Electric vehicle energy consumption prediction using stacked generalization: An ensemble learning approach
In this paper, we present an ensemble stacked generalization (ESG) approach for better
prediction of electric vehicles (EVs) energy consumption. ESG is a weighted combination of …
prediction of electric vehicles (EVs) energy consumption. ESG is a weighted combination of …
A comparative performance of machine learning algorithm to predict electric vehicles energy consumption: A path towards sustainability
The rapid growth of transportation sector and related emissions are attracting the attention of
policymakers to ensure environmental sustainability. Therefore, the deriving factors of …
policymakers to ensure environmental sustainability. Therefore, the deriving factors of …
Prediction of energy consumption for new electric vehicle models by machine learning
A Fukushima, T Yano, S Imahara, H Aisu… - IET Intelligent …, 2018 - Wiley Online Library
Recommending suitable charging spots to drivers on expressways for both charging
equipment and electric vehicles (EVs) is an important issue for the spread of EVs. Therefore …
equipment and electric vehicles (EVs) is an important issue for the spread of EVs. Therefore …
Ensemble machine learning-based algorithm for electric vehicle user behavior prediction
This research investigates electric vehicle (EV) charging behavior and aims to find the best
method for its prediction in order to optimize the EV charging schedule. This paper …
method for its prediction in order to optimize the EV charging schedule. This paper …
From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus
Accurate real-time energy consumption prediction of electric buses (EBs) is essential for bus
operation and management, which can effectively mitigate the driving range anxiety while …
operation and management, which can effectively mitigate the driving range anxiety while …
Prediction of electric vehicle charging duration time using ensemble machine learning algorithm and Shapley additive explanations
Electric vehicles (EVs) are the most important components of smart transportation systems.
Limited driving range, prolonged charging times, and inadequate charging infrastructure are …
Limited driving range, prolonged charging times, and inadequate charging infrastructure are …
Data-driven framework for large-scale prediction of charging energy in electric vehicles
Large-scale and high-precision predictions of the charging energy required for electric
vehicles (EVs) are essential to ensure the safety of EVs and provide reliable inputs for grid …
vehicles (EVs) are essential to ensure the safety of EVs and provide reliable inputs for grid …
Development of an energy consumption prediction model for battery electric vehicles in real-world driving: a combined approach of short-trip segment division and …
Y Pan, W Fang, W Zhang - Journal of Cleaner Production, 2023 - Elsevier
Due to the excellent energy-saving and environmental protection features, electric vehicles
(EVs) are gaining significant market penetration, especially in densely populated urban …
(EVs) are gaining significant market penetration, especially in densely populated urban …
[HTML][HTML] Artificial deep neural network enables one-size-fits-all electric vehicle user behavior prediction framework
A Ahmadian, V Ghodrati, R Gadh - Applied Energy, 2023 - Elsevier
As greener mobility becomes the norm with the advent of electric vehicles (EVs), a natural
question arises: how big of a change are we seeing in terms of the stochastic energy …
question arises: how big of a change are we seeing in terms of the stochastic energy …
[HTML][HTML] Prediction of transportation energy demand in Türkiye using stacking ensemble models: Methodology and comparative analysis
The transportation sector accounts for 61.5% of global oil consumption and is responsible
for 29% of the world's total energy demand. Passenger transportation utilizes around 50 …
for 29% of the world's total energy demand. Passenger transportation utilizes around 50 …