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In recent years, the concept of hybrid test systems consisting of real and virtual parts emerged in the aerospace industry. The concept features a communication infrastructure that provides the standardized transport mechanisms required for interoperability. For example, this allows system integrators to easily reuse and exchange laboratory tests means, even if they originate from different suppliers. The “Virtual and Hybrid Testing Next Generation” (VHTNG) research project aims at creating a standard for such an infrastructure. One central aspect is the unified monitoring and control of the test equipment. So far, VHTNG has primarily focused on monitoring and controlling related aspects of the test bench in a local environment. However, recent events have repeatedly shown that it becomes increasingly important to monitor and control test benches remotely.

The main objective of testing is to evaluate the functionality, reliability, and operational safety of products. However, this objective makes testing a complex and expensive stage in the development process. From the perspective of an aircraft OEM, test cases are used to verify integration, system, and application levels. Therefore, test cases certify the products against the requirements using the black box testing approach. In doing so, a test plan defines a sequence of test cases whereby it sets up the environment, stimulates the fault, and then observes the system under test (SUT) for each case. Subsequently, the postprocessing of the test execution classifies the test plan as passed or failed. The ongoing digitization and interconnectedness between aircraft systems increase the complexity in functional testing. This trend leads to a high number of test cases and a multitude of reasons why a specific test-case fails.

This paper presents a demonstrator developed in the European CleanSky2 project MISSION (Modelling and Simulation Tools for Systems Integration on Aircraft). Its scope is the development towards a seamless integrated, interconnected toolchain enabling more efficient processes with less rework time in todays, highly collaborative aerospace domain design applications. The demonstration described here, consists of an open, modular and multitool platform implementation, using specific techniques to achieve fully traceable (early stage) requirements verification by virtual testing. The most promising approach is a model based integration along the whole process from requirements definition to the verified, integrated (and certified) system. Extending previous publications in this series, the paper introduces the motivation and briefly describes the technical background and a potential implementation of a workflow suitable for that target.

In the aerospace industry, methods for virtual testing cover an increasing range of test executions carried out during the development and test process of avionics systems. Over the last years, most companies have focused on questions regarding the evaluation and implementation of methods for virtual testing. However, it has become more and more important to seamlessly integrate virtual testing into the overall development process. For instance, a company’s test strategy might stipulate a combination of different methods, such as SIL and HIL simulation, in order to benefit from the advantages of both in the same test process. In this case, efforts concentrate on the optimization of the overall process, from test specification to test execution, as well as the test result evaluation and its alignment with methods for virtual testing.

Increasing diagnosis capabilities in modern engine electronic control units (ECUs), especially in the exhaust path, in terms of emission and engine aftertreatment control utilize on-board NOx prediction models. Nowadays it is an established approach at hardware-in-theloop (HIL) test benches to replicate the engine's steady-state NOx emissions on the basis of stationary engine data. However, this method might be unsuitable for internal ECU plausibility checks and ECU test conditions based on dynamic engine operations. Examples of proven methods for modeling the engine behavior in HIL system applications are so-called mean value engine models (MVEMs) and crank-angle-synchronous (in-cylinder) models. Of these two, only the in-cylinder model replicates the engine’s inner combustion process at each time step and can therefore be used for chemical-based emission simulation, because the formation of the relevant gas species is caused by the inner combustion states.

This paper presents an overview of a project called “Modelling and Simulation Tools for Systems Integration on Aircraft (MISSION)”. This is a collaborative project being developed under the European Union Clean Sky 2 Program, a public-private partnership bringing together aeronautics industrial leaders and public research organizations based in Europe. The provision of integrated modeling, simulation, and optimization tools to effectively support all stages of aircraft design remains a critical challenge in the Aerospace industry. In particular the high level of system integration that is characteristic of new aircraft designs is dramatically increasing the complexity of both design and verification. Simultaneously, the multi-physics interactions between structural, electrical, thermal, and hydraulic components have become more significant as the systems become increasingly interconnected.

Increasing productivity along the development and verification process of safety-related projects is an important aspect in today’s technological developments, which need to be ever more efficient. The increase of productivity can be achieved by improving the usability of software tools and decreasing the effort of qualifying the software tool for a safety-related project. For safety-critical systems, the output of software tools has to be verified in order to ensure the tools’ suitability for safety-relevant applications. Verification is particularly important for test automation tools that are used to run hardware-in-the-loop (HIL) tests of safety-related software automatically 24/7. This qualification of software tools requires advanced knowledge and effort. This problem can be solved if a tool is suitable for developing safety-related software. This paper explains how this can be achieved for a COTS test automation tool.

