Browse Topic: Electrical, Electronics, and Avionics

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CONNECTORS, ELECTRICAL, CIRCULAR, MINIATURE, COMPOSITE, HIGH DENSITY, QUICK COUPLING, THREADED, ENVIRONMENT RESISTANT, REMOVABLE CRIMP CONTACTS, NUT, HEXAGON, CONNECTOR MOUNTINGAS29600/7 (Current)To be published on 08/01/2990
AE-8C1 Connectors Committee
CONNECTOR RECEPTACLE, BAYONET COUPLING, IN LINEAS95234/1A (Current)To be published on 06/01/2103
AE-8C1 Connectors Committee
Multi-Agent LLM workflow for Regulatory-Driven Requirement Generation in Automotive Software Development2026-01-0787To be published on 07/01/2026
The increasing regulatory complexity in automotive development places significant pressure on engineering teams to derive complete and correct requirements. This paper presents a multi-agent-based large language model (LLM) workflow designed to support requirement extraction from technical specifications and regulatory documents in compliance with automotive requirement guidelines. The approach structures the requirement derivation process across collaborating agents that interpret specification and regulatory text, generate candidate requirements for the early engineering activities, and cross-validate their outputs to improve consistency and traceability. To evaluate the applicability of the workflow in an industrial context, we applied it to the draft Euro 7 emissions regulation. The agents produced requirements for relevant functional domains, which were subsequently reviewed by domain experts at FEV. The evaluation focused on correctness, completeness, and coverage. Results
Abdalla, Abdelrahman, Schäfers, Lukas, Schmidt, Fabian, Schaub, Joschka, Lee, Sung-Yong, Andert, Jakob
Production-Ready Automated ECU Calibration using Residual Reinforcement Learning2026-01-0780To be published on 07/01/2026
Electronic Control Units (ECUs) have played a pivotal role in transforming motorcars of yore into the modern vehicles we see on our roads today. They actively regulate the actuation of individual components and thus determine the characteristics of the whole system. In this, the behavior of the control functions heavily depends on their calibration parameters which engineers traditionally design by hand. This is taking place in an environment of rising customer expectations and steadily shorter product development cycles. At the same time, legislative requirements are increasing while emission standards are getting stricter. Considering the number of vehicle variants on top of all that, the conventional method is losing its practical and financial viability. Prior work has already demonstrated that optimal control functions can be automatically developed with reinforcement learning (RL); since the resulting functions are represented by artificial neural networks, they lack
Kampmeier, Andreas, Badalian, Kevin, Koch, Lucas, Lee, Sung-Yong, Andert, Jakob
A graph classification approach to secure the compatibility of software and hardware configurations in distributed automotive systems2026-01-0783To be published on 07/01/2026
Software-defined, highly customizable vehicle architectures drastically increase the number of hardware–software constellations that must be validated, especially under safety and timing constraints. Traditional unit and integration testing, as well as current regression and combinatorial methods, cannot practically cover this configuration space or reliably capture emergent effects arising from complex interactions, such as bandwidth contention and non-linear latency behavior. This work presents a proof-of-concept for predictive, situational validation of self-describing hardware and software components within realistic automotive E/E architectures. Proposing a novel Machine Learning- (ML) based method for early systemic feasibility prediction of automotive configurations using Graph Neural Networks (GNNs). Specifically, the subclass Graph Isomorphism Networks (GINs) is applied to predict the compatibility of a randomly composed configuration of software and hardware components
Wizl, Jens, Guarda, Filippo
Multi-Agent LLM Architecture for Systems Engineering Automation2026-01-0777To be published on 07/01/2026
