Energy Demand Prediction in Hybrid Electrical Vehicles for Speed Optimization

verfasst von

Daniel Fink, Sean Shugar, Zygimantas Ziaukas, Christoph Schweers, Ahmed Trabelsi, Hans-Georg Jacob

Abstract

Targeting a resource-efficient automotive traffic, modern driver assistance systems include speed optimization algorithms to minimize the vehicle’s energy demand, based on predictive route data. Within these algorithms, the required energy for upcoming operation points has to be determined. This paper presents a model-based approach, to predict the energy demand of a parallel hybrid electrical vehicle, which is suitable to be used in speed optimization algorithms. It relies on separate models for the individual power train components, and is identified for a real test vehicle. On route sections of 5 to 7 km the averaged root mean square error for the state of charge prediction results to 0.91% while the required amount of fuel can be predicted with an averaged root mean square error of 0.05 liters.

Organisationseinheit(en)

Institut für Mechatronische Systeme

Externe Organisation(en)

IAV GmbH

Typ

Aufsatz in Konferenzband

Band

1

Seiten

116 - 123

Anzahl der Seiten

8

Publikationsdatum

03.05.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Fahrzeugbau, Steuerungs- und Systemtechnik, Verkehr

Elektronische Version(en)

https://doi.org/10.5220/0011075600003191 (Zugang: Offen)