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description Publicationkeyboard_double_arrow_right Article , Other literature type 2023 ItalyPublisher:MDPI AG Funded by:EC | eCharge4DriversEC| eCharge4DriversAuthors:Cubillos, Maximiliano;
Dell’Amico, Mauro;Cubillos, Maximiliano
Cubillos, Maximiliano in OpenAIREJabali, Ola;
Jabali, Ola
Jabali, Ola in OpenAIREMalucelli, Federico;
+1 AuthorsMalucelli, Federico
Malucelli, Federico in OpenAIRECubillos, Maximiliano;
Dell’Amico, Mauro;Cubillos, Maximiliano
Cubillos, Maximiliano in OpenAIREJabali, Ola;
Jabali, Ola
Jabali, Ola in OpenAIREMalucelli, Federico;
Malucelli, Federico
Malucelli, Federico in OpenAIRETresoldi, Emanuele;
Tresoldi, Emanuele
Tresoldi, Emanuele in OpenAIREhandle: 11311/1242112
A number of decision support tools facilitating the use of Electric Vehicles (EVs) have been recently developed. Due to the EVs’ limited autonomy, routing and path planning are the main challenges treated in such tools. Specifically, determining at which Charging Stations (CSs) to stop, and how much the EV should charge at them is complex. This complexity is further compounded by the fact that charging times depend on the CS technology, the EV characteristics, and follow a nonlinear function. Considering these factors, we propose a path-planning methodology for EVs with user preferences, where charging is performed at public CSs. To achieve this, we introduce the Electric Vehicle Shortest Path Problem with time windows and user preferences (EVSPPWP) and propose an efficient heuristic algorithm for it. Given an origin and a destination, the algorithm prioritizes CSs close to Points of Interest (POIs) that match user inputted preferences, and user-defined time windows are considered for activities such as lunch and spending the night at hotels. The algorithm produces flexible solutions by considering clusters of charging points (CPs) as separate CSs. Furthermore, the algorithm yields resilient paths by ensuring that recommended paths have a minimum number of CSs in their vicinity. The main contributions of our methodology are the following: modeling user-defined time windows, including user-defined weights for different POI categories, creating CSs based on clusters of CPs with sufficient proximity, using resilient paths, and proposing an efficient algorithm for solving the EVSPPWP. To facilitate the use of our methodology, the algorithm was integrated into a web interface. We demonstrate the use of the web interface, giving usage examples and comparing different settings.
Energies arrow_drop_down EnergiesOther literature type . 2023License: CC BYFull-Text: http://www.mdpi.com/1996-1073/16/10/4173/pdfData sources: Multidisciplinary Digital Publishing Institutehttps://doi.org/10.20944/prepr...Article . 2023 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en16104173&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 2 citations 2 popularity Average influence Average impulse Average Powered by BIP!
visibility 8visibility views 8 download downloads 10 Powered bymore_vert Energies arrow_drop_down EnergiesOther literature type . 2023License: CC BYFull-Text: http://www.mdpi.com/1996-1073/16/10/4173/pdfData sources: Multidisciplinary Digital Publishing Institutehttps://doi.org/10.20944/prepr...Article . 2023 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en16104173&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2023 ItalyPublisher:MDPI AG Funded by:EC | eCharge4DriversEC| eCharge4DriversAuthors:Cubillos, Maximiliano;
Dell’Amico, Mauro;Cubillos, Maximiliano
Cubillos, Maximiliano in OpenAIREJabali, Ola;
Jabali, Ola
Jabali, Ola in OpenAIREMalucelli, Federico;
+1 AuthorsMalucelli, Federico
Malucelli, Federico in OpenAIRECubillos, Maximiliano;
Dell’Amico, Mauro;Cubillos, Maximiliano
Cubillos, Maximiliano in OpenAIREJabali, Ola;
Jabali, Ola
Jabali, Ola in OpenAIREMalucelli, Federico;
Malucelli, Federico
Malucelli, Federico in OpenAIRETresoldi, Emanuele;
Tresoldi, Emanuele
Tresoldi, Emanuele in OpenAIREhandle: 11311/1242112
A number of decision support tools facilitating the use of Electric Vehicles (EVs) have been recently developed. Due to the EVs’ limited autonomy, routing and path planning are the main challenges treated in such tools. Specifically, determining at which Charging Stations (CSs) to stop, and how much the EV should charge at them is complex. This complexity is further compounded by the fact that charging times depend on the CS technology, the EV characteristics, and follow a nonlinear function. Considering these factors, we propose a path-planning methodology for EVs with user preferences, where charging is performed at public CSs. To achieve this, we introduce the Electric Vehicle Shortest Path Problem with time windows and user preferences (EVSPPWP) and propose an efficient heuristic algorithm for it. Given an origin and a destination, the algorithm prioritizes CSs close to Points of Interest (POIs) that match user inputted preferences, and user-defined time windows are considered for activities such as lunch and spending the night at hotels. The algorithm produces flexible solutions by considering clusters of charging points (CPs) as separate CSs. Furthermore, the algorithm yields resilient paths by ensuring that recommended paths have a minimum number of CSs in their vicinity. The main contributions of our methodology are the following: modeling user-defined time windows, including user-defined weights for different POI categories, creating CSs based on clusters of CPs with sufficient proximity, using resilient paths, and proposing an efficient algorithm for solving the EVSPPWP. To facilitate the use of our methodology, the algorithm was integrated into a web interface. We demonstrate the use of the web interface, giving usage examples and comparing different settings.
