Electric vehicle charging service operations: A review of machine learning applications for infrastructure planning, control, pricing and routing

N Fescioglu-Unver, MY Aktaş - Renewable and Sustainable Energy …, 2023 - Elsevier
The majority of global road transportation emissions come from passenger and freight
vehicles. Electric vehicles (EV) provide a sustainable transportation way, but customers' …

[HTML][HTML] An updated review and outlook on electric vehicle aggregators in electric energy networks

M Nazari-Heris, M Abapour, B Mohammadi-Ivatloo - Sustainability, 2022 - mdpi.com
Electric vehicles (EVs) are predicted to be highly integrated into future smart grids
considering their significant role in achieving a safe environment and sustainable …

Forecasting the development trend of new energy vehicles in China by an optimized fractional discrete grey power model

L Liu, S Liu, L Wu, J Zhu, G Shang - Journal of Cleaner Production, 2022 - Elsevier
As an effective technology to reduce traffic pollution emissions, the new energy vehicle
industry has developed rapidly in recent years, and the sales of new energy vehicles have …

Short-term electric vehicle charging demand prediction: A deep learning approach

S Wang, C Zhuge, C Shao, P Wang, X Yang, S Wang - Applied Energy, 2023 - Elsevier
Short-term prediction of the Electric Vehicle (EV) charging demand is of great importance to
the operation of EV fleets and charging stations. This paper develops a Long Short-Term …

Electric vehicle demand estimation and charging station allocation using urban informatics

Z Yi, XC Liu, R Wei - Transportation Research Part D: Transport and …, 2022 - Elsevier
This paper performs a novel data-driven approach to optimize electric vehicle (EV) public
charging. We translate the study area into a directed graph by partitioning it into discrete …

An agent-based modeling approach for public charging demand estimation and charging station location optimization at urban scale

Z Yi, B Chen, XC Liu, R Wei, J Chen, Z Chen - Computers, Environment and …, 2023 - Elsevier
As the market penetration of electric vehicles (EVs) increases, the surge of charging demand
could potentially overload the power grid and disrupt infrastructure planning. Hence, an …

[HTML][HTML] Short-term load forecasting model of electric vehicle charging load based on MCCNN-TCN

J Zhang, C Liu, L Ge - Energies, 2022 - mdpi.com
The large fluctuations in charging loads of electric vehicles (EVs) make short-term
forecasting challenging. In order to improve the short-term load forecasting performance of …

Predicting electric vehicle charging demand using a heterogeneous spatio-temporal graph convolutional network

S Wang, A Chen, P Wang, C Zhuge - Transportation Research Part C …, 2023 - Elsevier
Abstract Short-term Electric Vehicle (EV) charging demand prediction is an essential task in
the fields of smart grid and intelligent transportation systems, as understanding the …

Random parameters modeling of charging-power demand for the optimal location of electric vehicle charge facilities

MM Hamed, DM Kabtawi, A Al-Assaf… - Journal of Cleaner …, 2023 - Elsevier
Abstract Nowadays, Electric Vehicles (EVs) are considered a disruptive technology that will-
with time-become dominant over their predecessor. EVs range and cost are closely linked …

Proactive resource request for disaster response: A deep learning-based optimization model

H Zhang, X Zhao, X Fang… - Information Systems …, 2023 - pubsonline.informs.org
Disaster response is critical to save lives and reduce damages in the aftermath of a disaster.
Fundamental to disaster response operations is the management of disaster relief …