Rüdiger Berndt

A Computationally Inexpensive Battery Model for the Microscopic Simulation of Electric Vehicles.

The transition from classic combustion engine vehicles to electric vehicles is a major step to reduce worldwide CO2 emissions. In order to correctly and efficiently investigate impacts on the electric grid and the dimensioning of charging infrastructure, or to explore new technologies for a further increase of the driving range, realistic simulation models are required. In this paper, we present an accurate yet computationally inexpensive battery and kinematic model to be used in microscopic traffic simulation to help study the performance of thousands of electric vehicles. Our model also supports recuperation and range extender modules while only relying on the vehicles speed and a set of constant predefined parameters. It can therefore be easily coupled with current sophisticated traffic simulators. We show its applicability and correctness regarding the State of Charge and power flows using comprehensive real-life experiments.

Multi-valued Decision Diagrams for the Verification of Consistency in Automotive Product Data

Highly customizable products and mass customization - as increasing trends of the last years - are mainly responsible for an immense growth of complexity within the digital representations of knowledge of car manufacturers. We developed a method to detect and analyze inconsistencies by employing a Multi-Valued Decision Diagram (MDD) which issued to encode the set of all valid product configurations. On this basis, we stated a number of rules of consistency that are checked by a set-based verification scheme.

Product Line Fault Tree Analysis by Means of Multi-valued Decision Diagrams

The development of cyber-physical systems such as highly integrated, safety-relevant automotive functions is challenged by an increasing complexity resulting from both customizable products and numerous soft- and hardware variants. In order to reduce the time to market for scenarios like these, a systematic analysis of the dependencies between functions, as well as the functional and technical variance, is required (cf. ISO 26262). In this paper we introduce a new approach which allows for a compact representation and analysis of failure mechanisms of systems marked by numerous variants, also: Product Line Fault Tree (PLFTs), in a unified data structure based on Multi-valued Decision Diagram (MDDs). Therefore, instead of analyzing the Fault Tree (FT) of each variant separately, the proposed method enables one to analyze the FT in a single step. Summing up, this article introduces a systematic modeling concept to analyze fault propagation in variant-rich systems.

A Formal Model for Stateful and Variant-Rich Automotive Functions

The development of cyber-physical systems such as highly integrated, safety-relevant automotive functions is challenged by an increasing complexity resulting from customizable products, distributed development of specific artifacts, and numerous soft-and hardware variants. In order to reduce the time to market for such scenarios, a systematic analysis of the dependencies between functions, as well as the functional and technical variance, is required. In this paper we introduce a formal model which allows a later on analysis of a function with its different configurations, states, hardware, and software variants. The formal model allows in the next step to represent the functions architecture within a suitable data structure. Therefore, complete safety analyses can be done in one step—opposed to stepwise analyzing all configurations, software variants, and states. Summing up, this article introduces a formal model for automotive functions.

An energy demand model for the microscopic simulation of plug-in hybrid vehicles

More and more car manufactures are combining a normal combustion engine and an electric motor to satisfy the standards for CO2 emissions. In order to take advantage of this technology in the most efficient way, for example in companies car fleets, realistic simulation models are required. In this paper, we present an accurate yet computationally inexpensive energy demand model for the microscopic simulation of plug-in hybrid vehicles. Our model mostly consists of a combustion engine, an electric motor and, most importantly, a realistic engine management model. Furthermore, a pragmatic kinematic model as well as a model for recuperation of energy while breaking are provided. Modularly designed, our simulation model allows for simple replacement and adjustment of each module. As input values, our model only requires the vehicles speed and a set of constant predefined parameters. Consequently, this also enables the model to be easily connected with current sophisticated traffic simulators. We show the correctness of our model regarding the consumption of fuel and electric energy using comprehensive real-world experiments.

Poster: cOSMetic - towards reliable OSM to sumo network conversion

Realistic map data is the basis for meaningful simulation studies of Inter-Vehicle Communication (IVC) applications and protocols [1]. Synthetic scenarios such as isolated intersections or a perfectly laid-out grid do not feature the typical mix of low and high traffic density roads and can therefore not be used as a representative scenario for (sub)urban traffic [2].

Modeling IVC-based energy savings of Electric Vehicles

In this paper we present an easy-to-implement model that allows to determine the potential energy savings of regenerative braking (also: recuperation) for Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs) by exploiting Inter-Vehicle Communication (IVC). The proposed model has been designed to be applied within different braking scenarios: approaching traffic lights, stop-and-go, emergency brakes, or platooning, among others. Furthermore, the model has been implemented in MATLAB allowing not only to simulate numerous braking scenarios but also to extend it by adding IVC-related parameters like latency or packet-loss. The corresponding results can be carried over to large-scale scenarios by taking into account different equipment rates of both the number of EVs and the availability of Intelligent Transportation Systems (ITS).