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This paper describes a novel approach for modeling an automotive HVAC unit. The model consists of black-box models trained with experimental data from a self-developed measurement setup. It is capable of predicting the temperature and mass flow of the air entering the vehicle cabin at the various air vents. A combination of temperature and velocity sensors is the basis of the measurement setup. A measurement fault analysis is conducted to validate the accuracy of the measurement system. As the data collection is done under fluctuating ambient conditions, a review of the impact of various ambient conditions on the HVAC unit is performed. Correction models that account for the different ambient conditions incorporate these results. Numerous types of black-box models are compared to identify the best-suited type for this approach. Moreover, the accuracy of the model is validated using test drive data.

In this paper a method for the identification of most significant vehicle parameters influencing the behavior of a lateral control system of autonomous car is presented. Requirements for the design stage of the controller need to consider many uncertainties in the plant. While most vehicle properties can be compensated by an appropriate tuning of the control parameters, other vehicle properties can change significantly during usage. The control system is evaluated based on performance measures. Analyzed parameters comprise functional tire characteristics, mass of the vehicle and position of its center of gravity. Since the parameters are correlated, but Sobol’ sensitivity analysis assumes decorrelated inputs, random variation yields no reasonable results. Furthermore, the variation of each parameter or set of parameters is not applicable since the numbers of required simulations is increased significantly according to input dimension.

Highly automated driving (HAD) is under rapid development and will be available for customers within the next years. However the evidence that HAD is at least as safe as human driving has still not been produced. The challenge is to drive hundreds of millions of test kilometers without incidents to show that statistically HAD is significantly safer. One approach is to let a HAD function run in parallel with human drivers in customer cars to utilize a fraction of the billions of kilometers driven every year. To guarantee safety, the function under test (FUT) has access to sensors but its output is not executed, which results in an open loop problem. To overcome this shortcoming, the proposed method consists of four steps to close the loop for the FUT. First, sensor data from real driving scenarios is fused in a world model and enhanced by incorporating future time steps into original measurements.

This paper makes a contribution toward a more efficient chassis durability process for the development of passenger cars, in which the simulation of relevant load data is a time-consuming part. This is especially due to the full vehicle model complexity which is usually determined by the demands of rough road simulations. However, for the load calculation on a racetrack, time saving model approaches that are more simplified might be sufficient. Our investigation comprises two levels of vehicle model complexity: one with all chassis parts modeled in a multibody system environment and one characteristic curve based model in an internal simulation environment. Both approaches consider an original chassis control system as a Software-in-the-Loop model. By the evaluation of real-world experiments the main influence factors in terms of durability are demonstrated. With the help of those highly sensitive durability criteria the measurement and simulation results are then compared.

A cooperation of several research partners supported by the German Federal Ministry of Research and Education proposes a new active matrix LED light source. A multi pixel flip chip LED array is directly mounted to an active driver IC. A total of 1024 pixel can be individually addressed through a serial data bus. Several of these units are integrated in a prototype headlamp to enable advanced light distribution patterns in an evaluation vehicle.

A local Gaussian process regression approach is presented, which allows to model nonlinearities of internal combustion engines more accurate than global Gaussian process regression. By building smaller models, the prediction of local system behavior improves significantly. In order to predict a value, the algorithm chooses the nearest training points. The number of chosen training points depends on the intensity of estimated nonlinearity. After determining the training points, a model is built, the prediction performed and the model discarded. The approach is demonstrated with a benchmark system and air charge test bed measurements. The measurements are taken from a turbocharged SI gasoline engine with both variable inlet valve lift and variable inlet and exhaust valve opening angle. The results show how local Gaussian process regression outmatches global Gaussian process regression concerning model quality and nonlinearities in particular.

Air charge calibration of turbocharged SI gasoline engines with both variable inlet valve lift and variable inlet and exhaust valve opening angle has to be very accurate and needs a high number of measurements. In particular, the modeling of the transition area from unthrottled, inlet valve controlled resp. throttled mode to turbocharged mode, suffers from small number of measurements (e.g. when applying Design of Experiments (DoE)). This is due to the strong impact of residual gas respectively scavenging dominating locally in this area. In this article, a virtual residual gas sensor in order to enable black-box-modeling of the air charge is presented. The sensor is a multilayer perceptron artificial neural network. Amongst others, the physically calculated air mass is used as training data for the artificial neural network.

