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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 frequency item mining based energy consumption prediction method for electric bus
Zhao Li, Hanchen Ke, Weiwei Huo
Energy (2022) Vol. 263, pp. 125915-125915
Closed Access | Times Cited: 13

Showing 13 citing articles:

A review of machine learning approaches for electric vehicle energy consumption modelling in urban transportation
Xinfang Zhang, Zhe Zhang, Yang Liu, et al.
Renewable Energy (2024) Vol. 234, pp. 121243-121243
Closed Access | Times Cited: 11
Transfer learning based hybrid model for power demand prediction of large-scale electric vehicles
Chenlu Tian, Yechun Liu, Guiqing Zhang, et al.
Energy (2024) Vol. 300, pp. 131461-131461
Closed Access | Times Cited: 6
Online energy consumption forecast for battery electric buses using a learning-free algebraic method
Zejiang Wang, Guanhao Xu, Ruixiao Sun, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
An energy consumption prediction model for electric buses based on extreme gradient boosting fusion algorithm
Yiting Kang, Jianshu Wei, Zhiyuan Liu, et al.
International Journal of Green Energy (2025), pp. 1-14
Closed Access
The impact of electromobility in public transport: An estimation of energy consumption using disaggregated data in Santiago, Chile
Franco Basso, Felipe Feijoo, Raúl Pezoa, et al.
Energy (2023) Vol. 286, pp. 129550-129550
Closed Access | Times Cited: 8
Analysis and estimation of energy consumption of electric buses using real-world data
Zhaosheng Zhang, Bao‐Lin Ye, Shuai Wang, et al.
Transportation Research Part D Transport and Environment (2023) Vol. 126, pp. 104017-104017
Closed Access | Times Cited: 8
A frequency item mining based embedded feature selection algorithm and its application in energy consumption prediction of electric bus
Li Zhao, Yuqi Li, Shuai Li, et al.
Energy (2023) Vol. 271, pp. 126999-126999
Closed Access | Times Cited: 6
A Novel Energy Consumption Prediction Model of Electric Buses Using Real-Time Big Data From Route, Environment, and Vehicle Parameters
Yunus Emre Ekici, Ozan Akdağ, Ahmet Arif Aydın, et al.
IEEE Access (2023) Vol. 11, pp. 104305-104322
Open Access | Times Cited: 5
A novel ranking method based on semi-SPO for battery swapping allocation optimization in a hybrid electric transit system
Di Huang, Jinyu Zhang, Zhiyuan Liu, et al.
Transportation Research Part E Logistics and Transportation Review (2024) Vol. 188, pp. 103611-103611
Closed Access | Times Cited: 1
A new perspective on the performance comparison of pure electric buses versus converted electric buses in public transportation systems
Orhan Topal
International Journal of Low-Carbon Technologies (2024) Vol. 19, pp. 1829-1840
Open Access
Driving range estimation for electric bus based on atomic orbital search and back propagation neural network
Hanchen Ke, Jun Bi, Yongxing Wang, et al.
IET Intelligent Transport Systems (2024)
Closed Access
Analysing the factors of bus transport operations influencing the energy consumption of electric buses in real world for sustainable transport strategies
Priyanka Bendigiri, Prakash Rao
International Journal of Energy Sector Management (2024)
Closed Access
Electric City Buses Enhanced Energy Consumption Model Using Real-Time Big-Data
Teoman Karadağ, yunus emre ekici, Ozan Akdağ, et al.
(2023)
Closed Access
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