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This paper describes the development of a 0-D-sulfur poisoning model for a NOx storage catalyst (NSC). The model was developed and calibrated using findings and data obtained from a passenger car diesel engine used on testbed. Based on an empirical approach, the developed model is able to predict not only the lower sulfur adsorption with increasing temperature and therefore the higher SOx (SO2 and SO3) slip after NSC, but also the sulfur saturation with increasing sulfur loading, resulting in a decrease of the sulfur adsorption rate with ongoing sulfation. Furthermore, the 0-D sulfur poisoning model was integrated into an existing 1-D NOx storage catalyst kinetic model. The combination of the two models results in an “EAS Model” (exhaust aftertreatment system) able to predict the deterioration of NOx-storage in a NSC with increasing sulfation level, exhibiting higher NOx-emissions after the NSC once it is poisoned.

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.

State of the art technologies of 2 and 4 stroke engines have to fulfill severe future exhaust emission regulations, with special focus on the aspects of rising performance and low cost manufacturing, leading to an important challenge for the future. In special fields of applications (e.g. mopeds, hand held or off-road equipment) mainly engines with simple mixture preparation systems, partially without exhaust gas after treatment are used. The comparison of 2 and 4 stroke concepts equipped with different exhaust gas after treatment systems provides a decision support for applications in a broad field of small capacity engine classes.

The future exhaust emission legislation limits and the procedures for running the test cycles will have an important influence on future range extender concepts. Due to the special steady state operation strategy of the range extender engines, it is possible to create a simple methodology for comparing engine test bench emissions with the emission limits of exhaust gas legislations. Therefore the energy demand of a predefined vehicle was simulated with PHEM, a longitudinal dynamic simulation tool. According to that, the influence of different exhaust gas after treatment systems and preheating options on the tolerated raw emission concentration will be analyzed. With this information, a few chosen range extender engine concepts will be compared concerning their suitability for future exhaust emission legislations. The selection of the range extender concepts was carried out with the methotology of a value benefit analysis.

Friction losses as well as lube oil consumption at the piston group are key factors for future engine downsizing concepts regarding to emissions and consumption. This means an early identification of friction losses and wear is essential within development. The main problem is that the wear assessment is based on long durability tests which are typically performed in a later phase. This may lead to the fact that an early optimized configuration with respect to friction can cause a potential wear problem later in the durability test program. Still ongoing trends in combustion engine engineering lead to both the minimized oil supply in the tribocontact piston bore interface and improved wear resistance. One is forced to the conclusion that understanding and quantifying wear will be a key driver for the future engine development process. The aim is a holistic concept that combines different methods to investigate wear and furthermore its combination with friction loss studies.

Aiming at the test safety problems in the early stage of self-driving cars development, firstly the virtual vehicle on-board CAN data acquisition module of the present project was designed based on virtual LabVIEW. Then a wireless remote control system for the self-driving car was constructed, which integrated the built virtual vehicle on-board CAN data acquisition system, the remote real-time image monitoring module and the remote upper computer control module based on ZigBee wireless transmission. It can execute the environmental awareness training and continuous and complex motion manipulation testing of the vehicle without relying on the driver, which can solve the safety problems in the tests of initial development of self-driving cars. Finally, the four-wheel independent steering electric vehicle was used as the self-driving test vehicle, and the wireless remote control system was tested on the double lane change type path and S-type path.

To increase reliability and the maintenance interval of an internal combustion engine while operating it with the lowest possible emissions, spark plug wear must be reduced. In this context, information about the spark plug center and the ground electrode temperature is key. Several measurement devices have been developed that measure the temperature of spark plug electrodes. The great challenge is to measure the temperature of the center electrode; on the one hand, the measurement device must be insulated and capable of withstanding the high voltage of the ignition system, and on the other hand, the device should not influence the ignition system. All previously studied devices presented in this paper have in common that major reconstruction of the ignition system and/or spark plugs whose design is very different from the standard engine spark plug were necessary.

