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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.

A Hardware-in-the-Loop (HiL) system for Electronic Control Units (ECU) of electro-pneumatic brake systems is presented. The HiL system runs a real-time capable vehicle model comprising of both the vehicle dynamics and the electro-pneumatic brake system. The dynamic behaviour of the vehicle can be simulated either by a real-time multi-body vehicle model or by a simpler system dynamic (double-track) model. To assess the quality of the system dynamic vehicle model, it is compared to a multi-body vehicle model which was validated with comprehensive experimental results. Discrepancies can be seen for highly unsteady manoeuvres. Reasons for these discrepancies caused by the modelling topology of the system dynamic vehicle model are given. In order to simulate the electro-pneumatic brake system, a real-time model has been developed and validated. The different topologies of brake systems can be assembled from components and integrated into the vehicle model.

This paper focuses on the manufacturer's conflict in the conceptual design of commercial vehicles between highly customized special vehicles and the greatest possible degree of standardization. Modularity and standardization are crucial success factors for realizing high variance at the best cost efficiency in development and production as well for achieving the highest quality standards at reduced efforts for technical validation. The presented virtual design approach for commercial vehicle concepts allows for purposeful design and integration of new concepts and technologies on the component level in an existing product portfolio - not neglecting manufacture's portfolio requirements concerning standardization and modularity. The integrated tool chain helps to bring trade-offs to a head that exist in balancing between dedicated vehicles with best customer-relevant characteristics and standardized vehicles with the highest degree of commonality.

With the introduction of advanced digital road maps, which include information on the slope and curve radius of the highways, predictive control for standard and hybrid commercial vehicles, based on these maps, is about to be released by the vehicle manufacturers. For example, intelligent predictive cruise control has been announced for introduction in 2012 by Scania and Daimler. In addition, hybrid commercial city buses like MAN's Lion's City Hybrid have already been implemented. But the question remains about the type of vehicle suitable for the implementation of predictive intelligent concepts, due to the high investment cost compared to the sometimes relatively low operating cost savings.

Current commercial vehicles' engines are complex systems with multiple degrees of freedom. In conjunction with current emissions regulations manufacturers are forced to combine highly developed engines with complex aftertreatment systems. A comprehensive simulation model including the engine and aftertreatment system has been set up in order to study and optimize the overall system. The model uses a phenomenological spray combustion model to predict fuel consumption and NO emissions. In addition physical models for the material temperatures and the reaction kinetics were generated for the aftertreatment system. Steady state and transient measurements were used to calibrate the engine as well as the aftertreatment model. The aim for a system-level optimization was a reduction of fuel consumption while meeting emission standards.