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Most OEMs are shifting their strategy and way of thinking regarding ECUs. This, in combination with the electrification of vehicles and the shift towards software-based companies (car as a device), implies one of the biggest paradigm changes in automotive history. On the other hand, despite the current struggles, remarkable advances have been made in electronic technology during the past few years. These developments have opened a door to very promising enabling technology, with exterior lighting as a main target market. These circumstances seem to have created a perfect storm leading to new strategies for electronic control and driving for (front and rear) exterior lighting. We, at our company, have investigated the enabling technology, challenges, and benefits of this emerging exterior lighting approach, that we call ‘ECU-Less’.

In order to predict reality as accurately as possible leads to the fact that numerical models in automotive vibroacoustic problems become increasingly high dimensional. This makes applications with a large number of model evaluations, e.g. optimization tasks or uncertainty quantification hard to solve, as they become computationally very expensive. Engineers are thus faced with the challenge of making decisions based on a limited number of model evaluations, which increases the need for data-efficient methods and reduced order models. In this contribution, variational autoencoders (VAEs) are used to reduce the dimensionality of the vibroacoustic model of a vehicle body and to find a low-dimensional latent representation of the system.

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 aim of the presented research is to propose and benchmark two brake models, namely the novel dynamic ILVO (Ilmenau-Volvo) model and a neural-network based regression. These can estimate the evolution of the brake friction between pad and disc under different load conditions, which are typically experienced in vehicle applications. The research also aims improving the knowledge of the underlying mechanism related to the evolution of the BLFC (boundary layer friction coefficient), the reliability of virtual environment simulations to speed up the product development time and reducing the amount of vehicle test in later phases and finally improving brake control functions. With the support of extensive brake dynamometer testing, the proposed models are benchmarked against State-of-the-Art. Both approaches are parametrized to render the friction coefficient dynamics with respect to the same input parameters.

The introduction of new light-duty vehicle emission limits to comply under real driving conditions (RDE) is pushing the diesel engine manufacturers to identify and improve the technologies and strategies for further emission reduction. The latest technology advancements on the after-treatment systems have permitted to achieve very low emission conformity factors over the RDE, and therefore, the biggest challenge of the diesel engine development is maintaining its competitiveness in the trade-off “CO2-system cost” in comparison to other propulsion systems. In this regard, diesel engines can continue to play an important role, in the short-medium term, to enable cost-effective compliance of CO2-fleet emission targets, either in conventional or hybrid propulsion systems configuration. This is especially true for large-size cars, SUVs and light commercial vehicles.

Since several decades, passenger cars and light duty vehicles (LDV) with spark-ignited engines reach full pollutant conversion during warm up conditions; the major challenge has been represented by the cold start and warming up strategies. The focus on technology developments of exhaust after treatment systems have been done in the thermal management in order to reach the warm up conditions as soon as possible. A new challenge is now represented by the Real Driving Emission (RDE) Regulation as this bring more various, and not any longer cycle defined, cold start conditions. On the other hand, once the full conversion has been reached, it would be beneficial for many Exhaust After Treatment System (EATS) components, e.g. for overall durability if the exhaust gas temperature could be lowered. To take significant further emission steps, approaching e.g. zero emission concepts, we investigate the use of Electrical Heating Catalyst (EHC) also including pre-heating.

In times when automated driving is becoming increasingly relevant, dynamic simulators present an appropriate simulation environment to faithfully reproduce driving scenarios. A realistic replication of driving dynamics is an important criterion to immerse persons in the virtual environments provided by the simulator. Motion Cueing Algorithms (MCAs) compute the simulator’s control input, based on the motions of the simulated vehicle. The technical restrictions of the simulator’s actuators form the main limitation in the execution of these input commands. Typical dynamic simulators consist of a hexapod with six degrees of freedom (DoF) to reproduce the vehicle motion in all dimensions. Since its workspace dimensions are limited, significant improvements in motion capabilities can be achieved by expanding the simulator with redundant DoF by means of additional actuators.

