David Kraus

The ongoing transformation of the automotive industry is primarily driven by vehicle electrification and automation. In this context, system safety and reliability remain critical requirements in the development of modern vehicles and their subsystems. However, conventional diagnostic approaches exhibit significant limitations in addressing the increasing complexity of automated electric vehicles. Electrified powertrains introduce novel fault conditions, while highly automated driving systems reduce the possibility of human supervision of safety-critical components. This paper proposes a predictive diagnostic framework for battery systems in automated electric vehicles. The proposed methodology employs a two-stage diagnostic architecture based on Support Vector Machines (SVM) and one-dimensional Convolutional Neural Networks (1D CNNs) for real-time detection and prediction of electrical faults. The framework was experimentally validated on a fully automated electric vehicle prototype. The obtained results demonstrate high performance of proposed concept, achieving fault classification accuracies of 96.43% for the SVM model and 99.85% for the 1D CNN model. Furthermore, the proposed approach enables the prediction of specific battery-related faults up to 49 s prior to their actual occurrence.

Modern mobility faces increasing challenges, like carbon-free transportation and the need for flexible transportation solutions. The U-Shift II project addresses these problems through a modular electric vehicle architecture, a drive unit (Driveboard) and a vehicle body (Capsule). This separation offers high flexibility in different use cases. Current architecture paradigms, like AUTOSAR, face limitations in cost and development speed. To address these issues, this paper introduces a hybrid software architecture that integrates signal-oriented architecture (e.g., CAN bus) with service-oriented architecture for enhanced flexibility. A integral component of the hybrid architecture is the dynamic link system, which bridges these architectures by dynamically integrating Capsule-specific components into the Driveboard software stack during runtime. The performance of the developed systen and its functionality were evaluated using a hardware setup integrated into a Driveboard prototype. The dynamic link aystem was evaluated including latency measurements, as well as functionality tests. Additionally, a cloud-based reconfiguration process enhances the versatility of the Driveboard by allowing for over-the-air software updates and resource allocation. The results show a promising hybrid, reconfigurable E/E architecture that aims to enable a robust transition towards a pure service-oriented architecture required in future electric autonomous vehicles.

Numerous applications in our everyday life use artificial intelligence (AI) methods for speech and image recognition, as well as the recognition of human behavior. Especially the latter application represents an interesting research field for self-learning systems based on AI methods in the automotive domain. Human driving behavior is determined by routines that an AI system can learn, thereby predicting future actions. However, the methods and tools for validating these systems are insufficient and need to be adapted to the new types of self-learning algorithms. Our framework combines scenario-based testing and metamorphic testing to address the challenges of ensuring correctness and reliability in dynamic and probabilistic SLS. A proof of concept is performed using the example of a self-learning comfort function in a vehicle. The correct functionality is shown by comparing the generated test cases. The concept addresses the main challenges in testing self-learning systems, in particular, the generation of test inputs and the creation of a test oracle.