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Squeak and rattle (S&R) are nonstationary annoying and unwanted noises in the car cabin that result in considerable warranty costs for car manufacturers. Introduction of cars with remarkably lower background noises and the recent emphasis on electrification and autonomous driving further stress the need for producing squeak- and rattle-free cars. Automotive manufacturers use several road disturbances for physical evaluation and verification of S&R. The excitation signals collected from these road profiles are also employed in subsystem shaker rigs and virtual simulations that are gradually replacing physical complete vehicle test and verification. Considering the need for a shorter lead time and the introduction of optimisation loops, it is necessary to have efficient and inclusive excitation load cases for robust S&R evaluation.

The development of a new combustion system for a light-duty diesel engine is presented. The soot-NOx trade-off is significantly improved with maintained or improved efficiency. This is accomplished only by altering the combustion chamber geometry, and thereby the in-cylinder flow. The bowl geometry is developed in CFD and validated in single cylinder tests. Tests and simulations align remarkably well. Under identical conditions in the engine the new combustion chamber decreases smoke by 11-27%, NOx by 2-11%, and maintains efficiency as compared to the baseline geometry. The injector nozzle is matched to the new bowl using design of experiments (DoE). By this method transfer functions are obtained that can be used to optimize the system using analytical tools. The emissions show a complex dependence on the nozzle geometry. The emission dependence on nozzle geometry varies greatly over the engine operating range.

This paper describes the development of a robust and accurate method to model one-phase heat exchangers in complete vehicle air flow simulations along with a comprehensive comparison of EFD and CFD results. The comparison shows that the inlet radiator coolant temperatures obtained with CFD were within ±4°C of the experimental data with a trend in the differences being dependent on the car speed. The relative differences in cooling air mass flow rates increase with increasing car speed, with CFD values generally higher than EFD. From the investigation, the conclusion is that the methodology and modeling technique presented offer an accurate tool for concept and system solutions on the front end design, cooling package and fan. Care must be taken in order to provide the best possible boundary conditions paying particular attention to the heat losses in the engine, performance data for the radiator and fan characteristics.

Structural topology optimization is a truly interesting and important area, which has developed very rapidly and matured considerably in many fields. However, the use of topology optimization for global structures, using detailed design, is still tremendously time-consuming. From this perspective, the author sees the development of methods and tools to include optimization on simplified models during the design process as the most interesting and important step towards implementing structure topology optimization in the vehicle industry. In the design process, structures are broken down into beams and joints, and are described using a PBM (Property Based Model). Beams are described using a rectangular cross-section with the possibility of being changed in size, shape and orientation. Joints are described as flexible elements using a set of sub-elements called 2-joints that makes it possible for the joint model to change topology and stiffness.

A two-state forward dynamic programming algorithm is evaluated in a series hybrid drive-train application with the objective to minimize fuel consumption when look-ahead information is available. The states in the new method are battery state-of-charge and engine speed. The new method is compared to one-state dynamic programming optimization methods where the requested generator power is found such that the fuel consumption is minimized and engine speed is given by the optimum power-speed efficiency line. The other method compared is to run the engine at a given operating point where the system efficiency is highest, finding the combination of engine run requests over the drive-cycle that minimizes the fuel consumption. The work has included the engine torque and generator power as control signals and is evaluated in a full vehicle-simulation model based on the Volvo Car Corporation VSIM tool.

Flow visualization techniques are widely used in aerodynamics to investigate the surface trace pattern. In this experimental investigation, the surface flow pattern over the rear end of a full-scale passenger car is studied using tufts. The movement of the tufts is recorded with a DSLR still camera, which continuously takes pictures. A novel and efficient tuft image processing algorithm has been developed to extract the tuft orientations in each image. This allows the extraction of the mean tuft angle and other such statistics. From the extracted tuft angles, streamline plots are created to identify points of interest, such as saddle points as well as separation and reattachment lines. Furthermore, the information about the tuft orientation in each time step allows studying steady and unsteady flow phenomena. Hence, the tuft image processing algorithm provides more detailed information about the surface flow than the traditional tuft method.

In modern vehicles, each system must meet tough demands to fulfill the many different attribute requirements, design constraints and manufacturing limitations. It becomes difficult and time-consuming to find an optimal and robust design using a traditional engineering process. Volvo Cars has for several years been using Multi-Disciplinary Optimization, MDO, that basically shows the customer attributes levels, such as NVH, ride comfort, and driveability as a function of different parameter configurations. This greatly facilitates project team understanding of the limitations and possibilities of the different systems, and has become a key enabler to achieving a good balance between different attributes. Traditionally, this type of comprehensive Design of Experiments (DOE) optimization demands huge time and computer resources. Frequently, experimental designs will not fulfill manufacturing limitations or attribute targets, making this decision process slow, tedious, and fruitless.

Bushing elasticity is one of the most important compliance factors that significantly influence driving behavior. The deformations of the bushings change the wheel orientations under external forces. Another important factor of bushing compliance is to provide a comfortable driving experience by isolating the vibrations from road irregularities. However, the driving comfort and driving dynamics are often in conflict and need to be balanced in terms of bushing compliance design. Specifically, lateral force steer and brake force steer are closely related to safety and stability and comprises must be minimized. The sensitivity analysis helps engineers to understand the critical bushing for certain compliance attributes, but optimal balancing is complicated to understand. The combination of individual bushing stiffness must be carefully set to achieve an acceptable level of all the attributes.