As the number of different engine and vehicle concepts for powered-two wheelers is very high and will even rise with hybridization, the simulation of emissions and fuel consumption is indispensable for further development towards more environmentally friendly mobility. In this work, an adaptive artificial neural network based predictive model for emission and fuel consumption simulation of motorcycles operated in real world conditions is presented. The model is developed in Matlab and Simulink and is integrated into a longitudinal vehicle dynamic simulation whereby it is possible to simulate various and not yet measured test cycles. Subsequently, it is possible to predict real drive emissions RDE and on-road fuel consumption by a minimum of previous measurement effort.

The pulse-echo method of non-destructive ultrasonic material testing is modeled by an iterative coupling scheme combining analytical and numerical methods in different domains. The approach offers significant advantages in terms of computational efficiency compared to volumetric mesh-based numerical models if the distance between transducer and specimen is large compared to the wavelength in the coupling medium. Excitation and results are given in the time domain while computations are performed in the frequency domain and are compatible with usual FEM solvers for linear elasticity. Due to the use of interpolated phase-shifted transfer functions only a small number of frequency lines is required in comparison to a direct Fourier transform of signals. The method is illustrated alongside a typical application on a steel structure embedded in water.

Looking at upcoming emission legislations for two-wheelers, it is quite obvious that the fulfilment of these targets will become one of the biggest challenges within the engine development process. The gradual harmonization of emission limits for two-wheelers with existing automotive standards will subsequently lead to new approaches regarding mixture preparation and exhaust gas aftertreatment. Referring to these future scenarios, a state-of-the-art in development of catalytic converters for two- or three-wheeler applications should be presented. After choosing a suitable test carrier, which has already been equipped with EFI components including an oxygen sensor for λ=1 operation mode, a basic injection system calibration was used to optimize the combustion process. Based on this setup, a variable exhaust system was manufactured to be able to integrate different catalyst configurations.

As a result of the ever-increasing application of cyber-physical components in the automotive industry, cybersecurity has become an urgent topic. Adapting technologies and communication protocols like Ethernet and WiFi in connected vehicles yields many attack scenarios. Consequently, ISO/SAE 21434 and UN R155 (2021) define a standard and regulatory framework for automotive cybersecurity, Both documents follow a risk management-based approach and require a threat modeling methodology for risk analysis and identification. Such a threat modeling methodology must conform to the Threat Analysis and Risk Assessment (TARA) framework of ISO/SAE 21434. Conversely, existing threat modeling methods enumerate isolated threats disregarding the vehicle’s design and connections. Consequently, they neglect the role of attack paths from a vehicle’s interfaces to its assets.

The current European fleet of vehicles is ageing and lifetime mileages are rising proportionally. Consequently, a substantial fraction of the vehicle fleet is currently operating at mileages well beyond current durability legislation (≤ 160,000 km). Emissions inventories and models show substantial increases in emissions with increasing mileage, but knowledge of the effect of emissions control system deterioration at very high mileages is sparse. Emissions testing has been conducted on matched pairs (or more) of diesel and gasoline (and CNG) vehicles, of low and high mileage, supplementing the results with in-house data, in order to explore high mileage emission deterioration factors (DF). The study isolated, as far as possible, the effect of emissions deterioration with mileage, by using nominally identical vehicle models and controlling other variables.

The replacement of safety-critical mechanical components with electro-mechanical systems has led to the fact that safety aspects play a central role in development of embedded automotive systems. Recently, consumer demands for connectivity (e.g., infotainment, car-2-car or car-2-infrastructure communication) as well as new advances toward advanced driver assistance systems (ADAS) or even autonomous driving functions make cybersecurity another key factor to be taken into account by vehicle suppliers and manufacturers. Although these can capitalize on experiences from many other domains, they still have to face several unique challenges when gearing up for specific cybersecurity challenges. A key challenge is related to the increasing interconnection of automotive systems with networks (such as Car2X). Due to this connectivity, it is no longer acceptable to assume that safety-critical systems are immune to security risks.

Brake wear emissions gained significant relevance with the upcoming Euro7 type approval within the European Union for brake emission measurement on the test bed. While the controlled brake test bed approach provides consistent results, real-driving emission (RDE) measurements are needed to better understand actual emission behavior due to varying vehicle and environmental conditions. The EU has already announced its interest in RDE testing. Here we present the results of an RDE brake wear sampling system with minimal thermal impact, where particles are only sampled from one side of the brake disc, characterized on a laboratory sampling system. The investigations aim to validate symmetric particle release and to confirm that doubling the measured RDE results effectively represents the reference emissions on the test bed.

