Fink, D.; Maas, O.; Herda, D.; Ziaukas, Z.; Schweers, C. et al.: Data-Based Energy Demand Prediction for Hybrid Electrical Vehicles. In: SN Computer Science 5 (2024), Nr. 1, 192. DOI: https://doi.org/10.1007/s42979-023-02475-9
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. | |
License of this version: | CC BY 4.0 Unported |
Document Type: | Article |
Publishing status: | publishedVersion |
Issue Date: | 2024 |
Appears in Collections: | Fakultät für Maschinenbau |
pos. | country | downloads | ||
---|---|---|---|---|
total | perc. | |||
1 | ![]() |
Germany | 9 | 52.94% |
2 | ![]() |
United States | 3 | 17.65% |
3 | ![]() |
South Africa | 1 | 5.88% |
4 | ![]() |
No geo information available | 1 | 5.88% |
5 | ![]() |
France | 1 | 5.88% |
6 | ![]() |
China | 1 | 5.88% |
7 | ![]() |
Canada | 1 | 5.88% |
Hinweis
Zur Erhebung der Downloadstatistiken kommen entsprechend dem „COUNTER Code of Practice for e-Resources“ international anerkannte Regeln und Normen zur Anwendung. COUNTER ist eine internationale Non-Profit-Organisation, in der Bibliotheksverbände, Datenbankanbieter und Verlage gemeinsam an Standards zur Erhebung, Speicherung und Verarbeitung von Nutzungsdaten elektronischer Ressourcen arbeiten, welche so Objektivität und Vergleichbarkeit gewährleisten sollen. Es werden hierbei ausschließlich Zugriffe auf die entsprechenden Volltexte ausgewertet, keine Aufrufe der Website an sich.