Advanced driver assistance systems (ADAS) are becoming increasingly important for today's commercial vehicles. It is therefore crucial that different ADAS functionalities interact seamlessly with existing electronic control unit (ECU) networks. For example, autonomous emergency braking (AEB) systems directly influence the brake ECU and engine control. It has already become impossible to reliably validate this growing interconnectedness of control interventions in vehicle behavior with prototype vehicles alone. The relevant tests must be brought into the lab at an earlier development stage to evaluate ECU interaction automatically. This paper presents an approach for using hardware-in-the-loop (HIL) simulation to validate ECU networks for extremely diverse ADAS scenarios, while taking into account real sensor data. In a laboratory environment, the sensor systems based on radars, cameras, and maps are stimulated realistically with a combination of simulation and animation.

To make the development of complex aircraft systems manageable and economical, tests must be performed as early as possible in the development process. The test goals are already set in advance before the first hardware for the ECUs exists, to be able to make statements about the system functions or possible malfunctions. This paper describes the requirements on and solutions for test systems for ECUs that arise from these goals. It especially focuses on how a seamless workflow and consistent use of test systems and necessary software tools can be achieved, from the virtual test of ECUs, which exist only as models, up to the test of real hardware. This will be shown in connection with a scalable, fully software-configurable hardware-in-the-loop (HIL) technology. The paper also covers the seamless use of software tools that are required for HIL testing throughout the different test phases, enabling the reuse of work products throughout the test phases.

Model-based development is the established way of developing embedded control algorithms, especially for safety-critical applications. The aim is to improve development efficiency and safety by developing the software at a high abstraction level (the model) and by generating the implementation (the C code) automatically from the model. Although model-based development focuses on the models themselves, downstream artifacts such as source code or executable object code have to be considered in the verification stage. Safety standards such as ISO 26262 require upper bounds to be determined for the required storage space or the execution time of real-time tasks, and the absence of run-time errors to be demonstrated. Static analysis tools are available which work at the code level and can prove the absence of such errors. However, the connection to the model level has to be explicitly established.

Hardware-in-the-loop (HIL) simulation is now a standard component in the vehicle development process as a method for testing electronic control unit (ECU) software. HIL simulation is used for all aspects of development, naturally including safety-relevant functions and systems. This applies to all test tasks (from function testing to release tests, testing a single ECU or an ECU network, and so on) and also to different vehicle domains: The drivetrain, vehicle dynamics, driver assistance systems, interior/comfort systems and infotainment are all tested by HIL simulation. At the same time, modern vehicles feature more and more safety-related systems such as Adaptive Cruise Control, Electronic Stability Program, Power Assisted Steering, and Integrated Chassis Management.

This paper will first introduce and classify the basic principles of architecture-driven software development and will briefly sketch the presumed development process. This background information is then used to explain extensions which enable current behavior modeling and code generation tools to operate as software component generators. The generation of AUTOSAR software components using dSPACE's production code generator TargetLink is described as an example.

The importance of electronics, especially software, has greatly increased over the last few years. Efforts to maintain a high level of software quality have made testing an important part of the development process. With the advent of model-based development, testing methods can be used not only on code level, but also on model level. Next to test execution itself, test development is seen as the most time- and cost-intensive part of the testing process. This paper outlines and classifies current approaches to model-based test development, with the aim of providing guidelines for test developers for choosing the method best suited to the type of system under test and the test objective.

Not only is the number of electronic control units (ECUs) in modern vehicles constantly increasing, the software of the ECUs is also becoming more complex. Both make testing a central task within the development of automotive electronics. Testing ECUs in real vehicles is time-consuming and costly, and comes very late in the automotive development process. It is therefore increasingly being replaced by laboratory tests using hardware-in-the-loop (HIL) simulation. While new software functions are still being developed or optimized, other functions are already undergoing certain tests, mostly on module level but also on system and integration level. To achieve the highest quality, testing must be done as early as possible within the development process. This paper describes the various test phases during the development of automotive electronics (from single function testing to network testing of all the ECUs of a vehicle).

This paper describes a new production code generator that meets both the requirements of code developers for efficient and reliable production code, as well as the desire of system engineers to establish a control design process based on simulation models that double as executable specifications for the ECU software. The production code generator supports automatic scaling, generates optimized fixed-point C code for microcontrollers like the Motorola 683xx, Siemens C16x, and Hitachi SH-2, and produces ASAP2 [1] calibration information. Benchmark results show that the autogenerated code can match or even exceed the efficiency of typical handwritten production code. Code quality is assured by design and by systematic, automatic, and extremely comprehensive test procedures.