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The Vehicle Noise Control Engineering Academy covers a variety of vehicle noise control engineering principles and practices. There are two concurrent, specialty tracks (with some common sessions): Vehicle Interior Noise and Powertrain Noise. Participants should choose and register for the appropriate track they wish to attend. The Vehicle Interior Noise track focuses on understanding the characteristics of noise produced by different propulsion systems, including internal combustion, hybrid and electric powered vehicles and how these noises affect the sound quality of a vehicle’s interior.  

The Vehicle Noise Control Engineering Academy covers a variety of vehicle noise control engineering principles and practices. There are two concurrent, specialty tracks (with some common sessions): Powertrain Noise and Vehicle Interior Noise. Participants should choose and register for the appropriate Academy they wish to attend. The Powertrain Noise track focuses on noise and vibration control issues associated with internal combustion, hybrid and electric powered vehicles. The vehicle in this case includes passenger cars, SUVs, light trucks, off-highway vehicles, and heavy trucks.

This course is verified by Probitas Authentication as meeting the AS9104/3A requirements for continuing Professional Development. AS13100 and RM13000 define the Problem-Solving standard for suppliers within the aero-engine sector, with the Eight Disciplines (8D) problem solving method the basis for this standard. This two-day course provides participants with a comprehensive and standardized set of tools to become an 8D practitioner. Successful application of 8D achieves robust corrective and preventive actions to reduce the risk of repeat occurrences and minimize the cost of poor quality.

This course is verified by Probitas as meeting the AS9104/3A requirements for Continuing Professional Development. Production and continual improvement of safe and reliable products is key in the aviation, space, and defense industries. Customer and regulatory requirements must not only be met, but they are typically expected to exceeded requirements. Due to globalization, the supply chain of this industry has been expanded to countries which were not part of it in the past and has complicated the achievement of requirements compliance and customer satisfaction.

This course is verified by Probitas Authentication as meeting the AS9104/3A requirements for continuing Professional Development. In the Aerospace Industry there is a focus on Defect Prevention to ensure that quality goals are met. Failure Mode and Effects Analysis (PFMEA) and Control Plan activities are recognized as being one of the most effective, on the journey to Zero Defects. This two-day course is designed to explain the core tools of Design Failure Mode and Effects Analysis (DFMEA), Process Flow Diagrams, Process Failure Mode and Effects Analysis (PFMEA) and Control Plans as described in AS13100 and RM13004.

The global time that is propagated and synchronized in the vehicle E/E architecture is used in safety-critical, security-critical, and time-critical applications (e.g., driver assistance functions, intrusion detection system, vehicle diagnostics, external device authentication during vehicle diagnostics, vehicle-to-grid and so on). The cybersecurity attacks targeting the global time result in false time, accuracy degradation, and denial of service as stated in IETF RFC 7384. These failures reduce the vehicle availability, robustness, and safety of the road user. IEEE 1588 lists four mechanisms (integrated security mechanism, external security mechanism, architectural solution, and monitoring & management) to secure the global time. AUTOSAR defines the architecture and detailed specifications for the integrated security mechanism "Secured Global Time Synchronization (SGTS)" to secure the global time on automotive networks (CAN, FlexRay, Ethernet).

On the path to decarbonizing road transport, electric commercial vehicles will play a significant role. The first applications were directed to the smaller trucks for distribution traffic with relatively moderate driving and range requirements, but meanwhile, the first generation of a complete portfolio of truck sizes is developed and available on the market. In these early applications, many compromises were accepted to overcome component availability, but meanwhile, the supply chain can address the specific needs of electric trucks. With that, the optimization towards higher usability and lower costs can be moved to the next level. Especially for long-haul trucks, efficiency is a driving factor for the total costs of ownership. Besides the propulsion system, all other systems must be optimized for higher efficiency. This includes thermal management since the thermal management components consume energy and have a direct impact on the driving range.

