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The effectiveness of ADAS addressing property damage has an increasing impact on car manufacturers, insurers and customers, as accident avoidance or mitigation can lead to loss reduction. In order to obtain benefits, it is essential that ADAS primarily address monetarily relevant accident scenarios. Furthermore, sensor technologies and algorithms have to be configured in a way that relevant accident situations can be sufficiently avoided at reasonable system costs. A new methodology is developed to identify and configure monetarily effective parameters for ADAS during parking and maneuvering. ADAS parameters e.g. relevant accident scenarios, required crash avoidance speeds and different sensor layouts are analyzed and evaluated using a real-world in-depth accident database of insurance claims provided by Allianz Center for Technology and Allianz Automotive Innovation Center. For this purpose, a sensitivity analysis is conducted to identify most monetarily effective accident scenarios.

Global emission targets and high fuel costs encourage manufacturers to produce ultralight vehicles. This and the low range of electric cars have resulted in new vehicle concepts and reuse of the quadricycles classification for microcars in Europe.

Battery aging in electric and hybrid vehicles is a major issue, and one which has to be taken into consideration during all stages of the vehicle lifecycle. It depends on many factors, such as the cell chemistry, the cell design and stress factors as well as the current rate, ΔDOD and temperature. The stress factors have been identified as being crucial due to their influence on two important battery parameters: capacity and inner resistance. Battery aging models are essential to describing the interacting influences that stress factors have on battery parameters. They provide insights about battery aging without the need for extensive measurements. Various battery aging models with widely varying capabilities are described in the literature. The aim of this paper is to provide a decision guide for utilizing the most appropriate aging model for the major stages of the vehicle lifecycle: vehicle development, operation (onboard and offboard) and post-operation.

Upcoming stringent regulations on emissions and fuel efficiency are driving the automotive industry towards lightweight vehicle design. Thus, a higher share of carbon fiber composite materials in vehicle structures is expected. Current literature addresses development processes of composite components under a limited scope, however the considerations of design parameters used in these studies are inadequate for the realistic development of a full vehicle structure, especially in a resource-constrained development project. In addition, existing vehicle structure design philosophies applied for metal structures cannot be directly ported over for composite design due to differences in material properties, failure modes and design for manufacturing limitations.

This paper focuses on the manufacturer's conflict in the conceptual design of commercial vehicles between highly customized special vehicles and the greatest possible degree of standardization. Modularity and standardization are crucial success factors for realizing high variance at the best cost efficiency in development and production as well for achieving the highest quality standards at reduced efforts for technical validation. The presented virtual design approach for commercial vehicle concepts allows for purposeful design and integration of new concepts and technologies on the component level in an existing product portfolio - not neglecting manufacture's portfolio requirements concerning standardization and modularity. The integrated tool chain helps to bring trade-offs to a head that exist in balancing between dedicated vehicles with best customer-relevant characteristics and standardized vehicles with the highest degree of commonality.

Among the issues affecting the design process of a vehicle, there is the lack of multidisciplinary knowledge among the different teams involved. This often leads to the risk of loosing important key points from the initial concept idea of designers to the final vehicle package definition made by engineers. Therefore, this study builds up a method based on parameters defining exterior aesthetic priorities according to car segment to support engineers involved in the automotive design process. In particular, during the early design-engineering phase, this method should help them to understand better vehicle proportions defined by designers. This work is currently used in the course of design and simulation of road vehicles (Chair of Automotive Technology, TUM) to explain fundamentals of automotive design.

In this analysis we assess the life cycle greenhouse gas (GHG) emissions of four types of vehicles which might play a role in achieving future emission reductions: vehicles using compressed natural gas (CNG), battery electric vehicles (BEVs), mild hybrid CNG vehicles and range extended BEVs. Our analysis covers the manufacturing processes of these vehicles and their use as a city taxi in Singapore. We also consider upstream emissions from fuel and electricity production. All necessary parameters are derived from an intensive literature review and the model for calculating the life cycle emissions is presented. The influence of data uncertainties is analyzed by parameter variations within different scenarios. The calculation results are found to be quite robust: The BEV and the mild hybrid CNG vehicle similarly show very low GHG emissions within all scenarios whereas the pure CNG vehicle always ranks the worst.

In current research projects such as "Vehicle to Grid" (V2G), "Vehicle to Building" (V2B) or "Vehicle to Home" (V2H), plug-in vehicles are integrated into stationary energy systems. V2B or V2H therefore stands for intelligent networking between vehicles and buildings. However, in these projects the objective is mostly from a pure electric point of view, to smooth the load profile on a household level by optimized charging and discharging of electric vehicles. In the present paper a small energy system of this kind, consisting of a building and a vehicle, is investigated from a holistic point of view. Thermal as well as electrical system components are taken into account and there is a focus on reduction of overall energy consumption and CO₂ emissions. A predictive energy management is presented that coordinates the integration of a plug-in hybrid electric vehicle into the energy systems of a building. System operation is optimized in terms of energy consumption and CO₂ emissions.

The anti-lock braking system (ABS) is a widespread driver assistance system which allows a short braking distance while simultaneously maintaining the stability and steerability of the car. Vehicles with electric single-wheel drive offer many possibilities of improving the energy efficiency and the braking performance during ABS braking. In this paper, two different ways of including the electric machines in the ABS are analyzed in detail: the damping of torsional drive train vibrations in combination with recuperation and the dynamic split of the braking torque, where the hydraulic braking torque is kept constant and the dynamic modulation of the braking torque is performed by the electric machines. The damping algorithm is developed on the basis of a linearized model of the drive train and the tire-road contact by using state feedback and pole placement methods. Simulation results with a detailed multi-body system show the effectiveness of the control algorithms.

Issues relating to the reduction of CO₂ emissions and energy consumption are currently more important than ever before. In the construction engineering and automotive sectors research and development efforts are focused closely on efficient buildings and automobiles. The designated target is a reduction in greenhouse gas emissions and overall energy demand. However, almost all approaches focus solely on either "buildings" or "mobility." By considering both aspects as a single holistic system, further energy saving potential arises due to synergetic effects. The goal of current research projects relating to Smart Homes and Vehicle to Building (V2B) is to smooth the electrical load profile on a household level rather than to reduce the individual-related total energy consumption and thereby the CO₂ emissions.

Electric vehicles differ from conventionally powered vehicles in terms of many characteristics that are directly relevant to the customer. The most evident ones are the total driving range, which is limited by the battery capacity, and the different acceleration behavior, which is directly influenced by the electric motor's torque characteristics. Furthermore, there are many other vehicle characteristics, such as lateral dynamics, that are also strongly influenced by electrification. For all customer-relevant vehicle characteristics, it is important to know the necessary and optimal fulfillments in order to plan and evaluate new electrified vehicle concepts. Correlation functions can be used to convert values for technical characteristics to normalized customer satisfaction fulfillments. To evaluate the quality of a vehicle concept during the development process, a parametric cost function is defined.

The potential of determining the change of injury severity in the accident event taking passive as well as active measures into account at the vehicle (integral systems) are at present limited to pedestrian protective systems. Therefore, an extension of the existing methods for the application with common integral systems (front protection, side protection, etc.) is suggested. Nowadays the effectiveness of passive safety systems is determined in crash tests with very high accident severities. However, approximately 90% of real-world accidents have a lower accident severity as the required crash tests. Thus, this paper will present a method calculating the effectiveness of such an integral system based on real-world accident data. For these reasons, this paper is presenting a method for a more valid prediction of injury severity. The German In-Depth Database GIDAS allows clustering the accident event in relevant car-to-car scenarios.