The Deep Orange program immerses automotive engineering students into the world of an OEM as part of their 2-year graduate education. In support of developing the program’s seventh vehicle concept, the students studied the sponsoring brand essence, conducted market research, and made a heuristic assessment of competitor vehicles. The upfront research lead to the definition of target customers and setting vehicle level targets that were broken down into requirements to develop various vehicle sub-systems. The powertrain team was challenged to develop a scalable propulsion concept enabled by a common vehicle architecture that allowed future customers to select (at the point of purchase) among various levels of electrification best suiting their needs and personal desires. Four different configurations were identified and developed: all-electric, two plug-in hybrid electric configurations, and an internal combustion engine only.

Existing battery parameter model structures are evaluated by estimating model parameters on real driving data applying standard system identification methods. Models are then evaluated on the test data in terms of goodness of fit and RMSE in voltage predictions. This is different from previous battery model evaluations where a common approach is to train parameters using standardized tests, e.g. hybrid pulse-power capability (HPPC), with predetermined charge and discharge sequences. Equivalent linear circuit models of different complexity were tested and evaluated in order to identify parameter dependencies at different state of charge levels and temperatures. Models are then used to create voltage output given a current, state of charge and temperature. The average accuracy of modelling the DC bus voltage provides a model goodness of fit average higher than 90% for a single RC circuit model.

The quiet nature of hybrid and electric vehicles has triggered developments in research, vehicle manufacturing and legal requirements. Currently, three countries require fitting an Approaching Vehicle Alerting System (AVAS) to every new car capable of driving without a combustion engine. Various other geographical areas and groups are in the process of specifying new legal requirements. In this paper, the design challenges in the on-going process of designing the sound for quiet cars are discussed. A proposal is issued on how to achieve the optimum combination of safety, environmental noise, subjective sound character and technical realisation in an iterative sound design process. The proposed sound consists of two layers: the first layer contains tonal components with their pitch rising along with vehicle speed in order to ensure recognisability and an indication of speed.

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.

Vehicle dynamics development relies on subjective assessments (SA), which is a resource-intensive procedure requiring both expert drivers and vehicles. Furthermore, development projects becoming shorter and more complex, and increasing demands on quality require higher efficiency. Most research in this area has focused on moving from physical to virtual testing. However, SA remains the central method. Less attention has been given to provide better tools for the SA process itself. One promising approach is to introduce computer-tablets to aid data collection, which has proven to be useful in medical studies. Simple software solutions can eliminate the need to transcribe data and generate more flexible and better maintainable questionnaires. Tablets’ technical features envision promising enhancements of SA, which also enable better correlations to objective metrics, a requirement to improve CAE evaluations.

In areas such as Active Safety, new technologies, designs (e.g. AUTOSAR) and methods are introduced at a rapid pace. To address the new demands, and also requirements on Functional Safety imposed by ISO 26262, the support for engineering methods, including tools and data management, needs to evolve as well. Generic and file-based data management tools, like spreadsheet tools, are popular in the industry due to their flexibility and legacy in the industry but provide poor control and traceability, while rigid and special-purpose tools provide structure and control of data but with limited evolvability. As organizations become agile, the need for flexible data management increases. Since products become more complex and developed in larger and distributed teams, the need for more unified, controlled, and consistent data increases.

In recent years fuel consumption of passenger vehicles has received increasing attention by customers, the automotive industry, regulatory agencies and academia. However, some areas which affect the fuel consumption have received relatively small interest. One of these areas is the total energy used for vehicle interior climate which can have a large effect on real-world fuel consumption. Although there are several methods described in the literature for analyzing fuel consumption for parts of the climate control system, especially the Air-Condition (AC) system, the total fuel consumption including the vehicle interior climate has often been ignored, both in complete vehicle testing and simulation. The purpose of this research was to develop a model that predicts the total energy use for the vehicle interior climate. To predict the total energy use the model included sub models of the passenger compartment, the air-handling unit, the AC, the engine cooling system and the engine.

