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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:

Adaptive evolution enhanced physics-informed neural networks for time-variant health prognosis of lithium-ion batteries
Bo Sun, Junlin Pan, Zeyu Wu, et al.
Journal of Power Sources (2022) Vol. 556, pp. 232432-232432
Closed Access | Times Cited: 24

Showing 24 citing articles:

Estimation of Lithium-ion Battery Discharge Capacity by Integrating Optimized Explainable-AI and Stacked LSTM Model
Vinay Vakharia, Milind Shah, Pranav Nair, et al.
Batteries (2023) Vol. 9, Iss. 2, pp. 125-125
Open Access | Times Cited: 61
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: 48
Integrating mechanism and machine learning based capacity estimation for LiFePO4 batteries under slight overcharge cycling
Meng Wei, Min Ye, Chuanwei Zhang, et al.
Energy (2024) Vol. 296, pp. 131208-131208
Closed Access | Times Cited: 18
Application of physics-informed neural networks in fault diagnosis and fault-tolerant control design for electric vehicles: A review
Arslan Ahmed Amin, Amir Zaki Mubarak, Saba Waseem
Measurement (2025), pp. 116728-116728
Closed Access | Times Cited: 1
A Generic physics-informed machine learning framework for battery remaining useful life prediction using small early-stage lifecycle data
Weikun Deng, Hung Lê, Khanh T.P. Nguyen, et al.
Applied Energy (2025) Vol. 384, pp. 125314-125314
Closed Access | Times Cited: 1
Lithium-ion battery health assessment method based on belief rule base with interpretability
Peng Han, Wei He, You Cao, et al.
Applied Soft Computing (2023) Vol. 138, pp. 110160-110160
Closed Access | Times Cited: 33
Accurate Model Parameter Identification to Boost Precise Aging Prediction of Lithium‐Ion Batteries: A Review
Shicong Ding, Yiding Li, Haifeng Dai, et al.
Advanced Energy Materials (2023) Vol. 13, Iss. 39
Open Access | Times Cited: 30
Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation
Soumya Singh, Yvonne Eboumbou Ebongue, Shahed Rezaei, et al.
Batteries (2023) Vol. 9, Iss. 6, pp. 301-301
Open Access | Times Cited: 23
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: 21
Remaining useful life prediction and cycle life test optimization for multiple-formula battery: A method based on multi-source transfer learning
Dengwei Song, Yujie Cheng, Zhou An, et al.
Reliability Engineering & System Safety (2024) Vol. 249, pp. 110166-110166
Closed Access | Times Cited: 8
Artificial intelligence-based data-driven prognostics in industry: A survey
Mohamed A. El-Brawany, Dina A. Ibrahim, Hamdy K. Elminir, et al.
Computers & Industrial Engineering (2023) Vol. 184, pp. 109605-109605
Closed Access | Times Cited: 21
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: 6
Status, challenges, and promises of data‐driven battery lifetime prediction under cyber‐physical system context
Yang Liu, Sihui Chen, Peiyi Li, et al.
IET Cyber-Physical Systems Theory & Applications (2024) Vol. 9, Iss. 3, pp. 207-217
Open Access | Times Cited: 5
Probabilistic remaining useful life prediction without lifetime labels: A Bayesian deep learning and stochastic process fusion method
Junlin Pan, Bo Sun, Zeyu Wu, et al.
Reliability Engineering & System Safety (2024) Vol. 250, pp. 110313-110313
Closed Access | Times Cited: 5
Physics-informed Machine Learning for Accurate SOH Estimation of Lithium-ion Batteries Considering Various Temperatures and Operating Conditions
Chunsong Lin, Xianguo Tuo, Longxing Wu, et al.
Energy (2025), pp. 134937-134937
Closed Access
A review on full-, zero-, and partial-knowledge based predictive models for industrial applications
Stefano Zampini, Guido Parodi, Luca Oneto, et al.
Information Fusion (2025), pp. 102996-102996
Open Access
A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks
Renato Giorgiani do Nascimento, Felipe Viana, Matteo Corbetta, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 14
A review of physics-based learning for system health management
Samir Khan, Takehisa Yairi, Seiji Tsutsumi, et al.
Annual Reviews in Control (2024) Vol. 57, pp. 100932-100932
Closed Access | Times Cited: 4
A hybrid probabilistic battery health management approach for robust inspection drone operations
Jokin Alcibar, Jose Ignacio Aizpurua, Ekhi Zugasti, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 146, pp. 110246-110246
Closed Access
Remaining useful life prediction of lithium-ion batteries via an EIS based deep learning approach
Jie Li, Shiming Zhao, Md Sipon Miah, et al.
Energy Reports (2023) Vol. 10, pp. 3629-3638
Open Access | Times Cited: 11
Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model
Wenqian Chen, Yucheng Fu, Panos Stinis
Journal of Power Sources (2023) Vol. 584, pp. 233548-233548
Open Access | Times Cited: 9
Remaining Useful Life Prediction for Two-Phase Hybrid Deteriorating Lithium-Ion Batteries Using Wiener Process
Xuemiao Cui, Jiping Lu, Yafeng Han
IEEE Access (2024) Vol. 12, pp. 43575-43599
Open Access | Times Cited: 1
Synergizing physics and machine learning for advanced battery management
Manashita Borah, Qiao Wang, Scott Moura, et al.
Communications Engineering (2024) Vol. 3, Iss. 1
Open Access | Times Cited: 1
AN EVALUATION METHOD OF AN ENERGY CONSUMPTION AS AN OPERATION PARAMETER IN A CYBER-PHYSICAL SYSTEM
Kenzhegali Nurgaliyev, Akylbek Tokhmetov, Liliya Tanchenko
Scientific Journal of Astana IT University (2024), pp. 30-40
Open Access
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