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.

Detalles Bibliográficos

Autor Principal:	Scheubner, Stefan
Formato:	Online
Idioma:	inglés
Publicado:	KIT Scientific Publishing 2022
Subjects:	Elektromobilität Vorhersagen Algorithmen Fahrzeugtechnik Energiemanagement E-Mobility Forecasting Algorithms Vehicle Technology Energy Management
Acceso en liña:	ONIX_20220620_9783731511663_74
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