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Authors: Christian Gletter 1 ; Andre Mayer 1 ; Josef Kallo 2 ; Thomas Winsel 3 and Oliver Nelles 4

Affiliations: 1 Daimler AG, Stuttgart and Germany ; 2 Faculty of Engineering, Computer Sciences and Psychology, Ulm University and Germany ; 3 Department of Mechanical Engineering, Kempten University of Applied Sciences and Germany ; 4 Department of Mechanical Engineering, University of Siegen and Germany

ISBN: 978-989-758-374-2

Keyword(s): Neural Networks, Scalable Component Model, Electrical Machine, Hybrid Electric Vehicles, System-level Design, Multilayer Perceptron.

Abstract: To find the optimal system-level design of hybrid electric vehicles (HEVs), component models are used in simulations to evaluate a large number of different designs within a high dimensional design space. As the electrical machine (EM) represents a key component of the HEV powertrain in terms of energy consumption, models require scalability and sufficient accuracy with manageable computational effort. This paper presents a novel approach for the development of scalable EM models based on Neural Networks (NN). The models are trained with data derived by a Finite Element Analysis (FEA) based scaling procedure and capable to represent the characteristics of a wide range of EM designs without the incorporation of further details. Once a model is trained, it can be directly used in system-level design optimization. The practicality of the model is proven within an exemplary simulation study and its goodness of fit to the training data is validated by a statistical analysis. This approach can help to reduce the computational effort of EM efficiency maps calculation, since only a small number of time-consuming FEA based scaling simulations must be performed prior to the optimization. (More)

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Paper citation in several formats:
Gletter, C.; Mayer, A.; Kallo, J.; Winsel, T. and Nelles, O. (2019). A Novel Approach for Development of Neural Network based Electrical Machine Models for HEV System-level Design Optimization.In Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-374-2, pages 17-24. DOI: 10.5220/0007570300170024

@conference{vehits19,
author={Christian Gletter. and Andre Mayer. and Josef Kallo. and Thomas Winsel. and Oliver Nelles.},
title={A Novel Approach for Development of Neural Network based Electrical Machine Models for HEV System-level Design Optimization},
booktitle={Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2019},
pages={17-24},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007570300170024},
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 - A Novel Approach for Development of Neural Network based Electrical Machine Models for HEV System-level Design Optimization
SN - 978-989-758-374-2
AU - Gletter, C.
AU - Mayer, A.
AU - Kallo, J.
AU - Winsel, T.
AU - Nelles, O.
PY - 2019
SP - 17
EP - 24
DO - 10.5220/0007570300170024

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