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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Bibliografiset tiedot
Päätekijä:	Scheubner, Stefan (auth)
Aineistotyyppi:	Elektroninen Kirjan osa
Kieli:	englanti
Julkaistu:	Karlsruhe KIT Scientific Publishing 2022
Sarja:	Karlsruher Schriftenreihe Fahrzeugsystemtechnik
Aiheet:	Mechanical engineering & materials Elektromobilität Vorhersagen Algorithmen Fahrzeugtechnik Energiemanagement E-Mobility Forecasting Algorithms Vehicle Technology Energy Management
Linkit:	DOAB: download the publication DOAB: description of the publication
Tagit:	Lisää tagi Ei tageja, Lisää ensimmäinen tagi!

Stochastic Range Estimation Algorithms for Electric Vehicles using Data-Driven Learning Models Tekijä Scheubner, Stefan

Julkaistu 2022

OAPEN Library: download the publication
OAPEN Library: description of the publication

Elektroninen Kirjan osa

Stochastic Range Estimation Algorithms for Electric Vehicles using Data-Driven Learning Models

Stochastic Range Estimation Algorithms for Electric Vehicles using Data-Driven Learning Models Tekijä Scheubner, Stefan

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