Energies arrow_drop_down EnergiesOther literature type . 2023License: CC BYFull-Text: http://www.mdpi.com/1996-1073/16/10/4173/pdfData sources: Multidisciplinary Digital Publishing Institutehttps://doi.org/10.20944/prepr...Article . 2023 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en16104173&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 2 citations 2 popularity Average influence Average impulse Average Powered by BIP!
visibility 8visibility views 8 download downloads 10 Powered bymore_vert Energies arrow_drop_down EnergiesOther literature type . 2023License: CC BYFull-Text: http://www.mdpi.com/1996-1073/16/10/4173/pdfData sources: Multidisciplinary Digital Publishing Institutehttps://doi.org/10.20944/prepr...Article . 2023 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en16104173&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2023 ItalyPublisher:MDPI AG Funded by:EC | eCharge4DriversEC| eCharge4DriversAuthors:Schettini, Tommaso;
dell’Amico, Mauro;Schettini, Tommaso
Schettini, Tommaso in OpenAIREFumero, Francesca;
Fumero, Francesca
Fumero, Francesca in OpenAIREJabali, Ola;
+1 AuthorsJabali, Ola
Jabali, Ola in OpenAIRESchettini, Tommaso;
dell’Amico, Mauro;Schettini, Tommaso
Schettini, Tommaso in OpenAIREFumero, Francesca;
Fumero, Francesca
Fumero, Francesca in OpenAIREJabali, Ola;
Jabali, Ola
Jabali, Ola in OpenAIREMalucelli, Federico;
Malucelli, Federico
Malucelli, Federico in OpenAIREdoi: 10.3390/en16104186
handle: 11380/1362887 , 11311/1242109
In order to foster electric vehicle (EV) adoption rates, the availability of a pervasive and efficient charging network is a crucial requirement. In this paper, we provide a decision support tool for helping policymakers to locate and size EV charging stations. We consider a multi-year planning horizon, taking into account different charging technologies and different time periods (day and night). Accounting for these features, we propose an optimization model that minimizes total investment costs while ensuring a predetermined adequate level of demand coverage. In particular, the setup of charging stations is optimized every year, allowing for an increase in the number of chargers installed at charging stations set up in previous years. We have developed a tailored heuristic algorithm for the resulting problem. We validated our algorithm using case study instances based on the village of Gardone Val Trompia (Italy), the city of Barcelona (Spain), and the country of Luxembourg. Despite the variability in the sizes of the considered instances, our algorithm consistently provided high-quality results in short computational times, when compared to a commercial MILP solver. Produced solutions achieved optimality gaps within 7.5% in less than 90 s, often achieving computational times of less than 5 s.
Energies arrow_drop_down EnergiesOther literature type . 2023License: CC BYFull-Text: http://www.mdpi.com/1996-1073/16/10/4186/pdfData sources: Multidisciplinary Digital Publishing Instituteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en16104186&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 7 citations 7 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
visibility 9visibility views 9 download downloads 10 Powered bymore_vert Energies arrow_drop_down EnergiesOther literature type . 2023License: CC BYFull-Text: http://www.mdpi.com/1996-1073/16/10/4186/pdfData sources: Multidisciplinary Digital Publishing Instituteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en16104186&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2023 ItalyPublisher:MDPI AG Funded by:EC | eCharge4DriversEC| eCharge4DriversAuthors:Schettini, Tommaso;
dell’Amico, Mauro;Schettini, Tommaso
Schettini, Tommaso in OpenAIREFumero, Francesca;
Fumero, Francesca
Fumero, Francesca in OpenAIREJabali, Ola;
+1 AuthorsJabali, Ola
Jabali, Ola in OpenAIRESchettini, Tommaso;
dell’Amico, Mauro;Schettini, Tommaso
Schettini, Tommaso in OpenAIREFumero, Francesca;
Fumero, Francesca
Fumero, Francesca in OpenAIREJabali, Ola;
Jabali, Ola
Jabali, Ola in OpenAIREMalucelli, Federico;
Malucelli, Federico
Malucelli, Federico in OpenAIREdoi: 10.3390/en16104186
handle: 11380/1362887 , 11311/1242109
In order to foster electric vehicle (EV) adoption rates, the availability of a pervasive and efficient charging network is a crucial requirement. In this paper, we provide a decision support tool for helping policymakers to locate and size EV charging stations. We consider a multi-year planning horizon, taking into account different charging technologies and different time periods (day and night). Accounting for these features, we propose an optimization model that minimizes total investment costs while ensuring a predetermined adequate level of demand coverage. In particular, the setup of charging stations is optimized every year, allowing for an increase in the number of chargers installed at charging stations set up in previous years. We have developed a tailored heuristic algorithm for the resulting problem. We validated our algorithm using case study instances based on the village of Gardone Val Trompia (Italy), the city of Barcelona (Spain), and the country of Luxembourg. Despite the variability in the sizes of the considered instances, our algorithm consistently provided high-quality results in short computational times, when compared to a commercial MILP solver. Produced solutions achieved optimality gaps within 7.5% in less than 90 s, often achieving computational times of less than 5 s.
Energies arrow_drop_down EnergiesOther literature type . 2023License: CC BYFull-Text: http://www.mdpi.com/1996-1073/16/10/4186/pdfData sources: Multidisciplinary Digital Publishing Instituteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en16104186&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 7 citations 7 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
visibility 9visibility views 9 download downloads 10 Powered bymore_vert Energies arrow_drop_down EnergiesOther literature type . 2023License: CC BYFull-Text: http://www.mdpi.com/1996-1073/16/10/4186/pdfData sources: Multidisciplinary Digital Publishing Instituteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en16104186&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu