Planning and Scheduling System for Electric Vehicle Charging
The paper presents a novel approach to instant on-the-road electric vehicle charging scheduling planning. It benefits from a mixed-integer mathematical optimization model employed to efficiently select charging stations along the route. It also utilizes machine learning models to approximate the tra...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11053806/ |
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| author | Piotr Palka Tomasz Sliwinski Przemyslaw Kaszynski Marta Kuta Bogdan Ruszczak Marcin Malec Piotr Saluga Jacek Kaminski |
| author_facet | Piotr Palka Tomasz Sliwinski Przemyslaw Kaszynski Marta Kuta Bogdan Ruszczak Marcin Malec Piotr Saluga Jacek Kaminski |
| author_sort | Piotr Palka |
| collection | DOAJ |
| description | The paper presents a novel approach to instant on-the-road electric vehicle charging scheduling planning. It benefits from a mixed-integer mathematical optimization model employed to efficiently select charging stations along the route. It also utilizes machine learning models to approximate the traction battery state of charge using data on driving conditions (energy, speed, travel time, etc.) for individual route sections, considering the driver’s habits and preferences. The research details the functional requirements of this procedure application and its architecture. A step-by-step description of the optimization model is provided, in particular, the constraints responsible for the non-linear model of charging time, the varying time zone-dependent trip parameters (including rush hours), as well as the used optimization criteria, and the scalarization methods. Unlike other existing methods, the combination of the aforementioned mixed-integer mathematical optimization model and machine learning predictors allows for consideration of several factors and adaptation to actual data. Experiments are carried out on the operation of the application, including very long routes (with the actual Warsaw-Zakopane, 412 km long test drives). The results show enhanced, satisfactory execution times, from 0.088 [s] (for a simple model without time zones and with a linear charging model) to 0.365 [s] (for a model without time zones and with a non-linear charging model). |
| format | Article |
| id | doaj-art-d7f51712535d4b59adf663a2ff04387b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d7f51712535d4b59adf663a2ff04387b2025-08-20T03:33:34ZengIEEEIEEE Access2169-35362025-01-011311390511392310.1109/ACCESS.2025.358392911053806Planning and Scheduling System for Electric Vehicle ChargingPiotr Palka0https://orcid.org/0000-0002-0006-363XTomasz Sliwinski1https://orcid.org/0000-0002-5111-1830Przemyslaw Kaszynski2https://orcid.org/0000-0002-0600-4374Marta Kuta3https://orcid.org/0000-0003-0163-5801Bogdan Ruszczak4https://orcid.org/0000-0003-1089-1778Marcin Malec5https://orcid.org/0000-0003-4587-9613Piotr Saluga6https://orcid.org/0000-0002-7782-9947Jacek Kaminski7https://orcid.org/0000-0001-7514-8761Institute of Control and Computation Engineering, Warsaw University of Technology, Warsaw, PolandInstitute of Control and Computation Engineering, Warsaw University of Technology, Warsaw, PolandPolish Academy of Sciences, Mineral and Energy Economy Research Institute, Kraków, PolandFaculty of Energy and Fuels, AGH University of Krakow, Kraków, PolandFaculty of Computer Science, Opole University of Technology, Opole, PolandPolish Academy of Sciences, Mineral and Energy Economy Research Institute, Kraków, PolandFaculty of Applied Sciences, WSB University, Dąbrowa Górnicza, PolandPolish Academy of Sciences, Mineral and Energy Economy Research Institute, Kraków, PolandThe paper presents a novel approach to instant on-the-road electric vehicle charging scheduling planning. It benefits from a mixed-integer mathematical optimization model employed to efficiently select charging stations along the route. It also utilizes machine learning models to approximate the traction battery state of charge using data on driving conditions (energy, speed, travel time, etc.) for individual route sections, considering the driver’s habits and preferences. The research details the functional requirements of this procedure application and its architecture. A step-by-step description of the optimization model is provided, in particular, the constraints responsible for the non-linear model of charging time, the varying time zone-dependent trip parameters (including rush hours), as well as the used optimization criteria, and the scalarization methods. Unlike other existing methods, the combination of the aforementioned mixed-integer mathematical optimization model and machine learning predictors allows for consideration of several factors and adaptation to actual data. Experiments are carried out on the operation of the application, including very long routes (with the actual Warsaw-Zakopane, 412 km long test drives). The results show enhanced, satisfactory execution times, from 0.088 [s] (for a simple model without time zones and with a linear charging model) to 0.365 [s] (for a model without time zones and with a non-linear charging model).https://ieeexplore.ieee.org/document/11053806/Electric vehicleselectromobilityEVmachine learningmixed-integer programmingscheduling |
| spellingShingle | Piotr Palka Tomasz Sliwinski Przemyslaw Kaszynski Marta Kuta Bogdan Ruszczak Marcin Malec Piotr Saluga Jacek Kaminski Planning and Scheduling System for Electric Vehicle Charging IEEE Access Electric vehicles electromobility EV machine learning mixed-integer programming scheduling |
| title | Planning and Scheduling System for Electric Vehicle Charging |
| title_full | Planning and Scheduling System for Electric Vehicle Charging |
| title_fullStr | Planning and Scheduling System for Electric Vehicle Charging |
| title_full_unstemmed | Planning and Scheduling System for Electric Vehicle Charging |
| title_short | Planning and Scheduling System for Electric Vehicle Charging |
| title_sort | planning and scheduling system for electric vehicle charging |
| topic | Electric vehicles electromobility EV machine learning mixed-integer programming scheduling |
| url | https://ieeexplore.ieee.org/document/11053806/ |
| work_keys_str_mv | AT piotrpalka planningandschedulingsystemforelectricvehiclecharging AT tomaszsliwinski planningandschedulingsystemforelectricvehiclecharging AT przemyslawkaszynski planningandschedulingsystemforelectricvehiclecharging AT martakuta planningandschedulingsystemforelectricvehiclecharging AT bogdanruszczak planningandschedulingsystemforelectricvehiclecharging AT marcinmalec planningandschedulingsystemforelectricvehiclecharging AT piotrsaluga planningandschedulingsystemforelectricvehiclecharging AT jacekkaminski planningandschedulingsystemforelectricvehiclecharging |