Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 192 |
| Seitenumfang | 7 |
| Fachzeitschrift | SN Computer Science |
| Jahrgang | 5 |
| Publikationsstatus | Veröffentlicht - 11 Jan. 2024 |
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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Informatik (insg.)
- Angewandte Informatik
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Informatik (insg.)
- Computergrafik und computergestütztes Design
- Informatik (insg.)
- Theoretische Informatik und Mathematik
- Informatik (insg.)
- Artificial intelligence
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in: SN Computer Science, Jahrgang 5, 192, 11.01.2024.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Data-Based Energy Demand Prediction for Hybrid Electrical Vehicles
AU - Fink, Daniel
AU - Maas, Oliver
AU - Herda, Daniel
AU - Ziaukas, Zygimantas
AU - Schweers, Christoph
AU - Trabelsi, Ahmed
AU - Jacob, Hans Georg
N1 - Funding Information: Open Access funding enabled and organized by Projekt DEAL. The underlying project of this study was funded by IAV GmbH, Berlin, Germany.
PY - 2024/1/11
Y1 - 2024/1/11
N2 - 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.
AB - 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.
KW - Energy demand prediction
KW - Hybrid electrical vehicles
KW - Systems modeling
UR - http://www.scopus.com/inward/record.url?scp=85182223180&partnerID=8YFLogxK
U2 - 10.1007/s42979-023-02475-9
DO - 10.1007/s42979-023-02475-9
M3 - Article
AN - SCOPUS:85182223180
VL - 5
JO - SN Computer Science
JF - SN Computer Science
SN - 2662-995X
M1 - 192
ER -