Simulation is becoming increasingly important in the development, testing and approval process in many areas of engineering, ranging from finite element models to highly complex cyber-physical systems such as autonomous cars. Simulation must be accompanied by model verification, validation and uncertainty quantification (VV&UQ) activities to assess the inherent errors and uncertainties of each simulation model. However, the VV&UQ methods differ greatly between the application areas. In general, a major challenge is the aggregation of uncertainties from calibration and validation experiments to the actual model predictions under new, untested conditions. This is especially relevant due to high extrapolation uncertainties, if the experimental conditions differ strongly from the prediction conditions, or if the output quantities required for prediction cannot be measured during the experiments. In this paper, both the heterogeneous VV&UQ landscape and the challenge of aggregation will be addressed with a novel modular and unified framework to enable credible decision making based on simulation models. This paper contains a comprehensive survey of over 200 literature sources from many application areas and embeds them into the unified framework. In addition, this paper analyzes and compares the VV&UQ methods and the application areas in order to identify strengths and weaknesses and to derive further research directions. The framework thus combines a variety of VV&UQ methods, so that different engineering areas can benefit from new methods and combinations. Finally, this paper presents a procedure to select a suitable method from the framework for the desired application.

The modelling, simulation and analysis methods in automotive development are in the process of transformation. Increasing system complexity, variant diversity and efforts to improve efficiency lead to more complex simulations and depend on virtual vehicle development, testing and approval across a large application area. Consequently, the new key requirements of modern validation involve more precise reliability quantification of large application areas, achieved with reasonable effort of cost and time. This paper identifies that the neglection of uncertainties, low information in validation results, low extrapolation capability and the resulting small application area are preventing the state-of-the-art validation meeting those new requirements. In an extensive analysis examining more than twenty frameworks in detail, this paper shows that statistical methods exhibit a high potential to remedy these four key insufficiencies. The paper justifies comprehensively that consistent statistical validation is necessary, important and crucial for precise reliability quantification, which enables accurate model selection, knowledge building and decision making in modern automotive vehicle-dynamics simulations. An example is given explaining the basic principle and benefit of consistent statistical validation. Since automotive statistical methods are still at the beginning, the aim is to enable further investigation by showing their potential and providing deeper knowledge about this topic.

This paper presents the results of research into the application of an active battery switching system in the drivetrain of a prototype electric vehicle. The system's purpose is to increase the drivetrain's electrical efficiency especially in the partial-load range. Efficiency is increased using a switching unit that allows the vehicle's traction-battery circuitry to be changed during operation and thereby adjusting the intermediate circuit voltage stage to that actually required. The system consists of three electronic switches, which allow two different voltage stages in the intermediate circuit. This paper presents further results of roller dynamometer test series with the active battery switching system. The focus is thereby put on efficiency measurements in driving cycles.

The modelling and simulation process in the automotive domain is transforming. Increasing system complexity and variant diversity, especially in new electric powertrain systems, lead to complex, modular simulations that depend on virtual vehicle development, testing and approval. Consequently, the emerging key requirements for automotive validation involve a precise reliability quantification across a large application domain. Validation is unable to meet these requirements because its results provide little information, uncertainties are neglected, the model reliability cannot be easily extrapolated and the resulting application domain is small. In order to address these insufficiencies, this paper develops a statistical validation framework for dynamic systems with changing parameter configurations, thus enabling a flexible validation of complex total vehicle simulations including powertrain modelling. It uses non-deterministic models to consider input uncertainties, applies uncertainty learning to predict inherent model uncertainties and enables precise reliability quantification of arbitrary system parameter configurations to form a large application domain. The paper explains the framework with real-world data from a prototype electric vehicle on a dynamometer, validates it with additional tests and compares it to conventional validation methods. It is published as an open-source document. With the validation information from the framework and the knowledge deduced from the real-world problem, the paper solves its key requirements and offers recommendations on how to efficiently revise models with the framework’s validation results.

Although the importance of crash compatibility has been highlighted as an area for further improvement in terms of passive safety, the way in which partner-protection is assessed is still an unresolved problem. Some assessment approaches have been proposed that are based on the structural interaction between the test vehicle and the barrier. However, previous studies have shown that new concepts are needed due to the deficiencies in these approaches. An innovative approach is to use a moving deformable barrier as a collision partner, and assess the partner-protection using injury measurements on a virtual dummy on the moving barrier. While some acceleration-based criteria do exist that would allow us to assess injuries caused by high restraint loads, it is still unclear how to measure injuries caused by intrusions. Thus, we have developed an acceleration-based criterion for predicting significant intrusions into the compartment, which can be used in frontal impact tests with moving deformable barriers for assessment of the crash compatibility. The criterion developed is calibrated with the help of full-scale crash tests and validated with test data and simulation analysis. The results confirm the efficiency and reproducibility of the criterion developed for predicting significant intrusions in frontal impacts based on accelerometer measurements.