Version 1
: Received: 23 July 2024 / Approved: 25 July 2024 / Online: 25 July 2024 (09:09:48 CEST)
How to cite:
Mądziel, M.; Campisi, T. Predictive AI Models for Energy Efficiency in Hybrid and Electric Vehicles: Analysis for Enna, Sicily. Preprints2024, 2024072010. https://doi.org/10.20944/preprints202407.2010.v1
Mądziel, M.; Campisi, T. Predictive AI Models for Energy Efficiency in Hybrid and Electric Vehicles: Analysis for Enna, Sicily. Preprints 2024, 2024072010. https://doi.org/10.20944/preprints202407.2010.v1
Mądziel, M.; Campisi, T. Predictive AI Models for Energy Efficiency in Hybrid and Electric Vehicles: Analysis for Enna, Sicily. Preprints2024, 2024072010. https://doi.org/10.20944/preprints202407.2010.v1
APA Style
Mądziel, M., & Campisi, T. (2024). Predictive AI Models for Energy Efficiency in Hybrid and Electric Vehicles: Analysis for Enna, Sicily. Preprints. https://doi.org/10.20944/preprints202407.2010.v1
Chicago/Turabian Style
Mądziel, M. and Tiziana Campisi. 2024 "Predictive AI Models for Energy Efficiency in Hybrid and Electric Vehicles: Analysis for Enna, Sicily" Preprints. https://doi.org/10.20944/preprints202407.2010.v1
Abstract
Developments in artificial intelligence techniques allow for an improvement in sustainable mobility strategies with particular reference to energy consumption estimates of electric vehicles (EVs). This research proposes a vehicle energy model developed on the basis of Deep Neural Network (DNN) technology. The study also explores the potential application of the model developed for the movement data of new vehicles in the province of Enna, Sicily, Italy. which is characterized by numerous attractors and the increasing number of hybrid and electric cars circulating. The energy model for electric vehicles shows high accuracy and versatility, requiring vehicle velocity and acceleration as input data to predict energy consumption. The research article also provides recommendations for the energy modeling of electric vehicles and outlines additional steps for model development. The implemented methodological approach and its results can be used by transport decision makers to plan new transport policies in Italian cities aimed at optimizing vehicle charging infrastructure. They can also help vehicle users accurately estimate energy consumption, generate maps, and identify locations with the highest energy consumption.
Keywords
vehicles; EV; Energy consumption; predictive modelling; Italy; artificial intelligence
Subject
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
