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An Enhanced Path Planner for Electric Vehicles Considering User-Defined Time Windows and Preferences

Author

Listed:
  • Maximiliano Cubillos

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy)

  • Mauro Dell’Amico

    (Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Via Amendola 2, 42122 Reggio Emilia, Italy)

  • Ola Jabali

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy)

  • Federico Malucelli

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy)

  • Emanuele Tresoldi

    (Dipartimento di Informatica, Università degli Studi di Milano, Via Celoria 18, 20133 Milano, Italy)

Abstract

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.

Suggested Citation

  • Maximiliano Cubillos & Mauro Dell’Amico & Ola Jabali & Federico Malucelli & Emanuele Tresoldi, 2023. "An Enhanced Path Planner for Electric Vehicles Considering User-Defined Time Windows and Preferences," Energies, MDPI, vol. 16(10), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4173-:d:1150028
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    References listed on IDEAS

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