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Gasoline Controlled Auto Ignition offers a high CO2 emission reduction potential, which is comparable to state-of-the-art, lean stratified operated gasoline engines. Contrary to the latter, GCAI low temperature combustion avoids NOX emissions, thereby trying to avoid extensive exhaust aftertreatment. The challenges remain in a restricted operation range due to combustion instabilities and a high sensitivity towards changing boundary conditions like ambient temperature, intake pressure or fuel properties. Once combustion shows instability, cyclic fluctuations are observed. These appear to have near-chaotic behavior but are characterized by a superposition of clearly deterministic and stochastic effects. Previous works show that the fluctuations can be predicted precisely when taking cycle-tocycle correlations into account. This work extends current approaches by focusing on additional dependencies within one single combustion cycle.

Automotive technology is rapidly changing with electrification of vehicles, driver assistance systems, advanced safety systems etc. This advancement in technology is making the task of validation and verification of embedded software complex and challenging. In addition to the component testing, integration testing imposes even tougher requirements for software testing. To meet these challenges dSPACE is continuously evolving the Hardware-In-the-Loop (HIL) technology to provide a systematic way to manage this task. The paper presents developments in the HIL hardware technology with latest quad-core processors, FPGA based I/O technology and communication bus systems such as Flexray. Also presented are developments of the software components such as advanced user interfaces, GPS information integration, real-time testing and simulation models. This paper provides a real-world example of implication of integration testing on HIL environment for Chassis Controls.

The technological development in the field of automotive electronics is proceeding at almost break-neck speed. The functions being developed and integrated into cars are growing in complexity and volume. With the increasing number and variety of sensors and actuators, electronics have to handle a greater amount of data, and the acquisition and generation of I/O signals is also growing in complexity, for example, in engine management applications. Moreover, intelligent and complex algorithms need to be processed in a minimum of time. This all intensifies the need for Rapid Control Prototyping (RCP), a proven method of decisively speeding up the model-based software development process of automotive electronic control units (ECUs) [1],[2]. All these demanding tasks, including connecting sensors and actuators to the RCP system, need to be performed within a standard prototyping environment.

Hardware-in-the-loop (HIL) testing is used by commercial vehicle original equipment manufacturers (OEMs) in several fields of electronics development. HIL tests are a part of the standard development process for engine and machine control systems. For electronic control units (ECUs), not only the HIL test of the hardware but also the controller software validation is very important. For hardware diagnostics validation, a dynamic simulation of the real system could be omitted and an open-loop test of the controller is sufficient in most cases. For most controller software validation including OBD (on-board diagnosis) tests, detailed but real-time capable models have to be used. This article describes the needs and challenges of models in hardware-in-the-loop (HIL) based testing, taking into account the wide range of commercial and off-highway vehicles.

The development of perception functions for tomorrow’s automated vehicles is driven by enormous amounts of data: often exceeding a gigabyte per second and reaching into the terabytes per hour. Data is typically gathered by a fleet of dozens of mule vehicles which multiply the data generated into the hundreds of petabytes per year. Traditional methods for fueling data-driven development would record every bit of every second of a data logging drive on solid-state drives located on a PC in the vehicle. Recorded data must then be exported from these drives using an upload station which pushes to the data lake after arriving back at the garage. This paper considers different techniques for curating logged data.

Model-based software development and automatic code generation have become increasingly established in recent years. The automotive industry has widely adopted and successfully deployed these methods in many different series production programs worldwide. This brought various benefits, such as a reduction in development times, improved quality due to more precise specifications, and early verification and validation by means of simulation. At the same time, more and more safety-related and safety-critical systems have been - and will be -introduced into modern vehicles. Common examples are active front steering, adaptive cruise-control, and integrated chassis control. This leads to the question, if and how model-based design and automatic production code generation can be applied to the development of safety-critical systems.

Today, hardware-in-the-loop (HIL) simulation is common practice as a testing methodology for electronic control units (ECUs). An essential criterion for the efficiency of an HIL system is the availability of powerful test automation having access to all of its hardware and software components (including I/O channels, failure insertion units, bus communication controllers and diagnostic interfaces). The growing complexity of vehicle embedded systems, which are interconnected by bus systems (like CAN, LIN or FlexRay), result in hundreds or even thousands of tests that have to be done to ensure the correct system functionality. This is best achieved by automated testing. Automated testing usually is performed by executing tests on a standard PC, which is interconnected to the HIL system. However, higher demands regarding timing precision are hard to accomplish. As an example, ECU interaction has to be captured and responded to in the range of milliseconds.

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.

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 application of a communication infrastructure for hybrid test systems is currently a topic in the aerospace industry, as also in other industries. One main reason is flexibility. Future laboratory tests means (LTMs) need to be easier to exchange and reuse than they are today. They may originate from different suppliers and parts of them may need to fulfill special requirements and thus be based on dedicated technologies. The desired exchangeability needs to be achieved although suppliers employ different technologies with regard to specific needs. To achieve interoperability, a standardized transport mechanism between test systems is required. Designing such a mechanism poses a challenge as there are several different types of data that have to be exchanged. Simulation data is a prominent example. It has to be handled differently than control data, for example. No one technique or technology fits perfectly for all types of data.

