal of Renewable and Sustainable Energy (2024) Vol. 16, Iss. 2
Closed Access | Times Cited: 2
Market Operation of Energy Storage System in Smart Grid: A Review
Li Deng, Jiafei Huan, Wei Wang, et al.
Energy Engineering (2024) Vol. 121, Iss. 6, pp. 1403-1437
Open Access | Times Cited: 1
State-of-health estimation for lithium-ion batteries based on Bi-LSTM-AM and LLE feature extraction
Wentao Wang, Gaoyuan Yang, Muxi Li, et al.
Frontiers in Energy Research (2023) Vol. 11
Open Access | Times Cited: 2
Data‐Driven Online Prognosis of Rechargeable Batteries: Prospect and Perspective
K. Liu, T. Wang, Xinyan Liu, et al.
Batteries & Supercaps (2024) Vol. 7, Iss. 3
Closed Access
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