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Current developments in automotive industry such as hybrid powertrains and the continuously increasing demands on emission control systems, are pushing complexity still further. Validation of such systems lead to a huge amount of test cases and hence extreme testing efforts on the road. At the same time the pressure to reduce costs and minimize development time is creating challenging boundaries on development teams. Therefore, it is of utmost importance to utilize testing and validation prototypes in the most efficient way. It is necessary to apply high levels of instrumentation and collect as much data as possible. And a streamlined data pipeline allows the fleet managers to get new insights from the raw data and control the validation vehicles as well as the development team in the most efficient way. In this paper we will demonstrate a data-driven approach for validation testing.

Beside the automotive industry, where 2-cylinder inline engines are catching attention again, twin-cylinder configurations are quite usual in the small engine world. From stationary engines and range-extender use to small motorcycles up to big cruisers and K-Cars this engine architecture is used in many types of applications. Because of very good overall packaging, performance characteristics and not least the possibility of parts-commonality with 4-cylinder engines nearly every motorcycle manufacturer provides an inline twin in its model range. Especially for motorcycle applications where generally the engine is a rigid member of the frame and vibrations can be transferred directly to the rider an appropriate balancing system is required.

The acoustics of automotive intake and exhaust systems is typically modeled using linear acoustics or gas-dynamics simulation. These approaches are preferred during basic sound design in the early development stages due to their computational efficiency compared to complex 3D CFD and FEM solutions. The linear acoustic method reduces the component being modelled to an equivalent acoustic two-port transfer matrix which describes the acoustic characteristic of the muffler. Recently this method was used to create more detailed and more accurate models based on a network of 3D cells. As the typical automotive muffler includes perforated elements and sound absorptive material, this paper demonstrates the extension of the 3D linear acoustic network description of a muffler to include the aforementioned elements. The proposed method was then validated against experimental results from muffler systems with perforated elements and sound absorptive material.

Car manufacturers put large efforts into reducing wind noise to improve the comfort level of their cars. Each component of the vehicle is designed to meet its individual noise target to ensure the wind noise passenger comfort level inside the vehicle is met. Sunroof designs are tested to meet low-frequency buffeting (also known as boom) targets and broadband noise targets for the fully open sunroof with deflector and for the sunroof in vent position. Experimentally testing designs and making changes to meet these design targets typically involves high cost prototypes, expensive wind tunnel sessions, and potentially late design changes. To reduce the associated costs as well as development times, there is strong motivation for the use of a reliable numerical prediction capability early in the vehicle design process.

For most car manufacturers, wind noise from the greenhouse region has become the dominant high frequency noise contributor at highway speeds. Addressing this wind noise issue using experimental procedures involves high cost prototypes, expensive wind tunnel sessions, and potentially late design changes. To reduce the associated costs as well as development times, there is strong motivation for the use of a reliable numerical prediction capability early in the vehicle design process. Previously, a computational approach that couples an unsteady computational fluid dynamics solver (based on a Lattice Boltzmann method) to a Statistical Energy Analysis (SEA) solver had been validated for predicting the noise contribution from the side mirrors. This paper presents the use of this computational approach to predict the vehicle interior noise from the windshield wipers, so that different wiper placement options can be evaluated early in the design process before the surface is frozen.

The ability to assess noise transmitted through seals to cabin interiors early in the design process is very important for automotive manufacturers. When a seal design is inadequate, the noise transmitted can dominate the interior noise, making the wind noise performance of the vehicle unacceptable. This can cause launch delays, increasing costs and risking loss of sales. Designing seals using conventional experimental processes is challenging, since the location and strength of flow noise sources are not known when the seal design is planned. Making changes to the seal system after the tooling stage is expensive for manufacturers as tooling and redesign costs can be considerable. Deliberate overdesign by adding multiple layers of seals in a wide range of locations also can reduce profit by unnecessarily raising part and manufacturing costs.

During the last ten years, there is a significant tendency in automotive design to use lower aspect ratio tires and meanwhile also more and more run-flat tires. In appropriate publications, the influences of these tire types on the dynamic loads - transferred from the road passing wheel center into the car - have been investigated pretty well, including comparative wheel force transducer measurements as well as simulation results. It could be shown that the fatigue input into the vehicle tends to increase when using low aspect ratio tires and particularly when using run-flat tires. But which influences do we get for the loading and fatigue behavior of the respective rims? While the influences on the vehicle are relatively easy to detect by using wheel force transducers, the local forces acting on the rim flange (when for example passing a high obstacle) are much more difficult to detect (in measurement as well as in simulation).