An accurate model to predict the formation of fogging and defogging which occurs for low windshield temperatures is helpful for designing the air-conditioning system in a car. Using a multiphase flow approach and additional user-defined functions within the commercial CFD-software STAR-CCM+, a model which is able to calculate the amount of water droplets on the windshield from condensation and which causes the fogging is set up. Different parameters like relative humidity, air temperature, mass flow rate and droplet distributions are considered. Because of the condition of the windshield's surface, the condensation occurs as tiny droplets with different sizes. The distribution of these very small droplets must be obtained to estimate numerically the heat transfer coefficient during the condensation process to predict the defogging time.

Due to the diversity of Heavy Duty Vehicles (HDV), the European CO2 and fuel consumption monitoring methodology for HDVs will be based on a combination of component testing and vehicle simulation. In this context, one of the key input parameters that need to be accurately defined for achieving a representative and accurate fuel consumption simulation is the vehicle's aerodynamic drag. A highly repeatable, accurate and sensitive measurement methodology was needed, in order to capture small differences in the aerodynamic characteristics of different vehicle bodies. A measurement methodology is proposed which is based on constant speed measurements on a test track, the use of torque measurement systems and wind speed measurement. In order to support the development and evaluation of the proposed approach, a series of experiments were conducted on 2 different trucks, a Daimler 40 ton truck with a semi-trailer and a DAF 18 ton rigid truck.

Due to their high energy density, power density, and durability, lithium-ion (Li-ion) batteries are rapidly becoming the most popular energy storage method for electric vehicles. Difficulty arises in accurately estimating the amount of left capacity in the battery during operation time, commonly known as battery state of charge (SOC). This paper presents a comparative study between six different Equivalent Circuit Li-ion battery models and two different state of charge (SOC) estimation strategies. The Battery models cover the state-of-the-art of Equivalent Circuit models discussed in literature. The Li-ion battery SOC is estimated using non-linear estimation strategies i.e. Extended Kalman filter (EKF) and the Smooth Variable Structure Filter (SVSF). The models and the state of charge estimation strategies are compared against simulation data obtained from AVL CRUISE software.

The worldwide automotive industry is currently preparing for a market introduction of hydrogen-fueled powertrains. These powertrains in fuel cell electric vehicles (FCEVs) offer many advantages: high efficiency, zero tailpipe emissions, reduced greenhouse gas footprint, and use of domestic and renewable energy sources. To realize these benefits, hydrogen vehicles must be competitive with conventional vehicles with regards to fueling time and vehicle range. A key to maximizing the vehicle's driving range is to ensure that the fueling process achieves a complete fill to the rated Compressed Hydrogen Storage System (CHSS) capacity. An optimal process will safely transfer the maximum amount of hydrogen to the vehicle in the shortest amount of time, while staying within the prescribed pressure, temperature, and density limits. The SAE J2601 light duty vehicle fueling standard has been developed to meet these performance objectives under all practical conditions.

The number of full electric and hybrid electric vehicles is rapidly growing [1][2][3]. The new technologies accompanying this trend are increasingly becoming a focal point of interest for rescue services. There is much uncertainty about the right techniques to free trapped occupants after an accident. The same applies to vehicle fires. Can car fires involving vehicles with a lithium ion traction battery be handled in the same way as conventional vehicle fires? Is water the right extinguishing agent? Is there a risk of explosion? There are many unanswered questions surrounding the topic of electric vehicle safety. The lack of information is a breeding ground for rumours, misinformation and superficial knowledge. Discussions on various internet platforms further this trend. Tests were conducted on three lithium ion traction batteries, which were fuel-fired until burning on their own. The batteries were then extinguished with water, a surfactant and a gelling agent.

NH₃/urea SCR is a very effective and widely used technology for the abatement of NOx from diesel exhaust. The SCR mechanism is well understood and the catalyst behavior can be predicted by mathematical models - as long as operation above the temperature limit for AdBlue® injection is considered. The behavior below this level is less understood. During the first seconds up to minutes after cold start, complete NOx abatement can be observed over an SCR catalyst in test bench experiments, together with a significant increase in temperature after the converter (ca. 100 K). In this work these effects have been investigated over a monolith Cu-zeolite SCR catalyst. Concentration step experiments varying NO, NO₂ and H₂O have been carried out in lab scale, starting from room temperature. Further, the interaction of C₃H₆ and CO with NOx over the SCR has been investigated.

