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The production of electric vehicles (EVs) has a significant environmental impact, with up to 50 % of their lifetime greenhouse gas potential attributed to manufacturing processes. The use of sustainable materials in EV design is therefore crucial for reducing their overall carbon footprint. Wood laminates have emerged as a promising alternative due to their renewable nature. Additionally, wood-based materials offer unique damping properties that can contribute to improved Noise, Vibration, and Harshness (NVH) characteristics. In comparison to conventional materials such as aluminum, ply wood structures exhibit beneficial damping properties. The loss factor of plywood structures with a thickness below 20 mm ranges from 0.013 to 0.032. Comparable aluminum structures however exhibit only a fraction of this loss factor with a range between 0.002 and 0.005.

Trim materials are often used for vibroacoustic energy absorption purposes within vehicles. To estimate the sound impact at a driver’s ear, the substructuring approach can be applied. Thus, transfer functions are calculated starting from the acoustic source to the car body, from the car body to the trim and, finally, from the trim to the inner cavity where the driver is located. One of the most challenging parts is the calculation of the transfer functions from the car body inner surface to the bottom trim surface. Commonly, freely laying mass-spring systems (trims) are simulated with a fixed boundary and interface phenomena such as friction, stick-slip or discontinuities are not taken into consideration. Such an approach allows for faster simulations but results in simulations strongly overestimating the energy transfer, particularly in the frequency range where the mass-spring system’s resonances take place.

In an era characterized by the rapid proliferation and advancement of AI-based technologies across various domains, the spotlight is placed on the integration of these technologies into trustworthy autonomous systems. The integration into embedded systems necessitates a heightened focus on dependability. This paper combines the findings from the TEACHING project, which delves into the foundations of humanistic AI concepts, with insights derived from an expert workshop in the field of dependability engineering. We establish the body of knowledge and key findings deliberated upon during an expert workshop held at an international conference focused on computer safety, reliability and security. The dialogue makes it evident that despite advancements, the assurance of dependability in AI-driven systems remains an unresolved challenge, lacking a one-size-fits-all solution.

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.

The degradation rate of a Li-ion battery is a complex function of temperature and charge/discharge rates over its lifetime. There is obviously a keen interest in predictive electrochemical ageing models that account for known degradation mechanisms, primarily linked with the Solid Electrolyte Interface (SEI) formation and Li-plating, which are highly dependent on the cell temperature. Typically, such ageing models are formulated and employed at pack or cell level, neglecting intra-cell and cell-to-cell thermal and electrical non-uniformities. On the other hand, thermal management techniques can mitigate ageing by maintaining the battery pack within the desired temperature window either by cooling or heating. Inevitably, the cooling of the battery pack by conventional heat exchangers will introduce temperature non-uniformities that may in turn result in undesired intra-cell and/or cell-to-cell health non-uniformities.

Virtual sensing, i.e., the method of estimating quantities of interest indirectly via measurements of other quantities, has received a lot of attention in various fields: Virtual sensors have successfully been deployed in intelligent building systems, the process industry, water quality control, and combustion process monitoring. In most of these scenarios, measuring the quantities of interest is either impossible or difficult, or requires extensive modifications of the equipment under consideration – which in turn is associated with additional costs. At the same time, comprehensive data about equipment operation is collected by ever increasing deployment of inexpensive sensors that measure easily accessible quantities. Using this data to infer values of quantities which themselves are impossible to measure – i.e., virtual sensing – enables monitoring and control applications that would not be possible otherwise.

The degradation rate of Li-ion batteries and therefore their useful life depends on many parameters, including temperature, charge/discharge rates, the chemistry and microstructure of electrodes. The importance of understanding these mechanisms explains the large interest in developing predictive electrochemical ageing models accounting for the known deterioration mechanisms, mainly related to SEI layer formation and Li-plating. Usually, these ageing models are developed and applied at cell level assuming perfect uniformity in all dimensions apart from the through-plane direction. In this work, we extend the model to all dimensions within the cell to account for intra-cell non-uniformities in terms of local temperature and current. However, the temperature distribution of a cell within a battery pack depends on the interaction with its environment, which typically involves active cooling via an external fluid circulation within a channel network.

The upcoming Euro 7 regulation introduces the concept of continuous On-Board (emission) Monitoring (OBM), while On-Board Fuel/Energy Consumption Monitoring (OBFCM) is already an integral part of modern vehicles. The current work aims to assess whether on-board data could provide sufficient information to characterize real-world vehicle performance and emissions. Nine Euro 6d-ISC-FCM passenger cars were used, covering a wide range of powertrain technologies, from conventional gasoline and diesel to hybrid (HEV) and plug-in hybrid (PHEV) electric vehicles. Three vehicles were thoroughly tested in the laboratory and on the road, aiming at evaluating in detail the on-board data monitoring system. The evaluation concerned OBFCM device recordings of fuel consumed and distance travelled, as well as tailpipe NOx emissions and exhaust mass flow rate.

