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Energy Prediction Model Development and Parameter Estimation for Urban Battery Electric Buses with Real World Data

  • Many cities in Europe and around the world are concerned with reducing their CO2-emissions. One step on this agenda is the introduction of electric buses to replace combustion engines. The electrification of urban buses requires an accurate prediction of the energy demand. In this pa per, an energy model and the underlying calibration process is presented. This approach leverages substantial tracking data from 10 electric buses operated in Göttingen, Germany. It was shown that, with the use of additional information from the directly measured tracking data, like auxiliary power, charging power and vehicle weight, it is possible to precisely calibrate models based on physical equations with regard to generally poorly identifiable parameters like rolling friction coefficient or efficiency of the electric machine. With a multilayered approach for simulating the energy demand, it is possible to validate the results on the mechanical layer and on the electrical layer separately. This enables a far better parametrization and elimination ofMany cities in Europe and around the world are concerned with reducing their CO2-emissions. One step on this agenda is the introduction of electric buses to replace combustion engines. The electrification of urban buses requires an accurate prediction of the energy demand. In this pa per, an energy model and the underlying calibration process is presented. This approach leverages substantial tracking data from 10 electric buses operated in Göttingen, Germany. It was shown that, with the use of additional information from the directly measured tracking data, like auxiliary power, charging power and vehicle weight, it is possible to precisely calibrate models based on physical equations with regard to generally poorly identifiable parameters like rolling friction coefficient or efficiency of the electric machine. With a multilayered approach for simulating the energy demand, it is possible to validate the results on the mechanical layer and on the electrical layer separately. This enables a far better parametrization and elimination of uncertainties from the different parameters. Furthermore, we compare the results to other publications for sections with 1 km, 100 km and 230 km, respectively. The relative errors between the simulated and measured electrical power consumption are below 0.3%, 3% and 6.5%, respectively. Hence, the yielded model is appropriate for electric urban bus network planning applications. And the found parameters should be a good starting point for other energy prediction models. To further enable comparability with other approaches the dataset used for calibration is made publicly available.show moreshow less

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Author:Samuel WürtzORCiD, Denis Spiess, Benjamin Martin, Klaus BogenbergerORCiD, Ulrich GöhnerORCiD, Andreas RuppGND
URL / DOI:https://www.researchgate.net/publication/367238410_Energy_Prediction_Model_Development_and_Parameter_Estimation_for_Urban_Battery_Electric_Buses_with_Real_World_Data
URL / DOI:https://annualmeeting.mytrb.org/OnlineProgramArchive/Details/19446
Parent Title (English):2023 Transportation Research Board 102nd Annual Meeting
Document Type:conference proceeding (presentation)
Conference:2023 Transportation Research Board 102nd Annual Meeting, January 8–12 2023, Washington, D.C, USA
Language:English
Year of first Publication:2023
Number of pages:14 Seiten
Institutes:IFA – Institut für Innovative Fahrzeugantriebe
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften
Open Access:open_access
Research focus:FSP1: Energie
FSP2: Mobilität
FSP3: Produktion
Publication Lists:Rupp, Andreas
Spiess, Denis
Göhner, Ulrich
Würtz, Samuel
Martin, Benjamin
Publication reviewed:begutachtet
Release Date:2024/05/28
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