The increasing complexity of modern software-intensive systems, particularly in the automotive domain, demands new approaches to bridge the gap between high-level engineering specifications and executable, safety-compliant code. This need is amplified by the rapid transition toward software-defined vehicles (SDVs), where highly dynamic, updateable software functions significantly enlarge the scope and frequency of engineering activities and require scalable, transparent, and adaptive development processes. While recent advances in Large Language Models (LLMs) have demonstrated strong capabilities in automating tasks such as requirements analysis, code generation, and documentation, their deployment in safety-critical engineering workflows remains challenging due to the need for transparency, traceability, and controlled decision-making. This paper presents a modular multi-agent LLM pipeline that automates key steps of the systems engineering lifecycle - from requirement structuring and
Padubrin, Marcel, Kulzer, André Casal, Guerocak, Erol
Passive Thermal Management of Battery Disconnect Units for Fast-Charging Electric Vehicles: A Comparative Study of Phase Change Material Heat Transfer Configurations2026-01-0759To be published on 07/01/2026
Reducing charging time remains a critical challenge for the widespread adoption of electric vehicles (EVs). Limited charging infrastructure availability, combined with the long duration required for battery recharging, significantly affects user acceptance. As a result, fast-charging technologies are increasingly being introduced in both newly designed EV platforms and existing vehicles through retrofitting solutions. Despite their benefits, fast-charging systems impose substantial thermal loads on vehicle components, charging stations, and associated power electronics. Excessive heat generation can compromise performance, reliability, and safety, making effective thermal management a key design concern. Within the high-voltage architecture, the Battery Disconnect Unit (BDU) plays a vital role by monitoring and controlling the connection of the battery to the powertrain and charging system. Traditionally, BDUs are designed for conventional charging power levels; however, higher
Salameh, Georges, Goumy, Guillaume, Frecinaux, Anthony, Ratajczack, Christelle, Palluel, Marlène, Noiseau, Pascal, Lardeux, Sébastien
Design and Prototyping of a winding reconfiguration system for electric traction applications2026-01-0730To be published on 07/01/2026
Battery electric vehicles (BEVs) place high demands on electric drives across a wide operating range: high efficiency in customer-related driving scenarios and maximum performance in dynamic driving modes. A promising solution to this challenge is the dynamic reconfiguration of the electric machine winding configuration between series and parallel mode, enabling optimal electromagnetic properties of the drive for different operating points. This paper presents the design and prototyping of an electronic winding reconfiguration system for high-performance traction applications. Unlike mechanical switching concepts, which have long transition times and cannot switch under load, the proposed system enables fast and safe load transitions between the winding configurations. The study describes the topology and hardware of the switching unit, including the integration of power semiconductors, the required connection assemblies, cooling concept and the control of the power electronics. A
Oestreicher, Raphael, Schneider, Jörg, von Ohlen, David, Fuchs, Patrick, Kulzer, André Casal
Development of a digital twin of an electrical powertrain in a testbench environment for efficiency estimations2026-01-0744To be published on 07/01/2026
The aim of this work is to develop a modular digital twin of an electric powertrain based on novel machine learning (ML)-based model structures and a systematic, component-oriented architecture. This results in a modular, real-time-capable digital twin that precisely maps the efficiency characteristics of the entire drivetrain and can be used in development and testing processes. The further goal here is to enable virtual testing, which can be used for frontloading and thus both prevent errors and increase the speed of product development. Based on a comprehensive set of measurement data from a test bench, a multi-stage procedure is implemented that integrates data acquisition, physically informed feature selection, modeling at the component and subsystem level, and hybrid coupling strategies. The digital twin captures inverter, electric machine, and mechanical transmission stages and generates physically consistent predictions of key variables such as torque, speed, power factors, and
Kopp, Lennart, Proksch, Daniel, Ockert, Niels, Karthaus, Carsten, Kley, Markus
Stochastic Gradient Pulse Adaptation for Onboard Pulse Charging in Grid-Friendly DC Fast Charging of Battery Electric Vehicles2026-01-0734To be published on 07/01/2026
