A Physics-Informed Cold-Start Capability for xEV Charging Recommender System
An effortless charging experience will boost electric vehicle (xEV) adoption and assure driver satisfaction. Tailoring the charging experience incorporating smart algorithms introduces an exciting set of development opportunities. The goal of a smart charging algorithm is to lay down an accurate est...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/10697286/ |
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| author | Raik Orbay Aditya Pratap Singh Johannes Emilsson Michele Becciani Evelina Wikner Victor Gustafson Torbjorn Thiringer |
| author_facet | Raik Orbay Aditya Pratap Singh Johannes Emilsson Michele Becciani Evelina Wikner Victor Gustafson Torbjorn Thiringer |
| author_sort | Raik Orbay |
| collection | DOAJ |
| description | An effortless charging experience will boost electric vehicle (xEV) adoption and assure driver satisfaction. Tailoring the charging experience incorporating smart algorithms introduces an exciting set of development opportunities. The goal of a smart charging algorithm is to lay down an accurate estimation of charging power needs for each user. As recommender systems (RS) are frequently used for tailored services and products, a novel RS based approach is developed in this study. Based on a collaborative-filtering principle, an RS agent will customize charging power transient prioritizing the physical principles governing the battery system, correlated to customer preferences. However, parallel to other RS applications, a collaborative-filtering for charging power transient design may suffer from the cold-start problem. This paper thus aims to prescribe a remedy for the cold-start problem encountered in RS specifically for charging power transient design. The RS is cold-started based on multiphysical modelling, combined with customer driving styles. It is shown that using 7 fundamental charging power transients would capture about 70% of a set of representative charging power transient population. Matching a unsupervised learning based clustering pipeline for 7 possible customer driving styles, an RS agent can prescribe 7 charging power transients automatically and cold-start the RS until more data is available. |
| format | Article |
| id | doaj-art-1182cb529b084988aee1d923432de4e2 |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-1182cb529b084988aee1d923432de4e22025-01-30T00:04:38ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302024-01-0151457146910.1109/OJVT.2024.346957710697286A Physics-Informed Cold-Start Capability for xEV Charging Recommender SystemRaik Orbay0https://orcid.org/0000-0002-0339-1807Aditya Pratap Singh1https://orcid.org/0000-0003-4059-7844Johannes Emilsson2Michele Becciani3https://orcid.org/0000-0001-6758-8257Evelina Wikner4https://orcid.org/0000-0002-7203-6243Victor Gustafson5Torbjorn Thiringer6https://orcid.org/0000-0001-5777-1242Volvo Car Corporation - 97100 Propulsion and Energy - Strategy and Execution, Torslanda PVOSG 22, Gothenburg, SwedenVolvo Car Corporation - 97100 Propulsion and Energy - Strategy and Execution, Torslanda PVOSG 22, Gothenburg, SwedenVolvo Car Corporation - 97100 Propulsion and Energy - Strategy and Execution, Torslanda PVOSG 22, Gothenburg, SwedenAB Volvo, Gothenburg, SwedenDepartment of Electrical Engineering, Chalmers Technology University, Gothenburg, SwedenVolvo Car Corporation - 97100 Propulsion and Energy - Strategy and Execution, Torslanda PVOSG 22, Gothenburg, SwedenVolvo Car Corporation - 97100 Propulsion and Energy - Strategy and Execution, Torslanda PVOSG 22, Gothenburg, SwedenAn effortless charging experience will boost electric vehicle (xEV) adoption and assure driver satisfaction. Tailoring the charging experience incorporating smart algorithms introduces an exciting set of development opportunities. The goal of a smart charging algorithm is to lay down an accurate estimation of charging power needs for each user. As recommender systems (RS) are frequently used for tailored services and products, a novel RS based approach is developed in this study. Based on a collaborative-filtering principle, an RS agent will customize charging power transient prioritizing the physical principles governing the battery system, correlated to customer preferences. However, parallel to other RS applications, a collaborative-filtering for charging power transient design may suffer from the cold-start problem. This paper thus aims to prescribe a remedy for the cold-start problem encountered in RS specifically for charging power transient design. The RS is cold-started based on multiphysical modelling, combined with customer driving styles. It is shown that using 7 fundamental charging power transients would capture about 70% of a set of representative charging power transient population. Matching a unsupervised learning based clustering pipeline for 7 possible customer driving styles, an RS agent can prescribe 7 charging power transients automatically and cold-start the RS until more data is available.https://ieeexplore.ieee.org/document/10697286/Electric vehiclesfast chargingheat transferphysics-aware recommender systemRS cold-startthermomechatronic modelling |
| spellingShingle | Raik Orbay Aditya Pratap Singh Johannes Emilsson Michele Becciani Evelina Wikner Victor Gustafson Torbjorn Thiringer A Physics-Informed Cold-Start Capability for xEV Charging Recommender System IEEE Open Journal of Vehicular Technology Electric vehicles fast charging heat transfer physics-aware recommender system RS cold-start thermomechatronic modelling |
| title | A Physics-Informed Cold-Start Capability for xEV Charging Recommender System |
| title_full | A Physics-Informed Cold-Start Capability for xEV Charging Recommender System |
| title_fullStr | A Physics-Informed Cold-Start Capability for xEV Charging Recommender System |
| title_full_unstemmed | A Physics-Informed Cold-Start Capability for xEV Charging Recommender System |
| title_short | A Physics-Informed Cold-Start Capability for xEV Charging Recommender System |
| title_sort | physics informed cold start capability for xev charging recommender system |
| topic | Electric vehicles fast charging heat transfer physics-aware recommender system RS cold-start thermomechatronic modelling |
| url | https://ieeexplore.ieee.org/document/10697286/ |
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