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 |
|---|---|
| Language: | English |
| Published: |
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 |
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| 720 | 1 | |a Scheubner, Stefan |4 aut | |
| 245 | 0 | 0 | |a Stochastic Range Estimation Algorithms for Electric Vehicles using Data-Driven Learning Models |
| 260 | |a Karlsruhe |b KIT Scientific Publishing |c 2022 | ||
| 300 | |a 1 online resource (192 p.) | ||
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| 506 | 0 | |a Open Access |f Unrestricted online access |2 star | |
| 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 |u http://creativecommons.org/licenses/by/4.0 | ||
| 546 | |a English | ||
| 650 | 7 | |a Mechanical engineering & materials |2 bicssc | |
| 653 | |a Algorithmen | ||
| 653 | |a Algorithms | ||
| 653 | |a E-Mobility | ||
| 653 | |a Elektromobilität | ||
| 653 | |a Energiemanagement | ||
| 653 | |a Energy Management | ||
| 653 | |a Fahrzeugtechnik | ||
| 653 | |a Forecasting | ||
| 653 | |a Vehicle Technology | ||
| 653 | |a Vorhersagen | ||
| 793 | 0 | |a DOAB Library. | |
| 856 | 4 | 0 | |u https://directory.doabooks.org/handle/20.500.12854/84371 |7 0 |z Open Access: DOAB: description of the publication |
| 856 | 4 | 0 | |u https://library.oapen.org/bitstream/20.500.12657/56964/1/9783731511663.pdf |7 0 |z Open Access: DOAB, download the publication |