The increasing deployment of battery electric vehicles (BEVs), particularly in commercial and heavy-duty applications, is driving demand for higher DC fast-charging power while placing growing pressure on battery lifetime and local grid capacity. In today’s multi-battery-pack vehicles, a common practical solution is the use of multi-step or multi-constant CC-CV charging, where charging current is reduced in stages to accommodate pack-to-pack differences in state of charge, state of health, and ageing behavior. While effective from a safety standpoint, this approach often leads to conservative charging profiles, longer charging times, and inefficient use of available grid power, especially at higher charging rates. This paper presents an onboard pulse-charging concept for electric powertrains that addresses these limitations through a combination of vehicle-side power electronics and an adaptive control strategy referred to as Stochastic Gradient Pulse Adaptation (SGPA). Instead of
Prakashkumar, Balagopal, Mannar, Vignesh
Dynamic Wireless Charging for Electric Vehicles2026-01-0748To be published on 07/01/2026
This paper investigates the electromagnetic and circuit-level performance of an inductive power transfer (IPT) system for dynamic wireless charging of electric vehicles (EVs). Key design parameters affecting power transfer efficiency (PTE) are explored. A simplified Series-Series (SS) compensated IPT model using Double-D coil geometry and shielded ferrite backing was developed using MATLAB software. The framework has been built around variations of air gap, lateral misalignment, load resistance, and operating frequency. The results show that PTE is highly sensitive to spatial alignment, with a significant efficiency loss at air gaps greater than 10 cm and misalignments beyond 15 cm. Both parameters can cause a compounded nonlinear effect on the PTE, thus a 3D surface plot was used to confirm the simultaneous effects of both parameters. Load resistance analysis revealed an optimal range around 10–15 Ω, while frequency analysis showed a peak efficiency near 85 kHz, in alignment with SAE
Abdelrahman, Marwan, Sodre, Jose Ricardo
Cooperative Edge Extension for Automated Driving2026-01-0776To be published on 07/01/2026
E/E architectures are continuously evolving to meet newly emerging demands. In recent years, major drivers of this evolution have been the increasing software-defined nature of vehicles and the push toward automated driving. Key technologies such as edge-enhanced functions, vehicle-to-vehicle communication, and service-oriented architectures are therefore the focus of current research efforts. This paper presents a vision of how these technologies can be used to enable cooperation between vehicles, illustrated by using parked vehicles as edge nodes. Automated driving beyond SAE Level 4 remains an especially challenging task in dense urban environments. One major impediment is the presence of view obstructions caused by parked vehicles. These obstructions significantly increase the risk of missing or misinterpreting vulnerable road users such as pedestrians or cyclists. One possible approach to counteract this problem is the use of edge nodes that support object detection or even
Lüntzel, Vitus, Lukezic, Nikola, Kraus, David, Seidel, Luca, Beck, Maximilian, Schindewolf, Marc, Sax, Eric
Identifying and mitigating resonance effects in HV Powernets2026-01-0742To be published on 07/01/2026
HV Power nets of electric vehicles consists of various HV components such as batteries, inverters, auxiliaries and cables. During in-vehicle testing, multiple failures of an auxiliary inverter were observed, caused by resonance issues within the component filter. Initial investigations revealed that these resonances, absent during manufacturer testbench evaluations, were influenced by the vehicle power net and its impedance characteristics. To better understand the underlying causes and identify preventative measures, extensive simulations were performed. The results demonstrate a diminishing influence of the power net capacitance when significantly larger than the component capacitance. Also they highlight the critical impact of cable inductance on the component resonance frequency when comparable to the component’s inductance. A simplified electrical equivalent circuit was used to derive an equation predicting the resonance frequency as a function of the component’s capacitance
Schmiel, Fabian, Aurand, Tobias, Koehnlechner, Benjamin, Zimmer, Markus
Low-Temperature Internal Heating of Lithium-Ion Cells by Zero-Mean Square-Wave Current Excitation: Thermal Response and Aging Effects2026-01-0753To be published on 07/01/2026