Subjects who are well aware of what to judge commonly yield more consistent results in laboratory listening tests. This awareness may be raised by explicit instructions and training. However, too explicit instructions or use of only trained subjects may direct experiment results in an undesired way. An alternative is to give fairly open instructions to untrained subjects, but give the subjects a chance to get familiar with the product and context by, for example, riding a representative car under representative driving conditions before entering the laboratory. In this study, sound quality assessments of interior sounds of cars made by two groups were compared. In one group subjects were exposed to the same driving conditions that were later assessed in a laboratory listening test by taking them on a ride in one of the cars to be assessed, just before entering the laboratory. In the other group subjects made the laboratory assessments without prior car riding.

When it comes to the acoustic properties of electric cars, the powertrain noise differs dramatically compared to traditional vehicles with internal combustion engines. The low frequency firing orders, mechanical and combustion noise are exchanged with a more high frequency whining signature due to electromagnetic forces and gear meshing, lower in level but subject to annoyance. Previous studies have highlighted these differences and also investigated relevant perception criteria in terms of psycho-acoustic metrics. However, investigations of differences between different kinds of electric and hybrid electric cars are still rare. The purpose of this paper was to present the distribution of tonal components in today's hybrid/electric vehicles. More specifically, the number of prominent orders, their maximum levels and frequency separation were analyzed for the most critical driving conditions. The study is based upon measurements made on 13 electrified cars on the market.

The phenomenon of three-dimensional flow separation is and has been in the focus of many researchers. An improved understanding of the physics and the driving forces is desired to be able to improve numerical simulations and to minimize aerodynamic drag over bluff bodies. To investigate the sources of separation one wants to understand what happens at the surface when the flow starts to detach and the upwelling of the streamlines becomes strong. This observation of a flow leaving the surface could be captured by investigating the limiting streamlines and surface parameters as pressure, vorticity or the shear stress. In this paper, numerical methods are used to investigate the surface pressure and flow patterns on a sedan passenger vehicle. Observed limiting streamlines are compared to the pressure distribution and their correlation is shown. For this investigation the region behind the antenna and behind the wheel arch, are pointed out and studied in detail.

The efforts of the ISO “Test Variability Task Force” have been aimed at improving the understanding and at reducing brake dynamometer test variability during performance testing. In addition, dynamometer test results have been compared and correlated to vehicle testing. Even though there is already a vast amount of anecdotal evidence confirming the fact that different procedures generate different friction coefficients on the same brake corner, the availability of supporting data to the industry has been elusive up to this point. To overcome this issue, this paper focuses on assessing friction levels, friction coefficient sensitivity, and repeatability under ECE, GB, ISO, JASO, and SAE laboratory friction evaluation tests.

As computational methodologies become more integrated into industrial vehicle pre-development processes the potential for high transient vehicle thermal simulations is evident. This can also been seen in conjunction with the strong rise in computing power, which ultimately has supported many automotive manufactures in attempting non-steady simulation conditions. The following investigation aims at exploring an efficient means of utilizing the new rise in computing resources by resolving high time-dependent boundary conditions through a series of averaging methodologies. Through understanding the sensitivities associated with dynamic component temperature changes, optimised boundary conditions can be implemented to dampen irrelevant input frequencies whilst maintaining thermally critical velocity gradients.

Within the pre-development phase of a vehicle validation process, the role of computational simulation is becoming increasingly prominent in efforts to ensure thermal safety. This gain in popularity has resulted from the cost and time advantages that simulation has compared to experimental testing. Additionally many of these early concepts cannot be validated through experimental means due to the lack of hardware, and must be evaluated via numerical methods. The Race Track Simulation (RTS) can be considered as the final frontier for vehicle thermal management techniques, and to date no coherent method has been published which provides an efficient means of numerically modeling the temperature behavior of components without the dependency on statistical experimental data.