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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 Remaining Useful Life Prediction Method for Lithium-ion Battery Based on Temporal Transformer Network
Wenbin Song, Di Wu, Weiming Shen, et al.
Procedia Computer Science (2023) Vol. 217, pp. 1830-1838
Open Access | Times Cited: 28

Showing 1-25 of 28 citing articles:

Perspectives and challenges for future lithium-ion battery control and management
Yujie Wang, Xingchen Zhang, Kaiquan Li, et al.
eTransportation (2023) Vol. 18, pp. 100260-100260
Closed Access | Times Cited: 95
AttMoE: Attention with Mixture of Experts for remaining useful life prediction of lithium-ion batteries
Daoquan Chen, Xiuze Zhou
Journal of Energy Storage (2024) Vol. 84, pp. 110780-110780
Closed Access | Times Cited: 17
Advancements in Artificial Neural Networks for health management of energy storage lithium-ion batteries: A comprehensive review
Yuntao Zou, Zihui Lin, Dagang Li, et al.
Journal of Energy Storage (2023) Vol. 73, pp. 109069-109069
Closed Access | Times Cited: 37
State-of-health estimation for lithium-ion battery via an evolutionary Stacking ensemble learning paradigm of random vector functional link and active-state-tracking long–short-term memory neural network
Yue Zhang, Yeqin Wang, Chu Zhang, et al.
Applied Energy (2023) Vol. 356, pp. 122417-122417
Closed Access | Times Cited: 18
A Battery Prognostics and Health Management Technique Based on Knee Critical Interval and Linear Complexity Self-Attention Transformer in Electric Vehicles
Yan Ma, Jiaqi Li, Yunfeng Hu, et al.
IEEE Transactions on Intelligent Transportation Systems (2024) Vol. 25, Iss. 8, pp. 10216-10230
Closed Access | Times Cited: 4
Hybrid Neural Networks for Enhanced Predictions of Remaining Useful Life in Lithium-Ion Batteries
Alireza Rastegarpanah, M. Salman Asif, Rustam Stolkin
Batteries (2024) Vol. 10, Iss. 3, pp. 106-106
Open Access | Times Cited: 4
Opportunities and Challenges in Transformer Neural Networks for Battery State Estimation: Charge, Health, Lifetime, and Safety
Jingyuan Zhao, Xuebing Han, Yuyan Wu, et al.
Journal of Energy Chemistry (2024) Vol. 102, pp. 463-496
Closed Access | Times Cited: 4
Prediction of Lithium-ion Battery Degradation Trajectory in Electric Vehicles Under Real-World Scenarios
Fang Li, Haonan Feng, Yongjun Min, et al.
Energy (2025), pp. 134663-134663
Closed Access
Prediction of the Remaining Useful Life of Lithium–Ion Batteries Based on Mode Decomposition and ED-LSTM
Bowen Song, Guangzhao Yue, Dong Guo, et al.
Batteries (2025) Vol. 11, Iss. 3, pp. 86-86
Open Access
A dual-method approach using autoencoders and transductive learning for remaining useful life estimation
Jing Yang, Nika Anoosha Boroojeni, Mehran Kazemi Chahardeh, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 147, pp. 110285-110285
Closed Access
Accurate remaining useful life estimation of lithium-ion batteries in electric vehicles based on a measurable feature-based approach with explainable AI
Sadiqa Jafari, Yung Cheol Byun
The Journal of Supercomputing (2023) Vol. 80, Iss. 4, pp. 4707-4732
Closed Access | Times Cited: 12
Hybrid Neural Networks for Enhanced Prediction of Remaining Useful Life in Lithium-Ion Batteries
Alireza Rastegarparnah, Mohammed Eesa Asif, Rustam Stolkin
(2024)
Open Access | Times Cited: 3
A Review of Degradation Models and Remaining Useful Life Prediction for Testing Design and Predictive Maintenance of Lithium-Ion Batteries
Gabriele Patrizi, Luca Martiri, Antonio Pievatolo, et al.
Sensors (2024) Vol. 24, Iss. 11, pp. 3382-3382
Open Access | Times Cited: 3
A denoising semi-supervised deep learning model for remaining useful life prediction of turbofan engine degradation
Youming Wang, Yue Wang
Applied Intelligence (2023) Vol. 53, Iss. 19, pp. 22682-22699
Closed Access | Times Cited: 7
Prediction of remaining useful life and recycling node of lithium-ion batteries based on a hybrid method of LSTM and LightGBM
Zeyu Chang, Hanlin Tang, Zhiqi Zhang, et al.
Energy Sources Part A Recovery Utilization and Environmental Effects (2024) Vol. 46, Iss. 1, pp. 1-13
Closed Access | Times Cited: 2
Survey on task-centric robot battery management: A neural network framework
Zihui Lin, Zhongwei Huang, Shuojin Yang, et al.
Journal of Power Sources (2024) Vol. 610, pp. 234674-234674
Closed Access | Times Cited: 1
A novel data augmentation strategy for aeroengine multitask prognosis based on degradation behavior extrapolation and diversity-usability trade-off
Xiao Yan Li, De Jun Cheng, Xi Feng Fang, et al.
Reliability Engineering & System Safety (2024) Vol. 249, pp. 110238-110238
Closed Access | Times Cited: 1
Advanced RUL Estimation for Lithium-Ion Batteries: Integrating Attention-Based LSTM with Mutual Learning-enhanced Artificial Bee Colony Optimization
Yi-Jun Xu
Journal of The Institution of Engineers (India) Series B (2024)
Closed Access | Times Cited: 1
Optimal charging of Li-ion batteries using sparse identification of nonlinear dynamics
Bhavana Bhadriraju, Joo‐Young Lee, Silabrata Pahari, et al.
Chemical Engineering Journal (2024), pp. 155015-155015
Closed Access | Times Cited: 1
Evolutionary hybrid deep learning based on feature engineering and deep projection encoded echo-state network for lithium batteries state of health estimation
Zhongyi Tang, Zhirong Zhang, Xiumei Shen, et al.
Energy (2024), pp. 133978-133978
Closed Access | Times Cited: 1
Repurposing Second-Life EV Batteries to Advance Sustainable Development: A Comprehensive Review
Muhammad Nadeem Akram, Walid Abdul‐Kader
Batteries (2024) Vol. 10, Iss. 12, pp. 452-452
Open Access | Times Cited: 1
Early Prediction of Remaining Useful Life for Li-ion Batteries Using Transformer Model with Dual Auto-Encoder and Ensemble Techniques
C H Adithya, Akarsh R Hegde, Sathya Prasad
2022 IEEE 7th International conference for Convergence in Technology (I2CT) (2024)
Closed Access
Battery Health State Prediction Based on Singular Spectrum Analysis and Transformer Network
Chengti Huang, Na Li, Jianqing Zhu, et al.
Electronics (2024) Vol. 13, Iss. 13, pp. 2434-2434
Open Access
GPT-based equipment remaining useful life prediction
P Wang, Shaozhang Niu, Haoliang Cui, et al.
(2024), pp. 159-164
Closed Access
Dual Siamese transformer-encoder-based network for remaining useful life prediction
Ching-Sheng Lin
The Journal of Supercomputing (2024) Vol. 80, Iss. 17, pp. 25424-25449
Closed Access
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