Data-Based Energy Demand Prediction for Hybrid Electrical Vehicles

verfasst von

Daniel Fink, Oliver Maas, Daniel Herda, Zygimantas Ziaukas, Christoph Schweers, Ahmed Trabelsi, Hans Georg Jacob

Abstract

To achieve a resource-efficient automotive traffic, modern driver assistance systems minimize the vehicle’s energy demand through speed optimization algorithms. Based on predictive route data, the required energy for upcoming operation points has to be determined. This paper presents a method to predict the energy demand of a hybrid electrical vehicle. Within this method, data-based approaches, such as neural networks, Gaussian processes, and look-up tables, are applied and assessed regarding their ability to predict the behavior of separate powertrain parts. The applied approaches are trained using measured data of a test vehicle. As a result, for every separate powertrain part, the best-suited data-based approach is selected to obtain an optimal energy demand prediction method. On a validation data set, this method is able to predict the transmission ratio of the gearbox causing a rmse of 0.426. The combustion engine’s torque prediction results in an rmse of 19.01 Nm and the electric motor torque prediction to 19.11 Nm. The root mean square error of the motor voltage results to 1.211 V.

Organisationseinheit(en)

Institut für Mechatronische Systeme

Externe Organisation(en)

IAV GmbH

Typ

Artikel

Journal

SN Computer Science

Band

5

Anzahl der Seiten

7

ISSN

2662-995X

Publikationsdatum

11.01.2024

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Informatik (insg.), Angewandte Informatik, Computernetzwerke und -kommunikation, Computergrafik und computergestütztes Design, Theoretische Informatik und Mathematik, Artificial intelligence

Elektronische Version(en)

https://doi.org/10.1007/s42979-023-02475-9 (Zugang: Offen)