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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:

Online joint-prediction of multi-forward-step battery SOC using LSTM neural networks and multiple linear regression for real-world electric vehicles
Jichao Hong, Zhenpo Wang, Wen Chen, et al.
Journal of Energy Storage (2020) Vol. 30, pp. 101459-101459
Open Access | Times Cited: 146

Showing 1-25 of 146 citing articles:

State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network
Junxiong Chen, Feng Xiong, Lin Jiang, et al.
Energy (2021) Vol. 227, pp. 120451-120451
Closed Access | Times Cited: 140
A novel data-driven method for mining battery open-circuit voltage characterization
Cheng Chen, Rui Xiong, Ruixin Yang, et al.
Green Energy and Intelligent Transportation (2022) Vol. 1, Iss. 1, pp. 100001-100001
Open Access | Times Cited: 103
Transfer learning for battery smarter state estimation and ageing prognostics: Recent progress, challenges, and prospects
Kailong Liu, Qiao Peng, Yunhong Che, et al.
Advances in Applied Energy (2022) Vol. 9, pp. 100117-100117
Open Access | Times Cited: 90
Toward safe carbon–neutral transportation: Battery internal short circuit diagnosis based on cloud data for electric vehicles
Dongdong Qiao, Xuezhe Wei, Wenjun Fan, et al.
Applied Energy (2022) Vol. 317, pp. 119168-119168
Closed Access | Times Cited: 77
A novel back propagation neural network-dual extended Kalman filter method for state-of-charge and state-of-health co-estimation of lithium-ion batteries based on limited memory least square algorithm
Chao Wang, Shunli Wang, Jinzhi Zhou, et al.
Journal of Energy Storage (2023) Vol. 59, pp. 106563-106563
Closed Access | Times Cited: 59
A systematic review and meta-analysis of machine learning, deep learning, and ensemble learning approaches in predicting EV charging behavior
Elaheh Yaghoubi, Elnaz Yaghoubi, Ahmed A. Khamees, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 135, pp. 108789-108789
Closed Access | Times Cited: 55
Cloud-Based Artificial Intelligence Framework for Battery Management System
Dapai Shi, Jingyuan Zhao, Chika Eze, et al.
Energies (2023) Vol. 16, Iss. 11, pp. 4403-4403
Open Access | Times Cited: 41
A Smart Battery Management System for Electric Vehicles Using Deep Learning-Based Sensor Fault Detection
Venkata Satya Rahul Kosuru, Ashwin Kavasseri Venkitaraman
World Electric Vehicle Journal (2023) Vol. 14, Iss. 4, pp. 101-101
Open Access | Times Cited: 40
Multi- forword-step state of charge prediction for real-world electric vehicles battery systems using a novel LSTM-GRU hybrid neural network
Jichao Hong, Fengwei Liang, Haixu Yang, et al.
eTransportation (2024) Vol. 20, pp. 100322-100322
Closed Access | Times Cited: 26
A novel battery SOC estimation method based on random search optimized LSTM neural network
Xuqing Chai, Shihao Li, Fengwei Liang
Energy (2024) Vol. 306, pp. 132583-132583
Closed Access | Times Cited: 22
AI on Wheels: Bibliometric Approach to Mapping of Research on Machine Learning and Deep Learning in Electric Vehicles
Adrian Domenteanu, Liviu‐Adrian Cotfas, Paul Diaconu, et al.
Electronics (2025) Vol. 14, Iss. 2, pp. 378-378
Open Access | Times Cited: 3
Could fast battery charging effectively mitigate range anxiety in electric vehicle usage?Evidence from large-scale data on travel and charging in Beijing
Bin Zhang, Niu Niu, Hao Li, et al.
Transportation Research Part D Transport and Environment (2021) Vol. 95, pp. 102840-102840
Closed Access | Times Cited: 89
Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter
Zheng Chen, Hongqian Zhao, Xing Shu, et al.
Energy (2021) Vol. 228, pp. 120630-120630
Open Access | Times Cited: 88
State of Charge Estimation of Lithium-Ion Batteries Using LSTM and NARX Neural Networks
Meng Wei, Min Ye, Jia Bo Li, et al.
IEEE Access (2020) Vol. 8, pp. 189236-189245
Open Access | Times Cited: 87
Accurate and reliable state of charge estimation of lithium ion batteries using time-delayed recurrent neural networks through the identification of overexcited neurons
Zhimin Xi, Rui Wang, Yuhong Fu, et al.
Applied Energy (2021) Vol. 305, pp. 117962-117962
Closed Access | Times Cited: 76
An extended Kalman filter based data-driven method for state of charge estimation of Li-ion batteries
Xingtao Liu, Kun Li, Ji Wu, et al.
Journal of Energy Storage (2021) Vol. 40, pp. 102655-102655
Closed Access | Times Cited: 75
More intelligent and robust estimation of battery state-of-charge with an improved regularized extreme learning machine
Meng Jiao, Dongqing Wang, Yan Yang, et al.
Engineering Applications of Artificial Intelligence (2021) Vol. 104, pp. 104407-104407
Closed Access | Times Cited: 74
Modeling, prediction and analysis of new energy vehicle sales in China using a variable-structure grey model
Bo Zeng, Hui Li, Cuiwei Mao, et al.
Expert Systems with Applications (2022) Vol. 213, pp. 118879-118879
Closed Access | Times Cited: 66
An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries
Paul Takyi‐Aninakwa, Shunli Wang, Hongying Zhang, et al.
Applied Energy (2022) Vol. 326, pp. 120043-120043
Open Access | Times Cited: 43
State-of-health estimation for lithium-ion batteries with hierarchical feature construction and auto-configurable Gaussian process regression
Haiyan Jin, Ningmin Cui, Lei Cai, et al.
Energy (2022) Vol. 262, pp. 125503-125503
Closed Access | Times Cited: 42
Online estimation of driving range for battery electric vehicles based on SOC-segmented actual driving cycle
Heng Wei, Chao He, Jiaqiang Li, et al.
Journal of Energy Storage (2022) Vol. 49, pp. 104091-104091
Closed Access | Times Cited: 41
A state of health estimation framework based on real-world electric vehicles operating data
Xu Zhao, Jianyao Hu, Guangdi Hu, et al.
Journal of Energy Storage (2023) Vol. 63, pp. 107031-107031
Closed Access | Times Cited: 31
A feedforward deep neural network for predicting the state-of-charge of lithium-ion battery in electric vehicles
Bukola Peter Adedeji, Golam Kabir
Decision Analytics Journal (2023) Vol. 8, pp. 100255-100255
Open Access | Times Cited: 31
A novel state of health prediction method for battery system in real-world vehicles based on gated recurrent unit neural networks
Jichao Hong, Kerui Li, Fengwei Liang, et al.
Energy (2023) Vol. 289, pp. 129918-129918
Closed Access | Times Cited: 31
An improved parameter identification and radial basis correction-differential support vector machine strategies for state-of-charge estimation of urban-transportation-electric-vehicle lithium-ion batteries
Shunli Wang, Chao Wang, Paul Takyi‐Aninakwa, et al.
Journal of Energy Storage (2023) Vol. 80, pp. 110222-110222
Closed Access | Times Cited: 27
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