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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:

Remaining Useful Life Prediction of Lithium-Ion Battery via a Sequence Decomposition and Deep Learning Integrated Approach
Zhang Chen, Liqun Chen, Wenjing Shen, et al.
IEEE Transactions on Vehicular Technology (2021) Vol. 71, Iss. 2, pp. 1466-1479
Closed Access | Times Cited: 53

Showing 1-25 of 53 citing articles:

Improved singular filtering-Gaussian process regression-long short-term memory model for whole-life-cycle remaining capacity estimation of lithium-ion batteries adaptive to fast aging and multi-current variations
Shunli Wang, Fan Wu, Paul Takyi‐Aninakwa, et al.
Energy (2023) Vol. 284, pp. 128677-128677
Open Access | Times Cited: 211
Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects
Yunhong Che, Xiaosong Hu, Xianke Lin, et al.
Energy & Environmental Science (2023) Vol. 16, Iss. 2, pp. 338-371
Open Access | Times Cited: 172
Adaptive self-attention LSTM for RUL prediction of lithium-ion batteries
Zhuqing Wang, Ning Liu, Chilian Chen, et al.
Information Sciences (2023) Vol. 635, pp. 398-413
Closed Access | Times Cited: 74
A Review of Deep Learning Algorithms and Their Applications in Healthcare
Hussein Abdel-Jaber, Disha Devassy, Azhar Al Salam, et al.
Algorithms (2022) Vol. 15, Iss. 2, pp. 71-71
Open Access | Times Cited: 68
A Hybrid Battery Equivalent Circuit Model, Deep Learning, and Transfer Learning for Battery State Monitoring
Su Shaosen, Wei Li, Jianhui Mou, et al.
IEEE Transactions on Transportation Electrification (2022) Vol. 9, Iss. 1, pp. 1113-1127
Closed Access | Times Cited: 67
Improved Multiple Feature-Electrochemical Thermal Coupling Modeling of Lithium-Ion Batteries at Low-Temperature with Real-Time Coefficient Correction
Shunli Wang, Haiying Gao, Paul Takyi‐Aninakwa, et al.
Protection and Control of Modern Power Systems (2024) Vol. 9, Iss. 3, pp. 157-173
Open Access | Times Cited: 27
Recent advancement of remaining useful life prediction of lithium-ion battery in electric vehicle applications: A review of modelling mechanisms, network configurations, factors, and outstanding issues
M. S. Reza, M. Mannan, Muhamad Mansor, et al.
Energy Reports (2024) Vol. 11, pp. 4824-4848
Open Access | Times Cited: 20
A hybrid deep learning approach for remaining useful life prediction of lithium-ion batteries based on discharging fragments
Yunpeng Liu, Bo Hou, Moin Ahmed, et al.
Applied Energy (2024) Vol. 358, pp. 122555-122555
Closed Access | Times Cited: 16
Enhancing electric vehicle battery lifespan: integrating active balancing and machine learning for precise RUL estimation
Yara A. Sultan, Abdelfattah A. Eladl, Mohamed Hassan, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
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
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
Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Data Preprocessing and Improved ELM
Weili Wu, Shuangshuang Lu
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-14
Closed Access | Times Cited: 29
A hybrid framework for predicting the remaining useful life of battery using Gaussian process regression
Jiabo Li, Min Ye, Yan Wang, et al.
Journal of Energy Storage (2023) Vol. 66, pp. 107513-107513
Closed Access | Times Cited: 25
A Variational Bayesian Inference-Based En-Decoder Framework for Traffic Flow Prediction
Jianlei Kong, Xiaomeng Fan, Xue‐Bo Jin, et al.
IEEE Transactions on Intelligent Transportation Systems (2023) Vol. 25, Iss. 3, pp. 2966-2975
Closed Access | Times Cited: 24
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
A change point detection integrated remaining useful life estimation model under variable operating conditions
Anushiya Arunan, Yan Qin, Xiaoli Li, et al.
Control Engineering Practice (2024) Vol. 144, pp. 105840-105840
Open Access | Times Cited: 9
Capacity and remaining useful life prediction for lithium-ion batteries based on sequence decomposition and a deep-learning network
Zili Wang, Yonglu Liu, Fen Wang, et al.
Journal of Energy Storage (2023) Vol. 72, pp. 108085-108085
Closed Access | Times Cited: 19
A CNN-GRU Approach to the Accurate Prediction of Batteries’ Remaining Useful Life from Charging Profiles
Sadiqa Jafari, Yung-Cheol Byun
Computers (2023) Vol. 12, Iss. 11, pp. 219-219
Open Access | Times Cited: 16
Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study
Mohamed Massaoudi, Haitham Abu‐Rub, Ali Ghrayeb
IEEE Open Journal of Industry Applications (2024) Vol. 5, pp. 43-62
Open Access | Times Cited: 7
Advanced Machine Learning and Deep Learning Approaches for Estimating the Remaining Life of EV Batteries—A Review
Daniel H. de la Iglesia, Carlos Chinchilla Corbacho, Jorge Zakour Dib, et al.
Batteries (2025) Vol. 11, Iss. 1, pp. 17-17
Open Access
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
Prior task aware-augmented meta learning for early state-of-health estimation of lithium-ion batteries
Jing Yang, Minglan Zhang, Xiaomin Wang
Energy (2025), pp. 135648-135648
Closed Access
Hybrid Data-Driven Approach for Predicting the Remaining Useful Life of Lithium-Ion Batteries
Y. G. Li, Lei Li, Runze Mao, et al.
IEEE Transactions on Transportation Electrification (2023) Vol. 10, Iss. 2, pp. 2789-2805
Closed Access | Times Cited: 14
Long-term Traffic Flow Prediction using Stochastic Configuration Networks for Smart Cities
Yuqi Lin
IECE transactions on intelligent systematics. (2024) Vol. 1, Iss. 2, pp. 79-90
Closed Access | Times Cited: 4
A Hybrid Data-driven Granular Model for Battery Remaining Useful Life Prediction
TaiLong Jing, Sheng Du, Cong Wang, et al.
IEEE Transactions on Instrumentation and Measurement (2025) Vol. 74, pp. 1-12
Closed Access
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