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
Keyword(s):
Neural Networks, Scalable Component Model, Electrical Machine, Hybrid Electric Vehicles, System-level Design, Multilayer Perceptron.
Related
Ontology
Subjects/Areas/Topics:
Industrial Engineering
;
Informatics in Control, Automation and Robotics
;
Systems Modeling and Simulation
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)