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