The transportation sector's transition towards climate neutrality depends upon a substantial expansion of the charging infrastructure, particularly in densely populated urban areas where private charging options are limited. This study represents a contribution to the STRALI research project, which is concerned with the analysis of charging behavior and charging infrastructure in two districts of the city of Essen. To achieve this objective, a microscopic traffic flow simulation was developed in SUMO. This was accomplished by combining open data sources and real-world traffic data. A dedicated algorithm was developed to model user charging behavior, distinguishing between critical charging during the trip and convenience-oriented charging at the destination. The validation of the simulation results against real-world data demonstrates a high degree of agreement with regard to the duration of occupancy and the frequency of use of the charging points. The analysis identifies existing gaps in infrastructure coverage and provides a scientifically sound basis for future site optimization. The model also enables the simulation of future electric mobility ramp-up scenarios, facilitating proactive planning for public infrastructure needs and supporting the urban mobility transition at the local level.

Abstract Physics-informed neural networks are a promising method to yield surrogate models of flow fields. We present a metamodeling technique for variable geometries based on physics-informed neural networks. The method was applied to the DU99W350 airfoil at a Reynolds number of $$1\times 10^{5}$$ 1 × 10 5 . Using our technique, the angle of attack was introduced as an additional input parameter of the network and the model was trained to predict the Reynolds-averaged velocity and pressure fields around the airfoil for arbitrary angles of attack between 10.0 and 17.5 $$^{\circ }$$ ∘ . Furthermore, we present an effective method to generate the training points for the parameterized geometry. The model was trained with data from simulations for a limited set of angles of attack. Additionally, satisfaction of the a priori known boundary conditions as well as the Reynolds-averaged Navier–Stokes equations was attained. A sensitivity analysis concerning the Reynolds number, the amount and distribution of training data, and the turbulence model was conducted showing the superiority of the pseudo-Reynolds stress method and the demand for labeled training data in the domain. The trained network was capable of predicting the flow separation progressing with angle of attack on the suction surface and exhibited excellent agreement with numerically simulated results, even in the proximity of the wall for interpolations as well as extrapolations from the labeled data set. Our study demonstrates that physics-informed neural networks can be used to obtain accurate flow field surrogate models of variable geometries.

Abstract The software architecture in modern vehicles enables highly networked and innovative systems. At the same time, more and more regulations for type approval of a vehicle have to be taken into account in software development. To ensure consistent quality and compliance with regulations, it is therefore important to test effectively and efficiently. In this article, a modular testing approach is presented that assigns test activities based on special features of a software item. These activities are called modules in the approach. Each module is based on a legal regulation or standard. To ensure efficient and effective testing, the test modules with the highest required identification rate per test objective must always be performed. Carrying out several test modules with the same test objective is not expedient.

Data-driven models are obtained purely from data without complex theoretical modeling and without explicit model knowledge. This results in black box models whose traceability and reliability constitute a major challenge. This contribution addressed this issue and presents a novel hybrid estimation method, which enhances the reliability of artificial neural networks. Within this method, a simple model based on physical knowledge secures a model based on an artificial neural network. An unscented Kalman filter realizes the interaction of the two individual models. Thereby, a confidence level determines which model is trusted to a greater extent or even entirely. As part of the method for adjusting this confidence level, the input variables of the artificial neural network are related to the data used in training. The more often the artificial neural network has encountered a situation in the training process, the greater the confidence level will be. Finally, the confidence level is used to set the covariances of the unscented Kalman filter. In this contribution, the method is presented using the application of roll angle estimation for passenger cars. By using the hybrid method the reliability of the estimation is increased in comparison to the artificial neural network. For this purpose, sensor malfunctions as well as a sensor failure are simulated. These disturbances are compensated by the introduced method. In addition, the hybrid state estimator increases the estimation quality compared to the individual estimators. The proposed method can be applied to any problem, where knowledge-based models are available to secure data-driven models.

Generalized Learning Vector Quantization methods are a powerful and robust approach for classification tasks.They compare incoming samples with representative prototypes for each target class.While prototypes are physically interpretable, they do not account for changes in the environment.We propose a novel framework for the incorporation of context information into prototype generation.We can model dependencies in a modular way ranging from polynomials to neural networks.Evaluations on artificial and real-world datasets show an increase in performance and meaningful prototype adaptations.

In the context of automated driving, the control of vehicle dynamics is one of the important issues. In addition to conventional control strategies, algorithms with predictive working principles are particularly relevant here. Using mathematical models, the future system behavior can be predicted and thus optimally set. The present paper deals with an integrated non-linear model-based predictive vehicle dynamics control, taking into account the roll and pitch behavior of a vehicle. Due to the optimization, such model-based predictive control algorithms usually result in high computation efforts. With respect to this issue, a non-linear model-based predictive control algorithm regarding an integrated vehicle dynamics control is represented by a co-active neuro-fuzzy inference system. The validation of the two vehicle dynamics control algorithms is done with respect to the control quality and the computation effort.

The use of multivariate time series generation in industrial settings such as the automotive industry continues to increase. The complexity of data analysis requirements in such industries has led to an urgent need to develop effective methods for extracting structural information from data based o n the clustering of system behavior time series. Because there are complex interactions between vehicle data variables, the time series clustering of single variables can lead to insufficient results. To the best of our knowledge, only univariate dynamic time warping (DTW) approaches have thus far been applied in an automotive context. To close this research gap, this paper presents a review of generic approaches in multivariate dynamic time warping (MDTW) to determine the most promising approaches for use in the automotive domain. Four approaches are found to be particularly useful for tasks such as the objective assessment of subjective driving perceptions.

Tendon-based parallel manipulators with n d.o.f. use at least m = n + 1 tendons to guide the end effector along a given trajectory. Since tendons can only transmit limited and tractive forces, it is essential to apply a valid tendon force distribution. Due to safety and precision requirements, a combined position and force control is needed where the force calculation delivers the desired tendon force distributions. The high dynamic potential of the robot demands for realtime capable algorithms. To avoid steps in the motor torques the calculated tension force distributions also have to be continuous along the trajectory. In this paper, a new algorithm for tendon force distribution calculations capable for usage on a realtime system is proposed and its continuity is proven.