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Model-Based Range Prediction for Electric Cars and Trucks under Real-World Conditions

Center of Energy Technology (ZET), Chair of Measurement and Control Systems, Universität Bayreuth, Universitätsstr. 30, 95447 Bayreuth, Germany
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Academic Editor: Adolfo Dannier
Energies 2021, 14(18), 5804; https://doi.org/10.3390/en14185804
Received: 3 August 2021 / Revised: 6 September 2021 / Accepted: 7 September 2021 / Published: 14 September 2021
The further development of electric mobility requires major scientific efforts to obtain reliable data for vehicle and drive development. Practical experience has repeatedly shown that vehicle data sheets do not contain realistic consumption and range figures. Since the fear of low range is a significant obstacle to the acceptance of electric mobility, a reliable database can provide developers with additional insights and create confidence among vehicle users. This study presents a detailed, yet easy-to-implement and modular physical model for both passenger and commercial battery electric vehicles. The model takes consumption-relevant parameters, such as seasonal influences, terrain character, and driving behavior, into account. Without any a posteriori parameter adjustments, an excellent agreement with known field data and other experimental observations is achieved. This validation conveys much credibility to model predictions regarding the real-world impact on energy consumption and cruising range in standardized driving cycles. Some of the conclusions, almost impossible to obtain experimentally, are that winter conditions and a hilly terrain each reduce the range by 7–9%, and aggressive driving reduces the range by up to 20%. The quantitative results also reveal the important contributions of recuperation and rolling resistance towards the overall energy budget. View Full-Text
Keywords: battery electric vehicle; BEV; electric truck; cruising range; real-world conditions; physical model; range prediction; consumption shares; recuperation; rolling resistance battery electric vehicle; BEV; electric truck; cruising range; real-world conditions; physical model; range prediction; consumption shares; recuperation; rolling resistance
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MDPI and ACS Style

Dollinger, M.; Fischerauer, G. Model-Based Range Prediction for Electric Cars and Trucks under Real-World Conditions. Energies 2021, 14, 5804. https://doi.org/10.3390/en14185804

AMA Style

Dollinger M, Fischerauer G. Model-Based Range Prediction for Electric Cars and Trucks under Real-World Conditions. Energies. 2021; 14(18):5804. https://doi.org/10.3390/en14185804

Chicago/Turabian Style

Dollinger, Manfred, and Gerhard Fischerauer. 2021. "Model-Based Range Prediction for Electric Cars and Trucks under Real-World Conditions" Energies 14, no. 18: 5804. https://doi.org/10.3390/en14185804

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