Fink, D.; Shugar, S.; Ziaukas, Z.; Schweers, C.; Trabelsi, A. et al.: Energy Demand Prediction in Hybrid Electrical Vehicles for Speed Optimization. In: Ploeg, J.; Helfert, M.; Berns, K.; Gusikhin, O. (eds.): Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems. Setúbal : SciTePress - Science and Technology Publications, Lda., 2022, S. 116-123. DOI:
https://doi.org/10.5220/0011075600003191
Zusammenfassung: |
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.
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Lizenzbestimmungen: |
CC BY-NC-ND 4.0 Unported - https://creativecommons.org/licenses/by-nc-nd/4.0/
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Publikationstyp: |
BookPart |
Publikationsstatus: |
publishedVersion |
Erstveröffentlichung: |
2022 |
Schlagwörter (englisch): |
Automobile drivers, Battery management systems, Hybrid vehicles, Intelligent vehicle highway systems, Mean square error, Optimization, Automotives, Energy demand prediction, Energy demands, Hybrid electrical vehicle, Optimization algorithms, Resource-efficient, Root mean square errors, Speed optimization, System modeling, energy demand prediction, System models, Forecasting
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Kontrollierte Schlagwörter: |
Konferenzschrift
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