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

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
Otros Autores: Scheubner, Stefan (auth)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Karlsruhe KIT Scientific Publishing 2022
Colección:Karlsruher Schriftenreihe Fahrzeugsystemtechnik
Materias:
Mechanical engineering & materials
Elektromobilität
Vorhersagen
Algorithmen
Fahrzeugtechnik
Energiemanagement
E-Mobility
Forecasting
Algorithms
Vehicle Technology
Energy Management
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009666902806719
Descripción
Sumario: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.
Descripción Física:1 electronic resource (192 p.)

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