This paper addresses the time- and information-intensive task of calibrating an electric power steering assistance system, which is optimised for energy efficiency and can be adapted to different vehicle variants. Hereby, the focus is on determining the customer-perceived properties steering feeling, energy consumption, system price and market availability date. It will be demonstrated how this aim can be achieved with a reduced testing scope by automated generation of behaviour models that predict the system behaviour in the defined parameter range.
Driving quality optimization based on cross-linked cause and effect chain models using the example of energy-efficient steering assistance
Proceedings
2015-06-19
26 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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