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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Xehetasun bibliografikoak
Egile nagusia:	Scheubner, Stefan (auth)
Formatua:	Baliabide elektronikoa Liburu kapitulua
Hizkuntza:	ingelesa
Argitaratua:	Karlsruhe KIT Scientific Publishing 2022
Saila:	Karlsruher Schriftenreihe Fahrzeugsystemtechnik 6
Gaiak:	Mechanical engineering & materials Elektromobilität Vorhersagen Algorithmen Fahrzeugtechnik Energiemanagement E-Mobility Forecasting Algorithms Vehicle Technology Energy Management
Sarrera elektronikoa:	OAPEN Library: download the publication OAPEN Library: description of the publication
Etiketak:	Etiketa erantsi Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!

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

Argitaratua 2022

DOAB: download the publication
DOAB: description of the publication

Baliabide elektronikoa Liburu kapitulua

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

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

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