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Authors: Patrick Petersen 1 ; Adam Thorgeirsson 2 ; Stefan Scheubner 3 ; Stefan Otten 1 ; Frank Gauterin 4 and Eric Sax 1

Affiliations: 1 FZI Research Center for Information Technology, Haid-und-Neu-Straße 10-14, 76131 Karlsruhe and Germany ; 2 Dr. Ing. h.c. F. Porsche AG, Porschestraße 911, 71287 Weissach, Germany, Institute of Vehicle System Technology, Karlsruhe Institute of Technology, 76131 Karlsruhe and Germany ; 3 Dr. Ing. h.c. F. Porsche AG, Porschestraße 911, 71287 Weissach and Germany ; 4 Institute of Vehicle System Technology, Karlsruhe Institute of Technology, 76131 Karlsruhe and Germany

ISBN: 978-989-758-374-2

Keyword(s): Automotive Systems Engineering, Verification and Validation, Testing, Range Estimation, Battery Electric Vehicles.

Abstract: Estimating the range of battery electric vehicles is one of the most challenging topics for the current trend in the automotive industry, the electrification of vehicles. Range anxiety still limits the adoption of battery electric vehicles. Since the range estimation is dependent on different influencing factors, complex algorithms to accurately estimate the vehicles consumption are required. To evaluate the accuracy of data-driven machine learning algorithms, an exhaustive training and validation procedure is mandatory. In this paper, we propose a novel methodology for the development and validation of range estimation algorithms based on machine learning validation approaches. The proposed methodology considers the evaluation of driver-specific and driver-unspecific performance. In addition, an error measure is introduced to assess the performance of range estimation algorithms. This approach is demonstrated and evaluated on a set of recorded real-world driving data. It is shown tha t our approach helps to analyze the performance of the range estimation algorithm and the influences of different parameter sets. (More)

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Paper citation in several formats:
Petersen, P.; Thorgeirsson, A.; Scheubner, S.; Otten, S.; Gauterin, F. and Sax, E. (2019). Training and Validation Methodology for Range Estimation Algorithms.In Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-374-2, pages 434-443. DOI: 10.5220/0007717004340443

@conference{vehits19,
author={Patrick Petersen. and Adam Thor Thorgeirsson. and Stefan Scheubner. and Stefan Otten. and Frank Gauterin. and Eric Sax.},
title={Training and Validation Methodology for Range Estimation Algorithms},
booktitle={Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2019},
pages={434-443},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007717004340443},
isbn={978-989-758-374-2},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Training and Validation Methodology for Range Estimation Algorithms
SN - 978-989-758-374-2
AU - Petersen, P.
AU - Thorgeirsson, A.
AU - Scheubner, S.
AU - Otten, S.
AU - Gauterin, F.
AU - Sax, E.
PY - 2019
SP - 434
EP - 443
DO - 10.5220/0007717004340443

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