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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:

A data-driven approach based on deep neural networks for lithium-ion battery prognostics
Ahmet Kara
Neural Computing and Applications (2021) Vol. 33, Iss. 20, pp. 13525-13538
Closed Access | Times Cited: 59

Showing 1-25 of 59 citing articles:

Modeling, state of charge estimation, and charging of lithium‐ion battery in electric vehicle: A review
Maheshwari Adaikkappan, Nageswari Sathiyamoorthy
International Journal of Energy Research (2021) Vol. 46, Iss. 3, pp. 2141-2165
Open Access | Times Cited: 171
Developing an online data-driven approach for prognostics and health management of lithium-ion batteries
Sahar Khaleghi, Md Sazzad Hosen, Danial Karimi, et al.
Applied Energy (2021) Vol. 308, pp. 118348-118348
Closed Access | Times Cited: 119
Applications of artificial neural network based battery management systems: A literature review
Mehmet Kurucan, Mete Özbaltan, Zekí Yetgín, et al.
Renewable and Sustainable Energy Reviews (2023) Vol. 192, pp. 114262-114262
Closed Access | Times Cited: 65
Artificial Intelligence-based health diagnostic of Lithium-ion battery leveraging transient stage of constant current and constant voltage charging
Haokai Ruan, Zhongbao Wei, Wentao Shang, et al.
Applied Energy (2023) Vol. 336, pp. 120751-120751
Closed Access | Times Cited: 63
Research progress and application of deep learning in remaining useful life, state of health and battery thermal management of lithium batteries
Wenbin He, Zongze Li, Ting Liu, et al.
Journal of Energy Storage (2023) Vol. 70, pp. 107868-107868
Closed Access | Times Cited: 42
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 | Times Cited: 1
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
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: 34
Open access dataset, code library and benchmarking deep learning approaches for state-of-health estimation of lithium-ion batteries
Fujin Wang, Zhi Zhai, Bingchen Liu, et al.
Journal of Energy Storage (2023) Vol. 77, pp. 109884-109884
Closed Access | Times Cited: 23
Battery State-of-Health Estimation: A Step towards Battery Digital Twins
Vahid Safavi, Najmeh Bazmohammadi, Juan C. Vásquez, et al.
Electronics (2024) Vol. 13, Iss. 3, pp. 587-587
Open Access | Times Cited: 14
Hybrid modeling for vehicle lateral dynamics via AGRU with a dual-attention mechanism under limited data
Jianwei Chen, Chuanqiang Yu, Yafei Wang, et al.
Control Engineering Practice (2024) Vol. 151, pp. 106015-106015
Closed Access | Times Cited: 10
A Fault Diagnosis Method for Lithium-Ion Battery Packs Using Improved RBF Neural Network
Jia Wang, Shenglong Zhang, Xia Hu
Frontiers in Energy Research (2021) Vol. 9
Open Access | Times Cited: 41
Prognostics of Remaining Useful Life for Lithium-Ion Batteries Based on Hybrid Approach of Linear Pattern Extraction and Nonlinear Relationship Mining
Yingzhou Wang, Chenyang Hei, Hui Liu, et al.
IEEE Transactions on Power Electronics (2022) Vol. 38, Iss. 1, pp. 1054-1063
Closed Access | Times Cited: 32
Electric vehicle battery capacity degradation and health estimation using machine-learning techniques: a review
Kaushik Das, Roushan Kumar
Clean Energy (2023) Vol. 7, Iss. 6, pp. 1268-1281
Open Access | Times Cited: 20
An Overview of Remaining Useful Life Prediction of Battery Using Deep Learning and Ensemble Learning Algorithms on Data‐Dependent Models
Ch. Sravanthi, J. N. Chandra Sekhar, Chinna Alluraiah Nallolla, et al.
International Transactions on Electrical Energy Systems (2025) Vol. 2025, Iss. 1
Open Access
Deep learning-based anomaly-onset aware remaining useful life estimation of bearings
Pooja Kamat, Rekha Sugandhi, Satish Kumar
PeerJ Computer Science (2021) Vol. 7, pp. e795-e795
Open Access | Times Cited: 34
A physics-informed dynamic deep autoencoder for accurate state-of-health prediction of lithium-ion battery
Zhaoyi Xu, Yanjie Guo, Joseph H. Saleh
Neural Computing and Applications (2022) Vol. 34, Iss. 18, pp. 15997-16017
Closed Access | Times Cited: 27
Multi-scale deep neural network approach with attention mechanism for remaining useful life estimation
Ahmet Kara
Computers & Industrial Engineering (2022) Vol. 169, pp. 108211-108211
Closed Access | Times Cited: 26
Machine Learning Applications for Reliability Engineering: A Review
Mathieu Payette, Georges Abdul-Nour
Sustainability (2023) Vol. 15, Iss. 7, pp. 6270-6270
Open Access | Times Cited: 14
Daily reference evapotranspiration prediction for irrigation scheduling decisions based on the hybrid PSO-LSTM model
Weibing Jia, Yubin Zhang, Zhengying Wei, et al.
PLoS ONE (2023) Vol. 18, Iss. 4, pp. e0281478-e0281478
Open Access | Times Cited: 13
A Novel Competitive Temporal Convolutional Network for Remaining Useful Life Prediction of Rolling Bearings
Wei Wang, Gongbo Zhou, Guoqing Ma, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-12
Closed Access | Times Cited: 12
Employment of Artificial Intelligence (AI) Techniques in Battery Management System (BMS) for Electric Vehicles (EV): Issues and Challenges
Marwan Atef Badran, Siti Fauziah Toha
Pertanika journal of science & technology (2024) Vol. 32, Iss. 2, pp. 859-881
Open Access | Times Cited: 4
Dimensional-noise-aware battery lifetime prediction via an EM-TLS framework
Chenlong Yu, Ting Lu, Guohua Liu, et al.
Progress in Natural Science Materials International (2025)
Closed Access
Predictive Modeling of Electric Bicycle Battery Performance: Integrating Real-Time Sensor Data and Machine Learning Techniques
Catherine Rincón-Maya, Daniel Acosta-González, Fernando Guevara-Carazas, et al.
Sensors (2025) Vol. 25, Iss. 5, pp. 1392-1392
Open Access
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