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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:

Battery prognostics and health management from a machine learning perspective
Jingyuan Zhao, Xuning Feng, Quanquan Pang, et al.
Journal of Power Sources (2023) Vol. 581, pp. 233474-233474
Closed Access | Times Cited: 61

Showing 1-25 of 61 citing articles:

A review on physics-informed data-driven remaining useful life prediction: Challenges and opportunities
Huiqin Li, Zhengxin Zhang, Tianmei Li, et al.
Mechanical Systems and Signal Processing (2024) Vol. 209, pp. 111120-111120
Closed Access | Times Cited: 71
Specialized deep neural networks for battery health prognostics: Opportunities and challenges
Jingyuan Zhao, Xuebing Han, Minggao Ouyang, et al.
Journal of Energy Chemistry (2023) Vol. 87, pp. 416-438
Closed Access | Times Cited: 59
Battery safety: Machine learning-based prognostics
Jingyuan Zhao, Xuning Feng, Quanquan Pang, et al.
Progress in Energy and Combustion Science (2024) Vol. 102, pp. 101142-101142
Open Access | Times Cited: 59
Battery safety: Fault diagnosis from laboratory to real world
Jingyuan Zhao, Xuning Feng, Manh‐Kien Tran, et al.
Journal of Power Sources (2024) Vol. 598, pp. 234111-234111
Open Access | Times Cited: 49
Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks
Jingyuan Zhao, Xuning Feng, Junbin Wang, et al.
Applied Energy (2023) Vol. 352, pp. 121949-121949
Closed Access | Times Cited: 45
Machine learning for battery systems applications: Progress, challenges, and opportunities
Zahra Nozarijouybari, Hosam K. Fathy
Journal of Power Sources (2024) Vol. 601, pp. 234272-234272
Closed Access | Times Cited: 29
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: 27
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: 35
Battery health diagnostics: Bridging the gap between academia and industry
Zhenghong Wang, Dapai Shi, Jingyuan Zhao, et al.
eTransportation (2023) Vol. 19, pp. 100309-100309
Closed Access | Times Cited: 23
A novel method for remaining useful life of solid-state lithium-ion battery based on improved CNN and health indicators derivation
Yan Ma, Zhenxi Wang, Jinwu Gao, et al.
Mechanical Systems and Signal Processing (2024) Vol. 220, pp. 111646-111646
Closed Access | Times Cited: 15
Recent Advances in Thermal Management Strategies for Lithium-Ion Batteries: A Comprehensive Review
Yadyra Ortiz, Paúl Arévalo, Diego Patricio Peña Banegas, et al.
Batteries (2024) Vol. 10, Iss. 3, pp. 83-83
Open Access | Times Cited: 14
Communications and Data Science for the Success of Vehicle-to-Grid Technologies: Current State and Future Trends
Noelia Uribe-Pérez, Amaia González‐Garrido, Alexander Gallarreta, et al.
Electronics (2024) Vol. 13, Iss. 10, pp. 1940-1940
Open Access | Times Cited: 12
Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives
Adam Thelen, Xun Huan, Noah H. Paulson, et al.
npj Materials Sustainability (2024) Vol. 2, Iss. 1
Open Access | Times Cited: 12
Artificial intelligence-driven real-world battery diagnostics
Jingyuan Zhao, Xudong Qu, Yuyan Wu, et al.
Energy and AI (2024) Vol. 18, pp. 100419-100419
Open Access | Times Cited: 11
Specialized Convolutional Transformer Networks for Estimating Battery Health via Transfer Learning
Jingyuan Zhao, Zhenghong Wang
Energy storage materials (2024) Vol. 71, pp. 103668-103668
Closed Access | Times Cited: 10
Vehicle choice modeling for emerging zero-emission medium- and heavy-duty vehicle markets in California
Jingyuan Zhao, Andrew Burke, Marshall R. Miller, et al.
Transportation Research Interdisciplinary Perspectives (2024) Vol. 26, pp. 101169-101169
Open Access | Times Cited: 9
Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review
Arash Mousaei, Yahya Naderi, I. Safak Bayram
IEEE Access (2024) Vol. 12, pp. 43255-43283
Open Access | Times Cited: 8
Vehicle-cloud-collaborated prognosis and health management for lithium-ion batteries: framework, technics and perspective
Sida Zhou, Gao Zichao, Honglei Dong, et al.
Energy storage materials (2024) Vol. 70, pp. 103531-103531
Closed Access | Times Cited: 8
Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries
Seyed Saeed Madani, Carlos Ziebert, Parisa Vahdatkhah, et al.
Batteries (2024) Vol. 10, Iss. 6, pp. 204-204
Open Access | Times Cited: 8
Big data-driven prognostics and health management of lithium-ion batteries:A review
Kui Chen, Yang Luo, Zhou Long, et al.
Renewable and Sustainable Energy Reviews (2025) Vol. 214, pp. 115522-115522
Closed Access | Times Cited: 1
Optimized XGBoost modeling for accurate battery capacity degradation prediction
Sadiqa Jafari, Ji-Hyeok Yang, Yung-Cheol Byun
Results in Engineering (2024), pp. 102786-102786
Open Access | Times Cited: 7
Battery Engineering Safety Technologies (BEST): Mechanisms, Modes, Metrics, Modelling and Mitigation
Jingyuan Zhao, Zhilong Lv, Di Li, et al.
eTransportation (2024), pp. 100364-100364
Closed Access | Times Cited: 7
Cross-material battery capacity estimation using hybrid-model fusion transfer learning
Jingyuan Zhao, Xudong Qu, Xuebing Han, et al.
Journal of Power Sources (2024) Vol. 625, pp. 235674-235674
Closed Access | Times Cited: 7
Machine learning for full lifecycle management of lithium-ion batteries
Qiangxiang Zhai, Hongmin Jiang, Nengbing Long, et al.
Renewable and Sustainable Energy Reviews (2024) Vol. 202, pp. 114647-114647
Closed Access | Times Cited: 5
Enhanced multi-constraint dung beetle optimization-kernel extreme learning machine for lithium-ion battery state of health estimation with adaptive enhancement ability
Daijiang Mo, Shunli Wang, Yongcun Fan, et al.
Energy (2024) Vol. 307, pp. 132723-132723
Closed Access | Times Cited: 5
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