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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.

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.

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.

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].

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 reduction of environmentally harmful gases and the ambitions to reduce the exploitation of fossil resources lead to stricter legislation for all mobile sources. Legislative development significantly affected improvements in emissions and fuel consumptions over the last years, mainly measured under laboratory conditions. But real world operating scenarios have a major influence on emissions and it is already well known that these values considerably differ from officially published figures [1]. There are regulated emissions by the European Commission by means of real driving scenarios for passenger cars. A methodology to measure real drive emissions RDE is therefore well approved for automotive applications but was not adapted for two-wheeler-applications yet [2]. Hence measurements have been performed on-road and on chassis dynamometer for motorcycles with the state of the art RDE measurement equipment to be prepared for possible future legislation.

Real world operating scenarios have a major influence on emissions and fuel consumption. To reduce climate-relevant and environmentally harmful gaseous emissions and the exploitation of fossil resources, deep understanding concerning the real drive behavior of mobile sources is needed because emissions and fuel consumption of e.g. passenger cars, operated in real world conditions, considerably differ from the officially published values which are valid for specific test cycles only [1]. Due to legislative regulations by the European Commission a methodology to measure real drive emissions RDE is well approved for heavy duty vehicles and automotive applications but may not be adapted similar to two-wheeler-applications. This is due to several issues when using the state of the art portable emission measurement system PEMS that will be discussed.

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.

The enhancement of efficiency will play a more and more important role in the development of future (small) internal combustion engines. In recent years, the Atkinson (or Extended Expansion) cycle, realized over the crank drive, attracted increasing attention. Several OEMs have investigated this efficiency-increasing principle in the whole range from small engines up to automotive engines until now. In prior publications, the authors outlined the remarkable efficiency potentials of an Extended Expansion (EE) cycle. However, for an internal combustion engine, a smooth running performance as well as low vibrations and noise emissions are relevant aspects. This is especially true for an Extended Expansion engine realized over the crank drive. Therefore, design measures concerning friction and NVH need to be taken to enable possible series production status. Basically, these measures strongly depend on the reduction of the free mass forces and moments.

In order to fulfill future regulations regarding emissions and CO2 reduction, the small engine market inclines to migrate from carburetor systems to cleaner, more efficient electronic ignition controls and electronic fuel injection systems. When implementing such mechatronic systems in small engine applications, one has to consider specific boundary conditions like the lack of relevant sensors, limited possibilities in terms of space and of course the necessity to keep the costs as low as possible. Especially in the non-road mobile machinery (NRMM) segment, the absence of sensors makes it difficult to apply standard electronic control systems, which are based on engine related input signals provided by sensors. One engine related signal, which is even provided by the simplest engine setup, is some form of the crankshaft speed since it is essential for the functionality of the engine.

The release of the “Regulation No. 168/2013” for the approval and market surveillance of two- or three-wheel motorcycles and quadricycles of the European Union started a new challenge for the motorcycle industry. One goal of the European Union is to achieve emission parity between passenger cars (EURO 6) and motorcycles (EURO 5) in 2020. The hybridization of motorcycle powertrains is one way to achieve these strict legislation limits. In the automotive sector, hybridization is well investigated and has already shown improvements of fuel consumption, efficiency and emission behavior. Equally, motorcycle applications have a high potential to improve efficiency and to meet customer needs as fun to drive as well. This paper describes a methodical approach to analyze conventional motorcycles regarding the energy and power demand for different driving cycles and driving conditions. Therefore, a dynamic or forward vehicle simulation within MATLAB Simulink is used.

A worldwide decrease of legal limits for CO2 emissions and fuel economy led to stronger efforts for achieving the required reductions. The task is to evaluate technologies for CO2 reduction and to define a combination of such measures to ensure the targets. The challenge therefor is to find the optimal combination with respect to minimal costs. Individual vehicles as well as the whole fleet have to be considered in the cost analysis - which raises the complexity. Hereby, the focus of this work is the consideration and improvement of a new model series against the background of a fleet and the selection of measures. The ratio between the costs and the effect of the measures can be different for the each vehicle configuration. Also, the determination of targets depends whether a fleet or an individual vehicle is selected and has impact on the selection and optimization process of those measures.

A steady increasing share and complexity of automotive software is a huge challenge for quality management during software development and in-use phases. In cases of faults occurring in customer’s use, warranty leads to product recalls which are typically associated with high costs. To avoid software faults efficiently, quality management and enhanced development processes have to be realized by the introduction of specific analysis methods and Key Process/Performance Indicators (KPIs) to enable objective quality evaluations as soon as possible during product development process. The paper introduces an application of specific analysis methods by using KPIs and discusses their potential for automotive software quality improvement. Target is to support quality evaluation and risk-analysis for the release process of automotive software.