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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 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: 114

Showing 1-25 of 114 citing articles:

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: 65
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: 64
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: 63
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: 56
A comprehensive overview and comparison of parameter benchmark methods for lithium-ion battery application
Jichang Peng, Jinhao Meng, Ji Wu, et al.
Journal of Energy Storage (2023) Vol. 71, pp. 108197-108197
Closed Access | Times Cited: 46
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: 46
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: 43
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: 34
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: 28
AI on Wheels: Bibliometric Approach to Mapping of Research on Machine Learning and Deep Learning in Electric Vehicles
Adrian Domenteanu, Liviu‐Adrian Cotfas, Paul Diaconu, et al.
Electronics (2025) Vol. 14, Iss. 2, pp. 378-378
Open Access | Times Cited: 3
High-Throughput Screening of 6858 Compounds for Zinc-Ion Battery Cathodes via Hybrid Machine Learning Optimization
Y.S. Wudil, M.A. Gondal, Mohammed A. Al‐Osta
ACS Applied Materials & Interfaces (2025)
Closed Access | Times Cited: 2
Cloud-Based Deep Learning for Co-Estimation of Battery State of Charge and State of Health
Dapai Shi, Jingyuan Zhao, Zhenghong Wang, et al.
Energies (2023) Vol. 16, Iss. 9, pp. 3855-3855
Open Access | Times Cited: 41
Online health prognosis for lithium-ion batteries under dynamic discharge conditions over wide temperature range
Shizhuo Liu, Yuwei Nie, Aihua Tang, et al.
eTransportation (2023) Vol. 18, pp. 100296-100296
Closed Access | Times Cited: 40
Battery prognostics and health management for electric vehicles under industry 4.0
Jingyuan Zhao, Andrew Burke
Journal of Energy Chemistry (2023) Vol. 84, pp. 30-33
Closed Access | Times Cited: 36
Enabling battery digital twins at the industrial scale
Matthieu Dubarry, David A. Howey, Billy Wu
Joule (2023) Vol. 7, Iss. 6, pp. 1134-1144
Open Access | Times Cited: 35
A novel state of health estimation method for lithium-ion batteries based on constant-voltage charging partial data and convolutional neural network
Sizhe Chen, Zikang Liang, Haoliang Yuan, et al.
Energy (2023) Vol. 283, pp. 129103-129103
Closed Access | Times Cited: 30
Adaptive state of health estimation for lithium-ion batteries using impedance-based timescale information and ensemble learning
Yuli Zhu, Bo Jiang, Jiangong Zhu, et al.
Energy (2023) Vol. 284, pp. 129283-129283
Closed Access | Times Cited: 29
Estimate long-term impact on battery degradation by considering electric vehicle real-world end-use factors
Shiqi Ou
Journal of Power Sources (2023) Vol. 573, pp. 233133-233133
Open Access | Times Cited: 27
Spatial-Temporal Self-Attention Transformer Networks for Battery State of Charge Estimation
Dapai Shi, Jingyuan Zhao, Zhenghong Wang, et al.
Electronics (2023) Vol. 12, Iss. 12, pp. 2598-2598
Open Access | Times Cited: 27
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: 24
Optimizing Electric Vehicle Battery Life: A Machine Learning Approach for Sustainable Transportation
K. Karthick, Ravivarman Shanmugasundaram, R. Priyanka
World Electric Vehicle Journal (2024) Vol. 15, Iss. 2, pp. 60-60
Open Access | Times Cited: 15
Predictive machine learning in optimizing the performance of electric vehicle batteries: Techniques, challenges, and solutions
Vankamamidi S. Naresh, Guduru V. N. S. R. Ratnakara Rao, D. V. N. Prabhakar
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2024) Vol. 14, Iss. 5
Closed Access | Times Cited: 14
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: 13
The local lithium plating caused by anode crack defect in Li-ion battery
Yuebo Yuan, Hewu Wang, Xuebing Han, et al.
Applied Energy (2024) Vol. 361, pp. 122968-122968
Closed Access | Times Cited: 12
Comprehensive Review of Machine Learning, Deep Learning, and Digital Twin Data-Driven Approaches in Battery Health Prediction of Electric Vehicles
A. Pravin Renold, Neeraj Singh Kathayat
IEEE Access (2024) Vol. 12, pp. 43984-43999
Open Access | Times Cited: 12
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