The proposed Euro 7 emission standard foresees that the emission behaviour of Euro 7 vehicles is monitored via an on-board monitoring (OBM) system. In Euro 7 vehicles, OBM systems will monitor the emissions of nitrogen oxides (NOX), ammonia (NH3) and particulate matter (PM) for every trip through a combination of measured and modelled data. Sensors employed to support on-board diagnostics (OBD) in current vehicles may be used to support OBM. According to the Euro 7 OBM concept presented in this paper, OBM will serve a dual purpose: the first is to warn the user of a vehicle about the need to perform repairs on the engine or the pollution control systems when these are needed. If these repairs are not performed in a timely manner, the OBM system will be able to ultimately prevent engine restart, akin to the existing low-reagent driver warning system in some compression ignition vehicles. The second purpose of OBM is to monitor the compliance of vehicle types with the emission limits.

Following the recent trend in the automotive industry, hybrid and pure electric powertrain systems are more and more preferred over conventional combustion powertrain systems due to their significant potential to reduce greenhouse-gas emissions. Although electric powertrains do not produce direct emissions during their operational time, the indirect emissions over their whole life cycle have to be taken into consideration. In this direction, the carbon footprint due to the electrification of the hand-held power tool industry needs to be examined in the preliminary design phase. In this paper, after defining the carbon footprint calculation framework, assumptions and simplifications used for the calculations, a direct comparison of the total carbon dioxide equivalent (CO2eq) emissions of three equivalent power and range powertrain systems - a combustion-driven, a hybrid-driven, and a cordless electric-driven - is presented.

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.

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.

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.

On the basis of small hybrid powertrain investigations in hand-held power tools for fuel consumption and emissions reduction, the prototype hybrid configuration of a small single-cylinder four-stroke internal combustion engine together with a brushless DC electric motor is built and measured on the testbench in terms of efficiency and emissions but also torque and power capabilities. The onboard energy storage system allows the combustion engine electrification for controlling the fuel amount and the combustion behavior while the electric motor placement instead of the pull-start and flywheel allows for start-stop of the system and load point shifting strategy for lower fuel consumption. The transient start-up results as well as the steady-state characterization maps of the system can set the limits on the fuel consumption reduction for such a hybrid tool compared with the baseline combustion-driven tool for given load cycle characteristics.

Virtual sensing refers to the processing of desired physical data based on measured values. Virtual sensors can be applied not only to obtain physical quantities which cannot be measured or can only be measured at an unreasonable expense but also to reduce the number of physical sensors and thus lower costs. In the field of spark ignited internal combustion engines, the virtual sensing approach may be used to predict the cylinder pressure signal (or characteristic pressure values) based on the acceleration signal of a knock sensor. This paper presents a method for obtaining the cylinder pressure signal in the high-pressure phase of an internal combustion engine based on the measured acceleration signal of a knock sensor. The approach employs a partial differential equation to represent the physical transfer function between the measured signal and the desired pressure. A procedure to fit the modeling constants is described using the example of a large gas engine.

A transient behavior investigation of a hybrid hand-held tool is carried out on near real load conditions, through a hybrid experimental and simulative study. As this study focuses on handheld tools with a varied or transient load operation like chainsaws and brush cutters, a use of a blower tool as a test-carrier and a throttle body implementation on its blower air pipe adds a controllable braking mechanism. This allows for driving varied load cycles without the need of a testbench. Experimental investigation takes place at both start-up, shut-down and load conditions and for different drive control and commutation modes of electric motor. The controller characterization and parameter selection are done. After the load cycles are driven on the test-carrier, the characterizing data are transferred to the MATLAB and Simulink simulation model to correct and calibrate its transient behavior.

Due to current progresses in the field of driver assistance systems and the continuously growing electrification of vehicle drive trains, the evaluation of driver behavior has become an important part in the development process of modern cars. Findings from driver analyses are used for the creation of individual profiles, which can be permanently adapted due to ongoing data processing. A benefit of data-based dynamic control systems lies in the possibility to individually configure the vehicle behavior for a specific driver, which can contribute to increasing customer acceptance and satisfaction. In this way, an optimization of the control behavior between driver and vehicle and the resulting mutual system learning and -adjustment hold great potential for improvements in driving behavior, safety and energy consumption.

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.

Automated, conductive charging systems enable both, the transmission of high charging power for long electric driving distances as well as comfortable and safe charging processes. Especially by the use of heavy and unhandy cables for fast charging, these systems offer user friendly vehicle charging - in particularly in combination with autonomously driving and parking vehicles. This paper deals with the definition of requirements for automated conductive charging stations with standard charging connectors and vehicle inlets and the development of a fully-automated charging robot for electric and plug-in hybrid vehicles. In cooperation with the project partners BMW AG, MAGNA Steyr Engineering, KEBA AG and the Institute of Automotive Engineering at Graz University of Technology, the development and implementation of the prototype took place in the course of a governmental funded research project titled “Comfortable Mobility by Technology Integration (KoMoT)”.