The optimization and further development of automated driving functions offers great potential to relieve the driver in various driving situations and increase road safety. Simulative testing in particular is an indispensable tool in this process, allowing conclusions to be drawn about the design of automated driving functions at a very early stage of development. In this context, the use of driving simulators provides support so that the driving functions of tomorrow can be experienced in a very safe and reproducible environment. The focus of the acceptance and optimization of automated driving functions is particularly on vehicle lateral control functions. As part of this paper, a test person study was carried out regarding manual vehicle lateral control on the dynamic vehicle road simulator at the Institute of Automotive Engineering.

In electrified vehicles, auxiliary units can be a dominant source of noise, one of which is the refrigerant scroll compressor. Compared to vehicles with combustion engines, e-vehicles require larger refrigerant compressors, as in addition to the interior, also the battery and the electric motors have to be cooled. Currently, scroll compressors are widely used in the automotive industry, which generate one pressure pulse per revolution due to their discontinuous compression principle. This results in speed-dependent pressure fluctuations as well as higher-harmonic pulsations that arise from reflections. These fluctuations spread through the refrigeration cycle and cause the vibration excitation of refrigerant lines and heat exchangers. The sound transmission path in the air conditioning heat exchanger integrated in the dashboard is particularly critical. Various silencer configurations can be used to dampen these pulsations.

Increasing urbanisation and the growing environmental awareness in society require new and innovative vehicle concepts. In the present work, the design freedoms of additive manufacturing (AM) are used to develop a front axle wheel suspension for a novel modular vehicle concept. The development of the suspension components is based on a new method using industry standard load cases for the strength design of the components. To design the chassis components, first the available installation space is determined and a suitable configuration of the chassis components is defined. Furthermore, numerical methods are used to identify component geometries that are suitable for the force flow. The optimisation setup is selected in a way that allows to integrate information, energy and material-carrying conductors into the suspension arms. The conductors even serve as load-bearing structures because of the matching design of the components.

Homologation is an important process in vehicle development and aerodynamics a main data contributor. The process is heavily interconnected: Production planning defines the available assemblies. Construction defines their parts and features. Sales defines the assemblies offered in different markets, where Legislation defines the rules applicable to homologation. Control engineers define the behavior of active, aerodynamically relevant components. Wind tunnels are the main test tool for the homologation, accompanied by surface-area measurement systems. Mechanics support these test operations. The prototype management provides test vehicles, while parts come from various production and prototyping sources and are stored and commissioned by logistics. Several phases of this complex process share the same context: Production timelines for assemblies and parts for each chassis-engine package define which drag coefficients or drag coefficient contributions shall be determined.

Investigating human driver behavior enhances the acceptance of the autonomous driving and increases road safety in heterogeneous environments with human-operated and autonomous vehicles. The previously established driver fingerprint model, focuses on the classification of driving style based on CAN bus signals. However, driving styles are inherently complex and influenced by multiple factors, including changing driving environments and driver states. To comprehensively create a driver profile, an in-car measurement system based on the Driver-Driven vehicle-Driving environment (3D) framework is developed. The measurement system records emotional and physiological signals from the driver, including ECG signal and heart rate. A Raspberry Pi camera is utilized on the dashboard to capture the driver's facial expressions and a trained convolutional neural network (CNN) recognizes emotion. To conduct unobtrusive ECG measurements, an ECG sensor is integrated into the steering wheel.

As part of the safety validation of advanced driver assistance systems (ADAS) and automated driving (AD) functions, it is necessary to demonstrate that the frequency at which the system exhibits hazardous behavior (HB) in the field is below an acceptable threshold. This is typically tested by observation of the system behavior in a field operational test (FOT). For situations in which the system under test (SUT) actively intervenes in the dynamic driving behavior of the vehicle, it is assessed whether the SUT exhibits HB. Since the accepted threshold values are generally small, the amount of data required for this strategy is usually very large. This publication proposes an approach to reduce the amount of data required for the evaluation of emergency intervention systems with a state machine based intervention logic by including the time periods between intervention events in the validation process.