This work presents a physical model that calculates the efficiency maps of the inverter-fed Permanent Magnet Synchronous Machine (PMSM) drive. The corresponding electrical machine and its controller are implemented based on the two-phase (d-q) equivalent circuits that take into account the copper loss as well as the iron loss of the PMSM. A control strategy that optimizes the machine efficiency is applied in the controller to maximize the possible output torque. In addition, the model applies an analytical method to predict the losses of the voltage source inverter. Consequently, the efficiency maps within the entire operating region of the PMSM drive can be derived from the simulation results, and they are used to represent electric drives in the system simulation model of electric vehicles (EVs).

Precise prediction of combustion parameters such as peak firing pressure (PFP) or crank angle of 50% burned mass fraction (MFB50) is essential for optimal engine control. These quantities are commonly determined from in-cylinder pressure sensor signals and are crucial to reach high efficiencies and low emissions. Highly accurate in-cylinder pressure sensors are only applied to test rig engines due to their high cost, limited durability and special installation conditions. Therefore, alternative approaches which employ virtual sensing based on signals from non-intrusive sensors retrieved from common knock sensors are of great interest. This paper presents a comprehensive comparison of selected approaches from literature, as well as adjusted or further developed methods to determine engine combustion parameters based on knock sensor signals. All methods are evaluated on three different engines and two different sensor positions.

The objective of this experimental investigation was to analyze the effect of various exhaust gas aftertreatment technologies on particulate number emissions (PN) of an MPFI EU5 motorcycle. Specifically, three different aftertreatment strategies were compared, including a three-way-catalyst (TWC) with LS structure as the baseline, a hybrid catalyst with a wire mesh filter, and an optimized gasoline particulate filter (GPF) with three-way catalytic coating. Experimental investigations using the standard test cycle WMTC performed on a two-wheeler chassis dynamometer, while the inhouse particulate sampling system was utilized to gather information about size-dependent filtering efficiency, storage, and combustion of nanoparticles. The particulate sampling and measuring system consist of three condensation particle counters (CPCs) calibrated to three different size classes (SPN4, SPN10, SPN23).

Expansion chamber mufflers are commonly applied to reduce noise in HVAC. Dissipative materials, such as microperforated plates (MPPs), are often applied to achieve a more broadband mitigation effect. Such mufflers are typically characterized in the frequency domain, assuming time-harmonic excitation. From a computational point of view, transient analyses are more challenging. A transformation of the equivalent fluid model or impedance boundary conditions into the time domain induces convolution integrals. We apply the recently proposed finite element formulation of a time domain equivalent fluid (TDEF) model to simulate the transient response of dissipative acoustic media to arbitrary unsteady excitation. As most time domain approaches, the formulation relies on approximating the frequency-dependent equivalent fluid parameters by a sum of rational functions composed of real-valued or complex-conjugated poles.

Cybersecurity assumes a major role in the context of the automotive domain, where both existing and forthcoming regulations are heightening the need for robust security engineering. A significant milestone in advancing cybersecurity within the automotive industry is the release of the first international standard for automotive cybersecurity ISO/SAE 21434:2021 ‘Road Vehicles — Cybersecurity Engineering’. A recently published type approval regulation for automotive cybersecurity (UN R155) is also tailored for member countries of the UNECE WP.29 alliance. Thus, the challenges for embedded automotive systems engineers are increasing while frameworks, tools and shared concepts for cybersecurity engineering and training are scarce.

Brake wear particles are recognized as one of the dominant sources of road transport particulate matter emissions and are linked to adverse health effects and environmental impact. The UNECE mandated the Particle Measurement Program to address this issue, by developing a harmonized sampling and measurement methodology for the investigation of brake wear particles on a brake dynamometer (dyno). However, although the brake dyno approach with tightly controlled test conditions offers good reproducibility, a multitude of changing vehicle and surrounding conditions make real-driving emissions measurement a highly relevant task. Here we show two different prototypes for on-road particle measurement with minimal impact of the measurement setup on the emission behavior, tested on a brake dyno.