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.

Bibliografiska uppgifter

Huvudupphov:	Scheubner, Stefan
Materialtyp:	Online
Språk:	engelska
Utgiven:	KIT Scientific Publishing 2022
Ämnen:	Elektromobilität Vorhersagen Algorithmen Fahrzeugtechnik Energiemanagement E-Mobility Forecasting Algorithms Vehicle Technology Energy Management
Länkar:	ONIX_20220620_9783731511663_74
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