Few-Shot Learning for Triplet-Based EV Energy Consumption Estimation
Predicting the energy consumption of an electric vehicle (EV) is often relevant when planning and managing electric mobility. The prediction is challenging as EV energy consumption is highly variable and dependent on context. First, this paper proposes an integrated framework for the collection of o...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2025.2474785 |
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| _version_ | 1849393706342285312 |
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| author | Alminas Čivilis Linas Petkevičius Simonas Šaltenis Kristian Torp Ieva Markucevičiūtė-Vinckė |
| author_facet | Alminas Čivilis Linas Petkevičius Simonas Šaltenis Kristian Torp Ieva Markucevičiūtė-Vinckė |
| author_sort | Alminas Čivilis |
| collection | DOAJ |
| description | Predicting the energy consumption of an electric vehicle (EV) is often relevant when planning and managing electric mobility. The prediction is challenging as EV energy consumption is highly variable and dependent on context. First, this paper proposes an integrated framework for the collection of online telematic data, processing of this data, online maintenance of statistics, and machine-learning-based prediction of travel time and energy consumption. A key feature of the proposed framework is the preprocessing of the trajectory data into triplets, a convenient data unit that captures the relevant context necessary for effective energ y prediction. The second contribution of the paper addresses the effective management of drastic change in context through robust energy prediction models. In particular, using few-shot learning techniques, we tackle the problem of the need to create different energy prediction models for different EV types, from small EVs to electric buses. Experimental results on three different data sets demonstrate how energy prediction models adapt to different EV types. |
| format | Article |
| id | doaj-art-4238ab7fbfa74bc49fa3f54dde3e2c8c |
| institution | Kabale University |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-4238ab7fbfa74bc49fa3f54dde3e2c8c2025-08-20T03:40:21ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452025-12-0139110.1080/08839514.2025.2474785Few-Shot Learning for Triplet-Based EV Energy Consumption EstimationAlminas Čivilis0Linas Petkevičius1Simonas Šaltenis2Kristian Torp3Ieva Markucevičiūtė-Vinckė4Institute of Computer Science, Vilnius University, Vilnius, LithuaniaInstitute of Computer Science, Vilnius University, Vilnius, LithuaniaInstitute of Computer Science, Vilnius University, Vilnius, LithuaniaDepartment of Computer Science, Aalborg University, Aalborg, DenmarkNormalis Tech, Vilnius, LithuaniaPredicting the energy consumption of an electric vehicle (EV) is often relevant when planning and managing electric mobility. The prediction is challenging as EV energy consumption is highly variable and dependent on context. First, this paper proposes an integrated framework for the collection of online telematic data, processing of this data, online maintenance of statistics, and machine-learning-based prediction of travel time and energy consumption. A key feature of the proposed framework is the preprocessing of the trajectory data into triplets, a convenient data unit that captures the relevant context necessary for effective energ y prediction. The second contribution of the paper addresses the effective management of drastic change in context through robust energy prediction models. In particular, using few-shot learning techniques, we tackle the problem of the need to create different energy prediction models for different EV types, from small EVs to electric buses. Experimental results on three different data sets demonstrate how energy prediction models adapt to different EV types.https://www.tandfonline.com/doi/10.1080/08839514.2025.2474785 |
| spellingShingle | Alminas Čivilis Linas Petkevičius Simonas Šaltenis Kristian Torp Ieva Markucevičiūtė-Vinckė Few-Shot Learning for Triplet-Based EV Energy Consumption Estimation Applied Artificial Intelligence |
| title | Few-Shot Learning for Triplet-Based EV Energy Consumption Estimation |
| title_full | Few-Shot Learning for Triplet-Based EV Energy Consumption Estimation |
| title_fullStr | Few-Shot Learning for Triplet-Based EV Energy Consumption Estimation |
| title_full_unstemmed | Few-Shot Learning for Triplet-Based EV Energy Consumption Estimation |
| title_short | Few-Shot Learning for Triplet-Based EV Energy Consumption Estimation |
| title_sort | few shot learning for triplet based ev energy consumption estimation |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2025.2474785 |
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