In order to reduce fuel consumption in Fuel Cell Electric Vehicles, effective distribution of power demand between Fuel Cell and Battery is required. Energy management strategies can improve fuel economy by meeting power demand efficiently. This paper explains development of various energy management strategies for Fuel Cell Electric Vehicle with Lithium Ion Battery. Drive cycles used for optimization and analysis of the strategies are New European Drive cycles (NEDC), Japanese Drive cycles (JAP1015), City Drive cycles, Highway Drive cycles (FHDS) and Federal Urban Drive cycles (FUDS). All Fuel consumption and ageing calculations are done using backward model implemented in MATLAB/SIMULINK.

Nowadays, a continually growing system complexity due to the development of an increasing number of vehicle concepts in a steadily decreasing development time forces the engineering departments in the automotive industry to a deepened system understanding. The virtual design and validation of individual components from subsystems up to full vehicles becomes an even more significant role. As an answer to the challenge of reducing complete hardware prototypes, the virtual competence in NVH, among other methods, has been improved significantly in the last years. At first, the virtual design and validation of objectified phenomena in analogy to hardware tests via standardized test rigs, e.g. four poster test rig, have been conceived and validated with the so called MBS (Multi Body Systems).

Digital or virtual prototyping by means of a multibody simulation model (MBS) is a standard part of the automotive design process. A high-fidelity model is built and often correlated against test data to increase its accuracy. Once built the MBS model can then be used for high fidelity analysis in ride comfort, handling as well as durability. Next to the MBS model, current industry practice is to develop a reduced degree of freedom model for the design and validation of control or intelligent systems. The models used in the control system design are required to execute in hardware-in-the-loop (HIL) simulations where it is necessary to run real-time. The reason for the creation of the reduced degree of freedom models so far has been that the high-fidelity or off-line model does not execute fast enough to be used in an HIL simulation.

The BMW Group has introduced electric cars to the market with the MINI E already in 2009. The next step will be the launch of the BMW ActiveE in 2011, followed by the revolutionary Mega City Vehicle in 2013. The presentation will explain the BMW Group strategy for implementing sustainable mobility. A focus will be emobility, the use of carbon fiber and the holistic sustainability approach of BMW Group?s project i. Reference will be made to the research results of the MINI E projects in the US and in Europe. Presenter Andreas Klugescheid, BMW AG

The continuously integration of electrics and electronics (EE) in the last decades is one of the main key drivers for innovation and business success of the Automotive OEMs. This is also applicable for truck manufacturers. On the other side factors like the rising vehicle complexity, number of variants and the warranty costs for EE issues are increasing the pressure on the engineering teams responsible for the mechatronic systems. To address these issues one of the key activities in the European market (focus on Germany) during the last decade was to introduce industry-wide standards for the data transfer of wiring harness data between OEM and harness supplier. In this paper the benefits and technical background of using the standards KBL and KOMP formats within the MB-Trucks brand will be presented. Moreover the role of the Information Technology (IT) will be explained in detail.

The economic challenges and environmental imperatives facing the trucking and automobile industries today all point to a pressing need to improve fuel efficiency. Due to increasing volatility of fuel supplies, prices and a growing interest in reducing greenhouse gas emissions, fuel efficiency has taken on new urgency. In the long-haul trucking industry this is especially important given the fact that fuel accounts for a significant share of fleet operating costs. To this end Daimler and NAVTEQ have developed a system to improve fuel economy and reduce CO₂ emissions through the integration of digital map data into Advanced Driver Assistance Systems or ADAS. Digital road map attributes, especially road slope have been demonstrated to enable powertrain controls to anticipate road inclination changes and use this information to predictively enhance load management optimization versus the reactive approach afforded by current technology.

In this study the numerical simulation of the flow through an alternator inside an engine compartment of a passenger car is investigated. Specifically the interaction of the flow through the alternator with the flow through the engine compartment is explored in detail. The results are compared with a corresponding numerical simulation of an alternator in a surrounding of a test facility and with a numerical simulation of the flow through an engine compartment without taking into account the internal flow through the alternator. Finally the air temperature near the alternator and also the temperature of some components inside the alternator are compared with experimental values measured during a typical load case used for the thermal protection of the passenger car.