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.
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| Main Author: | |
|---|---|
| Format: | Electronic Book Chapter |
| Language: | English |
| Published: |
Karlsruhe
KIT Scientific Publishing
2022
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| Series: | Karlsruher Schriftenreihe Fahrzeugsystemtechnik
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| Subjects: | |
| Online Access: | DOAB: download the publication DOAB: description of the publication |
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| 100 | 1 | |a Scheubner, Stefan |4 auth | |
| 245 | 1 | 0 | |a Stochastic Range Estimation Algorithms for Electric Vehicles using Data-Driven Learning Models |
| 260 | |a Karlsruhe |b KIT Scientific Publishing |c 2022 | ||
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| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a 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. | ||
| 540 | |a Creative Commons |f by/4.0 |2 cc |4 http://creativecommons.org/licenses/by/4.0 | ||
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| 650 | 7 | |a Mechanical engineering & materials |2 bicssc | |
| 653 | |a Elektromobilität | ||
| 653 | |a Vorhersagen | ||
| 653 | |a Algorithmen | ||
| 653 | |a Fahrzeugtechnik | ||
| 653 | |a Energiemanagement | ||
| 653 | |a E-Mobility | ||
| 653 | |a Forecasting | ||
| 653 | |a Algorithms | ||
| 653 | |a Vehicle Technology | ||
| 653 | |a Energy Management | ||
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| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/84371 |7 0 |z DOAB: description of the publication |