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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 based on denoising technique and deep neural network for lithium-ion capacitors
Hao Yang, Penglei Wang, Yabin An, et al.
eTransportation (2020) Vol. 5, pp. 100078-100078
Closed Access | Times Cited: 88

Showing 1-25 of 88 citing articles:

Prognostics and health management of Lithium-ion battery using deep learning methods: A review
Ying Zhang, Yan‐Fu Li
Renewable and Sustainable Energy Reviews (2022) Vol. 161, pp. 112282-112282
Closed Access | Times Cited: 156
A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries
Kai Luo, Xiang Chen, Huiru Zheng, et al.
Journal of Energy Chemistry (2022) Vol. 74, pp. 159-173
Open Access | Times Cited: 153
Lithium battery state-of-health estimation and remaining useful lifetime prediction based on non-parametric aging model and particle filter algorithm
Xiaoyu Li, Changgui Yuan, Zhenpo Wang, et al.
eTransportation (2022) Vol. 11, pp. 100156-100156
Closed Access | Times Cited: 87
The development of machine learning-based remaining useful life prediction for lithium-ion batteries
Xingjun Li, Dan Yu, Søren Byg Vilsen, et al.
Journal of Energy Chemistry (2023) Vol. 82, pp. 103-121
Open Access | Times Cited: 85
Joint estimation of state-of-charge and state-of-health for all cells in the battery pack using “leader-follower” strategy
Xiaopeng Tang, Yuanqiang Zhou, Furong Gao, et al.
eTransportation (2022) Vol. 15, pp. 100213-100213
Closed Access | Times Cited: 76
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
Battery SOH estimation and RUL prediction framework based on variable forgetting factor online sequential extreme learning machine and particle filter
Wenxian Duan, Shixin Song, Feng Xiao, et al.
Journal of Energy Storage (2023) Vol. 65, pp. 107322-107322
Closed Access | Times Cited: 60
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
Adaptive Kalman filter and self-designed early stopping strategy optimized convolutional neural network for state of energy estimation of lithium-ion battery in complex temperature environment
Jin Hui Li, Shunli Wang, Lei Chen, et al.
Journal of Energy Storage (2024) Vol. 83, pp. 110750-110750
Closed Access | Times Cited: 15
A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods
Shunli Wang, Siyu Jin, Dan Deng, et al.
Frontiers in Mechanical Engineering (2021) Vol. 7
Open Access | Times Cited: 70
Assessment and management of health status in full life cycle of echelon utilization for retired power lithium batteries
Haopeng Chen, Tianshi Zhang, Qing Gao, et al.
Journal of Cleaner Production (2022) Vol. 379, pp. 134583-134583
Closed Access | Times Cited: 53
A vehicle-cloud collaborative method for multi-type fault diagnosis of lithium-ion batteries
Chaojie Xu, Laibao Li, Yuwen Xu, et al.
eTransportation (2022) Vol. 12, pp. 100172-100172
Closed Access | Times Cited: 42
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
Uncertainty quantification of proton-exchange-membrane fuel cells degradation prediction based on Bayesian-Gated Recurrent Unit
Wenchao Zhu, Bingxin Guo, Yang Li, et al.
eTransportation (2023) Vol. 16, pp. 100230-100230
Open Access | Times Cited: 37
State-of-health estimation and remaining useful life prediction for lithium-ion batteries based on an improved particle filter algorithm
Shiding Hong, Chaokui Qin, Xin Lai, et al.
Journal of Energy Storage (2023) Vol. 64, pp. 107179-107179
Closed Access | Times Cited: 35
Enhanced multi-state estimation methods for lithium-ion batteries considering temperature uncertainties
Paul Takyi‐Aninakwa, Shunli Wang, Hongying Zhang, et al.
Journal of Energy Storage (2023) Vol. 66, pp. 107495-107495
Closed Access | Times Cited: 24
Recent advances in transition metal oxides as anode materials for high-performance lithium-ion capacitors
Chunyu Zhao, Shuyu Yao, Chen Li, et al.
Chemical Engineering Journal (2024) Vol. 497, pp. 154535-154535
Closed Access | Times Cited: 13
A hybrid data driven framework considering feature extraction for battery state of health estimation and remaining useful life prediction
Yuan Chen, Wenxian Duan, Yigang He, et al.
Green Energy and Intelligent Transportation (2024) Vol. 3, Iss. 2, pp. 100160-100160
Open Access | Times Cited: 11
Data driven health and life prognosis management of supercapacitor and lithium-ion battery storage systems: Developments, implementation aspects, limitations, and future directions
Molla Shahadat Hossain Lipu, M. Rahman, Muhamad Mansor, et al.
Journal of Energy Storage (2024) Vol. 98, pp. 113172-113172
Closed Access | Times Cited: 11
Recurrent Neural Networks for Estimating the State of Health of Lithium-Ion Batteries
RAFAEL SAADI DANTAS TEIXEIRA, Rodrigo Flora Calili, Maria Fatima Ludovico de Almeida, et al.
Batteries (2024) Vol. 10, Iss. 3, pp. 111-111
Open Access | Times Cited: 10
An interpretable online prediction method for remaining useful life of lithium-ion batteries
Zuxin Li, Shengyu Shen, Yifu Ye, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 9
Multi-Kernel Relevance Vector Machine With Parameter Optimization for Cycling Aging Prediction of Lithium-Ion Batteries
Bo Jiang, Haifeng Dai, Xuezhe Wei, et al.
IEEE Journal of Emerging and Selected Topics in Power Electronics (2021) Vol. 11, Iss. 1, pp. 175-186
Closed Access | Times Cited: 54
Overview of Machine Learning Methods for Lithium-Ion Battery Remaining Useful Lifetime Prediction
Siyu Jin, Xin Sui, Xinrong Huang, et al.
Electronics (2021) Vol. 10, Iss. 24, pp. 3126-3126
Open Access | Times Cited: 48
Rapid ultracapacitor life prediction with a convolutional neural network
Chenxu Wang, Rui Xiong, Jinpeng Tian, et al.
Applied Energy (2021) Vol. 305, pp. 117819-117819
Closed Access | Times Cited: 44
Joint state estimation of lithium-ion batteries combining improved equivalent circuit model with electrochemical mechanism and diffusion process
Xiaodong Xu, Shengjin Tang, Huahua Ren, et al.
Journal of Energy Storage (2022) Vol. 56, pp. 106135-106135
Closed Access | Times Cited: 32
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