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.

Increasing dynamic performance and the general level of automation of commercial vehicles emphasize the issue of safety. Modern braking systems focus on sustaining vehicle stability, often degrading the brake performance. The major downgrades of the braking performance are nearly impossible to model using a classical mathematical approach, making them not feasible to use in real braking system controllers. In this paper, the use of combined Neural Networks and Fuzzy logic for the control of the braking system of a commercial vehicle while maximizing performance and sustaining stability is proposed. The control system comprises adhesion estimation, an inverse brake model, and a fuzzy logic controller to keep the system giving optimal control signals in various brake conditions while sustaining vehicle stability and steerability. The results based on a semi-trailer system reveal the success of the proposed AI-based braking system algorithm while braking under varying conditions. Graphical abstract

Vehicle diagnostic systems are crucial for the normal operation of vehicles and their propulsion-related systems. Undetected unusual behaviour of such systems makes the vehicle diagnostic system unreliable. Current diagnostic systems, such as On-Board Diagnostics (OBD), are limited to monitoring only specific systems in order to make fault decision. However, various anomalies, including drastic performance drops, vehicle tampering, and changes in the driving environment, often go undetected during OBD system testing, validation, and inspection. This research presents a novel explainable OBD anomaly detection pipeline that is able to detect anomalies based only on OBD data snapshots during processes of OBD validation and inspection. The novel approach is implemented using combined dimension reduction and data clustering methodologies. First, the data is transformed into a latent space using t-distributed Stochastic Neighbor Embedding (t-SNE), where the general structure of the anomaly in the data can be exploited. Subsequently, clustering using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is applied to group similar normal data and identify anomalous patterns. The novelty of the solution is further extended with a feedback loop that suggests the root cause of OBD signals for individual anomalies using explainable AI (XAI) methodology, in this case Shapley additive explanations (SHAP). The proposed concept was verified and evaluated using real OBD snapshots with synthetically generated anomalies in two scenarios with different engine status, engine off and on, with an achieved accuracy of 92.89% and 96.45% for anomaly detection, respectively. The majority of anomaly causes in the form of specific OBD signals from propulsion- and emission-related systems were successfully explained using SHAP.

The operational reliability of electric buses depends heavily on the thermal stability and longevity of their battery packs. In urban public transit, highly dynamic driving cycles characterized by severe current surges during regenerative braking and prolonged constant-current charging phases induce massive localized thermal stress. Traditional battery management systems often fail to dynamically balance energy recovery with the physical constraints of cell matrices, leading to accelerated degradation and potential safety hazards. This paper proposes an integrated cyber–physical framework that merges intelligent control with advanced prognostic monitoring and predictive thermal management. The core of the control layer is a deep Q-network (DQN) agent designed for real-time smart charging and dynamic regenerative braking current limitation (smart derating). This control loop is tightly integrated with a long short-term memory (LSTM) network-based digital twin (DT) operating at the edge level to mirror complex electro-thermal dynamics and track the battery state of health (SOH). To ensure transparency and proactive thermal control, a nonlinear model predictive control (NMPC) strategy is implemented, utilizing SHapley Additive exPlanations (SHAP) to map core heat generation and dynamically adjust for internal thermal lag. The predictive NMPC-xAI loop maintained cell temperatures within a tight, optimal envelope, preventing delayed under-cooling and reducing peak temperature fluctuations.

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.