Complexity of electronics and embedded software systems in automobiles has been increasing over the years. This necessitates the need for an effective and exhaustive development and validation process in order to deliver fault free vehicles at reduced time to market. Model-based Product Engineering (MBPE) is a new process for development and validation of embedded control software. The process is generic and defines the engineering activities to plan and assess the progress and quality of the software developed for automotive applications. The MBPE process is comprised of six levels (one design level and five verification and validation levels) ranging from the vehicle requirements phase to the start of production. The process describes the work products to be delivered during the course of product development and also aligns the delivery plan to overall vehicle development milestones.

For noise quality and brand sound design of passenger cars a unique software tool is currently used by our clients world-wide to evaluate and optimise the interior noise quality and brand sound aspects of passenger cars on an objective basis. The software tools AVL-VOICE and AVL-COMFORT are designed for the objective analysis of interior noise quality, for benchmarking, for the definition of noise quality targets and most important for effective vehicle sound engineering. With this tool, the target orientated implementation of the required interior noise quality or brand sound by predictable hardware modifications into passenger cars - for tailor made joy of driving - becomes feasible. The use of this tools is drastically reducing vehicle evaluation time and sound engineering effort when compared with traditional jury subjective evaluation methods and standard acoustic NVH optimisation procedures.

In the eighties, the main concern in the automotive industry from a designer's standpoint was a level issue. In the nineties, the market has put more stringent requirements on the automotive industry with respect to noise in general and psychoacoustics. The governments have imposed lower limits with respect to pass-by noise standards. Customers are spending more time in their car than in the past and are demanding acoustical comfort. All of this is leading to an environment where a sound quality system is becoming a daily tool in the design and trouble-shooting world. This paper describes what should be looked for in a sound, how to quantify these properties and what tools are needed. These steps are then applied in a case study.

Increasing demands for safety, security, and certifiability of embedded automotive systems require additional development effort to generate the required evidences that the developed system can be trusted for the application and environment it is intended for. Safety standards such as ISO 26262 for road vehicles have been established to provide guidance during the development of safety-critical systems. The challenge in this context is to provide evidence of consistency, correctness, and completeness of system specifications over different work-products. One of these required work-products is the hardware-software interface (HSI) definition. This work-product is especially important since it defines the interfaces between different technologies. Model-based development (MBD) is a promising approach to support the description of the system under development in a more structured way, thus improving resulting consistency.

At first glance RDE seems to be a road testing topic only, mistakenly. While Type approval test must be performed at the Road and Chassis Dyno, development work beforehand delivers solutions fulfilling the demanded legislation limits. Making the right development steps and decisions will lead to a technical solution within economy of scales. Much of this work done happens on engine testbeds and Real Driving Emissions (RDE) per UN-ECE legislation or the new test cycle for the chassis dyno according WLTP (Worldwide Harmonized Light-Duty Test Procedure) will not change that. The question is, are engine test beds fit for this new challenge or are changes required? One characteristic element of RDE is the randomness of operating conditions generated by a road drive. There must be found a way, to achieve RDE relevant test conditions in a most reproducible manner.

The advent of 3D displays offers Human-Machine Interface (HMI) designers and engineers new opportunities to shape the user's experience of information within the vehicle. However, the application of 3D displays to the in-vehicle environment introduces a number of new parameters that must be carefully considered in order to optimise the user experience. In addition, there is potential for 3D displays to increase driver inattention, either through diverting the driver's attention away from the road or by increasing the time taken to assimilate information. Manufacturers must therefore take great care in establishing the ‘do’s and ‘don’t's of 3D interface design for the automotive context, providing a sound basis upon which HMI designers can innovate. This paper describes the approach and findings of a three-part investigation into the use of 3D displays in the instrument cluster of a road car, the overall aim of which was to define the boundaries of the 3D HMI design space.

