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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.

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.

This paper describes challenges and possible solution of hybrid electrical vehicles test systems with a special focus on hardware-in-the-loop (HIL) test bench. The degree of novelty of this work can be seen in the fact that development and test of ECU for hybrid electrical powertrains can move more and more from mechanical test benches with real automotive components to HIL test systems. The challenging task in terms of electrical interface between an electric motor ECU and an HIL system and necessary real-time capable simulation models for electric machines have been investigated and partly solved. Even cell balancing strategies performed by battery management systems (BMU) can be developed and tested using HIL technology with battery simulation models and a precise cell voltage simulation on electrical level.

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).

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.

The essential task of a battery management system (BMS) is to consistently operate the high-voltage battery in an optimum range. Due to the safety-critical nature of its components, prior testing of a BMS is absolutely necessary. Hardware-in-the-loop (HIL) simulation is a cost-effective and efficient tool for this. Testing the BMS on a HIL test bench requires an electronics unit to simulate the cell voltages and a scalable real-time battery model. This paper describes a HIL system that enables comprehensive testing of BMS components. Hardware and software solutions are proposed for the high requirements of these tests. The individual components are combined to make a modular system, and safety-critical aspects are examined. The paper shows that the system as developed fulfills all the requirements derived from the different test scenarios for BMS systems.

This presentation focuses on several examples of partnerships between tool suppliers and embedded software developers in which state-of-the-art tools are used to optimize a variety of electric and hybrid vehicle architectures. Projects with Automotive OEMs, Tier One Suppliers as well as with academic institutions will be described. Due to the growing complexity in multiple electronic control units (“ECUs”) inter-communicating over numerous network bus systems, combined with the challenge of controlling and maintaining charges for electric motors, vehicle development would be impossible without use of increasingly sophisticated tools. Hybrid drive trains are much more complex than conventional ones, they have at least one degree of freedom more.

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.

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.

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.