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Precise prediction of combustion parameters such as peak firing pressure (PFP) or crank angle of 50% burned mass fraction is essential for optimal engine control. These quantities are commonly computed from in-cylinder pressure sensor signals and are crucial for high efficiencies and low emissions. Highly accurate in-cylinder pressure sensors are usually only applied to test rig engines due to their high cost, limited durability and special installation conditions. Therefore, there is great interest in alternative approaches that employ virtual sensing based on the non-intrusive sensor signals retrieved from common knock sensors. These methods are either data-driven or physics-based and have been proven to deliver an accurate prediction of the aforementioned combustion parameters. This paper presents a comprehensive comparison of selected approaches to determine engine combustion parameters based on knock sensor signals.

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, their indirect emission production during their overall life cycle is 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 framework, assumptions and simplifications used for the calculations, a direct comparison of the total CO2-equivalent emissions of three equivalent power and range powertrain systems - a combustion-driven, a hybrid-driven, and a cordless electric-driven - is presented.

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.

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.

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.

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.

Drivetrain electrification is increasing in the kart racing sector since noise emissions are an important factor in urban areas. To improve range, it has become necessary to optimize the rolling resistance of kart racing tires. This paper introduces a parameter study for small bias-ply tires which are used in kart racing and investigates the effect of these parameters on rolling resistance. In recent literature, rolling resistance is mostly examined in radial passenger car tires. Most testing devices are limited to rim sizes from ten inches upwards. In this study, a test rig was developed with focus on low cost and small rim sizes. This self-developed test rig was validated through a comparison with an approved test rig according to ISO 18164 standard. A parameter study was conducted to investigate the effect of changes in the construction of the tire. These changes affect the warp count of the carcass fabric and the crown angle of the different plies.

Amid the increasing demand for higher efficiency in combustion driven handheld tools, the recent developments in electric machine technology together with the already existing benefits of small combustion engines for these applications favor the investigation of potential advantages in hybrid powertrain tools. This concept-design study aims to use a fully parametric, system-level simulation model with exchangeable blocks, created with a power-loss approach in Matlab and Simulink, in order to examine the potential of different hybrid configurations for different tool load cycles. After the model introduction, the results of numerous simulations for 36 to 100 cc engine displacement will be presented and compared in terms of overall system efficiency and overall powertrain size. The different optimum hybrid configurations can show a reduction up to 30 % in system’s brake specific fuel consumption compared to the baseline combustion engine driven model.

The absence of combustion engine noise pushes increasingly attention to the sound generation from other, even much weaker, sources in the acoustic design of electric vehicles. The present work focusses on the numerical computation of flow induced noise, typically emerging in components of flow guiding devices in electro-mobile applications. The method of Large-Eddy Simulation (LES) represents a powerful technique for capturing most part of the turbulent fluctuating motion, which qualifies this approach as a highly reliable candidate for providing a sufficiently accurate level of description of the flow induced generation of sound. Considering the generic test configuration of turbulent pipe flow, the present study investigates in particular the scope and the limits of incompressible Large-Eddy Simulation in predicting the evolution of turbulent sound sources to be supplied as source terms into the acoustic analogy of Lighthill.

Lean burn combustion concepts with high mean effective pressures are being pursued for large gas engines in order to meet future stringent emission limits while maintaining high engine efficiencies. Since severe boundary conditions for the ignition process are encountered with these combustion concepts, the processes of spark ignition and flame initiation are important topics of applied research, which aims to avoid misfiring and to keep cycle-to-cycle combustion variability within reasonable limits. This paper focuses on the fundamental investigation of early flame kernel development using different ignition system settings. The investigations are carried out on a rapid compression-expansion machine in which the spark ignition process can be observed under engine-like pressure and excess air ratio conditions while low flow velocities are maintained.

Aerodynamic CFD simulations in the automotive industry, which are based on the steady-state RANS (Reynolds-averaged Navier-Stokes) approach typically utilize approximate numerical methods to account for rotating wheels. In these methods, the computational mesh representing the rim geometry remains stationary, and the influence of the wheel rotation on the air flow is modelled. As the rims are considered only in one fixed rotational position (chosen arbitrarily in most cases), the effects of the rim orientation on the aerodynamic simulation results are disregarded and remain unquantified. This paper presents a numerical sensitivity study to examine the impact of the rim orientation position on the aerodynamic parameters of a detailed production vehicle. The simulations are based on the steady-state RANS approach.

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)”.

In the research and development of internal combustion engines, there are several drivers for developing an accurate online lube oil consumption (LOC) measurement system. Lube oil consumption is considered to be a root cause of hydrocarbon and particle emissions and lubricating oil autoignition. It also negatively influences the life cycle cost for engine operators. Highly accurate measurement of lube oil consumption must be possible before it can be reduced - or rather optimized - to levels stakeholders will require in the future. State-of-the-art methods such as gravimetric and volumetric measurements are not fully satisfactory for several reasons. Generally, offline LOC measurement is no longer suitable for fast and accurate measuring cycles, oil condition monitoring and wear monitoring. At present, tracer methods are considered to be the most promising approach. However, current tracer methods have their downsides as well.

Measuring emissions of internal combustion engines-not only at steady-state conditions, but also with highly dynamic test cycles-is an important issue in modern engine development. Due to the fact that ambient conditions have an essential influence on power and emissions of internal combustion engines, test beds used for such measurements typically incorporate intake air and exhaust back pressure control for reasons of repeatability, accuracy and comparability. As test cycle dynamics get faster and legal pressure tolerances get narrower, pressure control becomes more demanding and simple PI control schemes are pushed to their limits; therefore, more sophisticated control schemes are necessary. In this paper, a linearised model is first derived and then used to both simplify and optimise PI controller tuning. This is done by means of frequency domain methods. Limitations to such controllers and possible approaches to overcome them are discussed.

Small engines for non-automotive applications include 2-stroke and 4-stroke gasoline engine concepts which have a reduced number of sensors due to cost and packaging constraints. In order to cope with future emission regulations, more sophisticated engine control and monitoring becomes mandatory. Therefore, a cost-effective way has to be found to gain maximum information from the existing sensors and actuators. Due to an increasing bio-fuel share in the market, the detection of bio-fuel content is necessary to guarantee a stable combustion by adapting the injection and ignition control strategy. Meaningful information about the combustion can be retrieved from combustion chamber ion current measurements. This paper proposes a general overview of combustion process monitoring in different engine concepts by measuring the ion current during combustion.

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.