This paper presents a system-level thermal model of a fluid-cooled Li-Ion battery module. The model is a reduced order model (ROM) identified by results from finite element analysis (FEA)/computational fluid dynamic (CFD) coupling simulation using the linear and time-invariant (LTI) method. The ROM consists of two LTI sub-systems: one of which describes the battery temperature response to a transient battery current, and the other of which takes into account of the battery temperature variation due to a heat flux induced by a varied inlet temperature of the battery cooling circuit. The thermal LTI model can be coupled to an electrical model to build a complete system-level battery ROM. Test examples show that the ROM is able to provide as accurate results as those from FEA/CFD coupling simulations.

Many electric (EV) and hybrid-electric (HEV) vehicles are designed to operate using only electric propulsion at low road speeds. This has resulted in significantly reduced vehicle noise levels in urban situations. Although this may be viewed by many as a benefit, a risk to safety exists for those who rely on the engine noise to help detect the presence, location and behaviour of a vehicle in their vicinity. In recognition of this, legislation is being introduced globally which will require automotive manufacturers to implement external warning sound systems. A key challenge for premium vehicle manufacturers is the development of a suitable warning sound signature which also conveys the appropriate brand aspirations for the product. A further major difficulty exists when trying to robustly evaluate potential exterior sounds by running large-scale trials in the real world.

The automotive domain has seen safety engineering at the forefront of the industry’s priorities for the last decade. Therefore, additional safety engineering efforts, design approaches, and well-established safety processes have been stipulated. Today many connected and automated vehicles are available and connectivity features and information sharing are increasingly used. This increases the attractiveness of an attack on vehicles and thus introduces new risks for vehicle cybersecurity. Thus, just as safety became a critical part of the development in the late 20th century, the automotive domain must now consider cybersecurity as an integral part of the development of modern vehicles. Aware of this fact, the automotive industry has, therefore, recently taken multiple efforts in designing and producing safe and secure connected and automated vehicles.

The development of new design tools to predict the vibro-acoustic behavior within the vehicle development process is of essential importance to achieve better products in an ever shorter timeframe. In this paper, an energy flow post-processing tool for structural dynamic analysis is presented. The method is based on the conversion of conventional finite element (FE) results into energy quantities corresponding with each of the vehicle subcomponents. Based on the global dynamic system behavior and local subcomponent descriptions, one can efficiently evaluate the energy distribution and analyze the vibro-acoustic behavior in complex structures. By using energy as a response variable, instead of conventional design variables as pressure or velocity, one can obtain important information regarding the understanding of the vibro-acoustic behavior of the system.

For the automotive industry, the quality and level of the wind noise contribution has a growing importance and therefore should be addressed as early as possible in the development process. Each component of the vehicle is designed to meet its individual noise target to ensure the wind noise passenger comfort level inside the vehicle is met. Sunroof broadband noise is generated by the turbulent flow developed over the roof opening. A strong shear layer and vortices impacting on the trailing edge of the sunroof are typical mechanisms related to the noise production. Sunroof designs are tested to meet broadband noise targets. Experimentally testing designs and making changes to meet these design targets typically involves high cost prototypes, expensive wind tunnel sessions and potentially late design changes.

Future demands regarding emissions, fuel consumption and driveability lead to complex engine and power train control systems. The calibration of the increasing number of free parameters in the ECU's contradicts the demand for reduced time in the power train development cycle. This paper will focus on the automatic, unmanned closed loop optimization of driveability quality on a high dynamic engine test bed. The collaboration of three advanced methods will be presented: Objective real time driveability assessment, to predict the expected feelings of the buyers of the car Automatic computer assisted variation of ECU parameters on the basis of statistical methods like design of experiments (DoE). Thus data are measured in an automated process allowing an optimization based on models (e.g. neural networks).

Vehicle development involves the design and integration of subsystems of different domains to meet performance, efficiency, and emissions targets set during the initial developmental stages. Before a physical prototype of a vehicle or vehicle powertrain is tested, engineers build and test virtual prototypes of the design(s) on multiple stages throughout the development cycle. In addition, controllers and physical prototypes of subsystems are tested under simulated signals before a physical prototype of the vehicle is available. Different departments within an automotive company tend to use different modelling and simulation tools specific to the needs of their specific engineering discipline. While this makes sense considering the development of the said system, subsystem, or component, modern holistic vehicle engineering requires the constituent parts to operate in synergy with one-another in order to ensure vehicle-level optimal performance.