Functionality and Safety Weaknesses in Integration of PhysicsBased Models on Critical Embedded Systems Embedded automotive software is currently showing trends towards model predictive control (MPC), virtual sensors or modelbased diagnosis, mainly used in advanced driver assistance systems (ADAS) and automated driving. Such applications use physical models in the control algorithms. The integration of physical models is a risky task, since weaknesses, such as the need for floatingpoint arithmetic and discretization or model properties, such as discontinuities and nonlinearities, quickly bring a project to a standstill or establish errors in the final product. The use of known verification and validation methods is often not possible or offers false safety guarantees. This article is intended to help developers understand and identify safety weaknesses and develop new verification and validation methods specifically adapted for physicsbased, critical, embedded code. For this purpose, corresponding weaknesses in current industrial projects with physicsbased systems have been identified and categorized. In this article, these are described and illustrated with examples from applications in order to get an idea of their relevance in the current context. On this basis, approaches for the analysis and diagnosis of potentially faulty code are proposed to motivate testers and quality managers to find new methods for error identification and validation of critical, physicsbased, embedded code.

Cyberphysical modelling in Modelica with modelreduction techniques Abstract   Objectoriented modelling of cyberphysical systems with Modelica and similar environments has brought many advantages, especially the efficient reuse of models and thus the possibility of creating powerful multidomain libraries. Unfortunately, the models have become highly complex, which causes serious problems during processing and execution. Consequently, verification and debugging is becoming an increasingly challenging task. The continuous investigation of simplifications and reductions in all phases of model developments is thus urgent.  The present paper deals with reduction methods based on metric ranking and preserve realisation, which means that the structure and the parameters of the model remain physically interpretable. Two modelreduction methods are described and implemented in Open Modelica. The first operates on a set of differentialalgebraic equations, and the second is based on modified bondgraphsreduction techniques. The latter approach is suitable for componentbased models in Modelica that are usually represented graphically with object diagrams. The paper briefly describes the research area, the problems of the adoption of the developed model reduction techniques to the Modelica environments, and the final implementation. Both proposed approaches are tested on the model of a car suspension system and briefly discussed.