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In order to achieve high customer satisfaction and to avoid high warranty costs caused by component failures of the power-train of hybrid electric vehicles (HEV), car manufacturers have to optimize the dimensioning of these elements. Hence, it is obligatory for them to gain knowledge about the different types of vehicle usage being predominant all over the world. Therefore, in this paper we present a Data Mining system that employs a Random Forest (RF) based dissimilarity measure in the dimensionality reduction technique t-Distributed Stochastic Neighbor Embedding (t-SNE) to automatically identify and visualize different types of vehicle usage by applying these methods to aggregated logged on-board data, i.e., load spectrum data. This kind of data is calculated and recorded directly on the control units of the vehicles and consists of aggregated numerical data, like the histogram of the velocity signal or the traveled distance of a vehicle.

The reduction of fuel consumption as well as the rising demands of customers regarding a vehicle’s driving dynamic and the legislator’s continually rising demands are a current issue in vehicle development. Hybrid vehicles offer a possibility to rise to this challenge. Realistic driving cycles are of utmost importance for the calibration of a hybrid vehicle’s operational strategy. Deriving replacement speed cycles from extensive customer data sets seems to be an approach for solving these problems. The contribution at hand describes the derivation of replacement cycles by using stochastic models, probabilistic (weighted) drawings and a combinatorial optimisation. The novelty value is that the characteristic influences of all drivers are being considered in the generation due to the stochastic modelling.

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.

The increasing functionality and complexity of future electric/electronic architectures requires efficient methods and tools to support design decisions, which are taken in early development phases 6. For the past four years, a holistic approach for architecture development has been established at Mercedes-Benz Cars R&D department. At its core is a seamless design flow, including the conception, the analysis and the documentation for electric/electronic architectures. One of the actual challenges in the design of electric/electronic architectures concerns communication behavior and network topologies. The increasing data exchange between the ECUs creates high requirements for the networks. With the introduction of FlexRay 21 and Ethernet the automotive network architecture become a lot more heterogeneous. Especially gateways must fulfill many new requirements to handle the strict periodic schedule of FlexRay and the partly event-triggered communication on CAN-busses 23.