Predictive Artificial Intelligence Models for Energy Efficiency in Hybrid and Electric Vehicles: Analysis for Enna, Sicily
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) techn...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-09-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/17/19/4913 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850185034508533760 |
|---|---|
| author | Maksymilian Mądziel Tiziana Campisi |
| author_facet | Maksymilian Mądziel Tiziana Campisi |
| author_sort | Maksymilian Mądziel |
| collection | DOAJ |
| description | 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. This 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 are 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. This 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. |
| format | Article |
| id | doaj-art-cfb2f8c2ed7f4595957c7684fe617bf5 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-cfb2f8c2ed7f4595957c7684fe617bf52025-08-20T02:16:50ZengMDPI AGEnergies1996-10732024-09-011719491310.3390/en17194913Predictive Artificial Intelligence Models for Energy Efficiency in Hybrid and Electric Vehicles: Analysis for Enna, SicilyMaksymilian Mądziel0Tiziana Campisi1Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, 35-959 Rzeszow, PolandDepartment of Engineering and Architecture, Kore University of Enna, Cittadella Universitaria, 94100 Enna, ItalyDevelopments 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. This 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 are 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. This 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.https://www.mdpi.com/1996-1073/17/19/4913vehiclesEVenergy consumptionpredictive modelingItalyartificial intelligence |
| spellingShingle | Maksymilian Mądziel Tiziana Campisi Predictive Artificial Intelligence Models for Energy Efficiency in Hybrid and Electric Vehicles: Analysis for Enna, Sicily Energies vehicles EV energy consumption predictive modeling Italy artificial intelligence |
| title | Predictive Artificial Intelligence Models for Energy Efficiency in Hybrid and Electric Vehicles: Analysis for Enna, Sicily |
| title_full | Predictive Artificial Intelligence Models for Energy Efficiency in Hybrid and Electric Vehicles: Analysis for Enna, Sicily |
| title_fullStr | Predictive Artificial Intelligence Models for Energy Efficiency in Hybrid and Electric Vehicles: Analysis for Enna, Sicily |
| title_full_unstemmed | Predictive Artificial Intelligence Models for Energy Efficiency in Hybrid and Electric Vehicles: Analysis for Enna, Sicily |
| title_short | Predictive Artificial Intelligence Models for Energy Efficiency in Hybrid and Electric Vehicles: Analysis for Enna, Sicily |
| title_sort | predictive artificial intelligence models for energy efficiency in hybrid and electric vehicles analysis for enna sicily |
| topic | vehicles EV energy consumption predictive modeling Italy artificial intelligence |
| url | https://www.mdpi.com/1996-1073/17/19/4913 |
| work_keys_str_mv | AT maksymilianmadziel predictiveartificialintelligencemodelsforenergyefficiencyinhybridandelectricvehiclesanalysisforennasicily AT tizianacampisi predictiveartificialintelligencemodelsforenergyefficiencyinhybridandelectricvehiclesanalysisforennasicily |