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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.

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.

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)”.

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.

Car driver’s behavior and its influence on driving characteristics play an increasing role in the development of modern vehicles, e.g. in view of efficient powertrain control and implementation of driving assistance functions. In addition, knowledge about actual driving style can provide feedback to the driver and support efficient driving or even safety-related measures. Driving patterns are caused not only by the driver, but also influenced by road characteristics, environmental boundary conditions and other traffic participants. Thus, it is necessary to take the driving circumstances into account, when driving patterns are studied. This work proposes a methodology to cluster and evaluate driving patterns under consideration of vehicle-related parameters (e.g. acceleration and jerk) in combination with additional influencing factors, e.g. road style and inclination. Firstly, segmentation of the trip in distance series is performed to generate micro cycles.

The paper focuses on an optimization methodology, which uses Design of Experiments (DoE) methods to define component parameters of parallel hybrid powertrains such as number of gears, transmission spread, gear ratios, progression factor, electric motor power, electric motor nominal speed, battery voltage and cell capacity. Target is to find the optimal configuration based on specific customer targets (e.g. fuel consumption, performance targets). In the method developed here, the hybrid drive train configuration and the combustion engine are considered as fixed components. The introduced methodology is able to reduce development time and to increase output quality of the early system definition phase. The output parameters are used as a first hint for subsequently performed detailed component development. The methodology integrates existing software tools like AVL CRUISE [5] and AVL CAMEO [1].

The present technical article deals with the modeling of dynamic tire forces, which are relevant during interactions of safety relevant Advanced Driver Assistance Systems (ADAS). Special attention has been paid on simple but effective tire modeling of semi-physical type. In previous investigations, experimental validation showed that the well-known first-order Kelvin-Voigt model, described by a spring and damper element, describes good suitability around fixed operation points, but is limited for a wide working range. When aiming to run vehicle dynamics models within a frequency band of excitation up to 8 Hz, these models deliver remarkable deviations from measured tire characteristics. To overcome this limitation, a nonlinear Maxwell spring-damper element was introduced which is qualified to model the dynamic hardening of the elastomer materials of the tire.

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.

Experimental investigations on engine test beds represent a significant cost in engine development. To reduce development time and related costs, it is necessary to check the quality of measurements automatically whenever possible directly on the test bed to allow early detection of faults. A fault diagnosis system should provide information about the presence, cause and magnitude of an inconsistency in measurement. The main challenge in developing such a system is to detect the fault quickly and reliably. However, only faults that have actually occurred should be detected because the user will only adopt a system that provides accurate results. This paper presents a methodology for automated fault diagnosis at engine test beds, starting with an explanation of the general procedure. Next, the methods applied for fault detection are introduced.

Worldwide increasing energy consumption, decreasing energy resources and continuous restriction of emission legislation cause a rethinking in the development of internal combustion engines and fuels. Alternative renewable fuels, so called bio-fuels, have the potential to contribute to environmentally friendly propulsion systems. This study concentrates on the usage of alcohol fuels like ethanol, methanol and butanol in non-automotive high power engines, handheld power tools and garden equipment with the focus on mixture formation and cold start capability. Although bio-fuels have been investigated intensely for the use in automotive applications yet, the different propulsion systems and operation scenarios of nonautomotive applications raise the need for specific research. A zero dimensional vaporization model has been set up to calculate the connections between physical properties and mixture formation.