Komplexitätsbeherrschung von Motorsteuerungs-Funktionalitäten

Zimmermann, Michael; Bleile, Thomas; Heiber, Friedrun; Henle, Alexander

doi:10.1007/s35146-014-2003-z

Cited by 3 publications

(1 citation statement)

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“…Original equipment manufacturers (OEM) as well as Tier 1 suppliers are aiming to cope with these demands by using mathematical models of physical systems (referred to as physical models) as part of the control software. For example, in Zimmermann et al (2015) physical models are considered essential to manage the growing complexity of Diesel engine control as map-based approaches lead to an overwhelming calibration effort to satisfy the requirements of real driving emissions (RDE). Englert et al (2019) present a framework for embedded non-linear model predictive controllers (NMPC) as a very generic approach of integrating physical models into a controller.…”

Section: Motivationmentioning

confidence: 99%

eFMI: An open standard for physical models in embedded software

Lenord¹,

Otter²,

Bürger³

et al. 2021

Linköping Electronic Conference Proceedings

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This paper summarizes the final research results of the ITEA3 project EMPHYSIS (embedded systems with physical models in the production code software). Its core achievement is the new open eFMI Standard enabling automated workflows from high-level mathematical models of physical systems (referred to as physical models) to automotive compliant embedded software. eFMI (FMI for embedded systems) defines a container architecture for model exchange and testing. Multiple representations from an intermediate representation of sampled algorithms (GALEC) to production and binary code for specific embedded targets are maintained in a traceable workspace. The successful integration of the developed eFMI tooling is demonstrated by a comprehensive open source Modelica test cases library and industrial demonstrators. The readiness of the proposed approach is proven by compliance checks according to common automotive code quality standards like MISRA C:2012 and a performance benchmark in terms of runtime and resource demand in comparison with state-of-the-art hand coded solutions.

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Section: Motivationmentioning

confidence: 99%