Stochastic Range Estimation Algorithms for Electric Vehicles using Data-Driven Learning Models
This work aims at improving the energy consumption forecast of electric vehicles by enhancing the prediction with a notion of uncertainty. The algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts.
| Format: | eBook |
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| Language: | English |
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Karlsruhe
KIT Scientific Publishing
2022
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| Series: | Karlsruher Schriftenreihe Fahrzeugsystemtechnik
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| Subjects: | |
| Online Access: | Open Access: DOAB: description of the publication Open Access: DOAB, download the publication |
| Summary: | This work aims at improving the energy consumption forecast of electric vehicles by enhancing the prediction with a notion of uncertainty. The algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts. |
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| Physical Description: | 1 online resource (192 p.) |
| ISBN: | 9783731511663 KSP/1000143200 |
| Access: | Open Access |