Hardware-in-the-loop (HIL) testing is indispensable in the software development process for control units and has been an integral part of the software development process for years. Large HIL systems for integration tests are used to test the correct behavior of distributed functions and the communication between the control units. The vast development programs that are involved require building duplicates of such test systems or parts of them, due to the fact that the tasks are distributed between different companies or different departments within a company. However, there is an alternative to duplicating a test system. Instead of using a cloned system, coupling HIL systems over large distances is an alternate approach. This paper presents what requirements this coupling must fulfill and and describes a path-breaking method to fulfill them. In addition, results of an implementation are shown.

Hardware-in-the-Loop Simulation is a technology where the actual vehicles, engines or other components are replaced by a real-time simulation in a simulation computer, based on a mathematical model. That simulation reads ECU (Electronic Control Unit) output signals which would normally go to actuators. On the other hand the simulation must output the sensor signals which make the ECU ‘think’ it controls a real system. Generating these signals can be very difficult. Signals may be complex, depend on on-line computed variables, and be required to be output at high timing resolution. This paper describes the problems and presents a solution which employs high-performance Digital Signal Processors (DSP) to generate such signals on-line by Direct-Digital-Synthesis (DDS).

Embedded software in the car is becoming increasingly complex due to the growing number of software-based controller functions and the increasing complexity of the software itself. Model-based development with Simulink combined with TargetLink for automatic code generation helps significantly to improve the quality of the embedded software. The development of large-scale Simulink models in distributed teams is a challenging task, especially when developing safety-critical software that must fulfill requirements stated in the ISO 26262 [1] safety standard. In practice, many questions on how to avoid the pitfalls of distributed model-based development remain open, such as how to define an appropriate model architecture, handle model complexity, and achieve compliance with ISO 26262. The intent of this paper is threefold. Firstly, we summarize those requirements of ISO 26262 that are relevant for developing complex software in a distributed environment.

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 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.

The complexity of automobile powertrains grows continuously. At the same time, development time and budget are limited. Shifting development tasks to earlier phases (frontloading) increases the efficiency by utilizing test benches instead of prototype vehicles (road-to-rig approach). Early system verification of powertrain components requires a closed-loop coupling to real-time simulation models, comparable to hardware-in-the-loop testing (HiL). The international research project Advanced Co-Simulation Open System Architecture (ACOSAR) has the goal to develop a non-proprietary communication architecture between real-time and non-real-time systems in order to speed up the commissioning process and to decrease the monetary effort for testing and validation. One major outcome will be a generic interface for coupling different simulation tools and real-time systems (e.g. HiL simulators or test benches).

Hardware-in-the-loop (HIL) testing is an indispensable tool in the software development process for electronic control units (ECUs) and Logical Replaceable Units (LRUs) and is an integral part of the software validation process for many organizations. HIL simulation is regarded as the tried-and-tested method for function, component, integration and network tests for the entire system. Using the Model based design approach has further enabled improved and faster HIL implementations in recent years. This paper describes the changing requirements for HIL simulation, and how they need to be addressed by HIL technology. It also addresses the challenges faced while setting up a successful HIL system: namely the division of tasks, the total cost of ownership, budget constraints and tough competition and the adaptability of a HIL simulator to new demands. These requirements are discussed using a dSPACE HIL system architecture that was designed from the ground-up to address these needs.

Real-time simulation of truck and trailer combinations can be applied to hardware-in-the-loop (HIL) systems for developing and testing electronic control units (ECUs). The large number of configuration variations in vehicle and axle types requires the simulation model to be adjustable in a wide range. This paper presents a modular multibody approach for the vehicle dynamics simulation of single track configurations and truck-and-trailer combinations. The equations of motion are expressed by a new formula which is a combination of Jourdain's principle and the articulated body algorithm. With the proposed algorithm, a robust model is achieved that is numerically stable even at handling limits. Moreover, the presented approach is suitable for modular modeling and has been successfully implemented as a basis for various system definitions. As a result, only one simulation model is needed for a large variety of track and trailer types.

Real-time simulation of truck and trailer combinations can be applied to hardware-in-the-loop (HIL) systems for developing and testing electronic control units (ECUs). The large number of configuration variations in vehicle and axle types requires the simulation model to be adjustable in a wide range. This paper presents a modular multibody approach for the vehicle dynamics simulation of single track configurations and truck-and-trailer combinations. The equations of motion are expressed by a new formula which is a combination of Jourdain's principle and the articulated body algorithm. With the proposed algorithm, a robust model is achieved that is numerically stable even at handling limits. Moreover, the presented approach is suitable for modular modeling and has been successfully implemented as a basis for various system definitions. As a result, only one simulation model is needed for a large variety of track and trailer types.

Multi-physics interactions between structural, electrical, thermal, or hydraulic components and the high level of system integration, characteristic of new aircraft designs, is increasing the complexity of both design and verification processes. Therefore the availability of tools, supporting integrated modelling, simulation, optimization and testing across all stages of aircraft design remains a critical challenge. This paper presents some results of the project MISSION (Modelling and Simulation Tools for Systems Integration on Aircraft). It is a collaborative task being developed under the European Union Clean Sky 2 Program, which is a public-private partnership bringing together aeronautics industrial leaders and public research organizations based in Europe. The first levels of integration of different models and tools proposed in the MISSION framework will be presented, along with simulation results.