Lithium-ion batteries commonly used today may only be charged at temperatures above 0 °C. However, the heating elements currently used to warm the battery packs make the battery heavier and more complicated in design. In addition, heat is transferred from outside into the cell, causing the temperature inside the cell to rise slowly. The aim is to heat the cells without additional components by applying alternating current and without significantly accelerating aging. To this end, a series of measurements were carried out at temperatures between 0 °C and -20 °C and at frequencies between 50 Hz and 10 mHz. A square wave signal and an amplitude of 5 A were kept constant in all measurement series. The cells were heated for 30 minutes and then cooled for 60 minutes. After every 50 heating cycles, the state of health (SOH) of the cells was determined, and a total of 400 heating cycles were carried out. Aging due to heating is independent of temperature and frequency, with the SOH ending at
Raiber, Stefan, Allmendinger, Frank, Degler, David, Parschau, Anke
Vision-Language Model Training for Code Generation from Requirements and Model-Based Artifacts2026-01-0770To be published on 07/01/2026
Recent advancements in Vision-Language Models (VLMs) have opened new possibilities for bridging the gap between Systems Engineering artifacts and automated code generation. Traditional Large Language Models (LLMs) are primarily trained on textual data and generic code repositories, which limits their ability to interpret graphical engineering artifacts such as Simulink block diagrams or system architecture models. In safety-critical domains like the automotive industry, these graphical models are central to development workflows and must remain closely aligned with textual requirements and implementation code to ensure traceability, compliance, and functional correctness. This paper proposes a VLM-centered multimodal training framework for code generation that integrates textual requirements, graphical model-based artifacts, and annotated source code into a unified learning process. By leveraging models which combine vision encoders with language backbones, the approach enables the
Padubrin, Marcel, Kulzer, Andre Casal, Guerocak, Erol
Enhancing Efficiency in Megawatt-Scale EV Charging via Adaptive Multi-Agent Control of DC/DC Converters2026-01-0769To be published on 07/01/2026
The rapid adoption of electric vehicles (EVs) demands high-power charging solutions that are efficient, scalable, and reliable. This work introduces a comprehensive simulation framework for megawatt-scale charging systems, focusing on the integration and control of multiple DC/DC converters. With the primary objective of maximizing overall system efficiency during megawatt-scale charging operations. The study addresses key challenges in multi-converter operation, including load distribution, synchronization, and system efficiency. A multi-agent adaptive control strategy was implemented to dynamically optimize operating points and allocate charging currents across converters in real time so that each participating converter operates at its optimal operating point where the maximum efficiency is delivered. This multi-agent adaptive control strategy allocates not only the individual optimal operating points of the multiple DC/DC converters but rather determines the optimal number of
Salah, Alia, Abu Mohareb, Omar
Stakeholder-Oriented Explainability Requirements for Advanced Driver Assistance Systems and Automated Driving Systems: A Structured Analysis2026-01-0788To be published on 07/01/2026
The development and validation of advanced driver-assistance systems (ADAS) and automated driving systems (ADS) are shifting from traditional linear V-model processes toward more iterative engineering cycles. Despite faster iteration, these safety-critical systems remain subject to stringent German and European regulations. Standards and guidance, including UNECE UN Regulation No. 157 and ISO/TR 4804, emphasize traceability, transparency, and explainability throughout development and validation. Nevertheless, as ADAS/ADS are developed and validated in faster, more iterative release cycles, additional stakeholders become involved and new explainability requirements emerge. These requirements vary between stakeholders and across development, validation, and post-market deployment phases, yet they are not systematically captured in the current state of research and practice. Therefore, to ensure that explainability supports rapid iteration, it is essential to identify relevant
Liu, Xuanheng, Bairy, Akhila, Paudel, Bijay, Adolph, Laurenz, Heck, Melanie, Hettich, Lennard, Nägele, Ann-Therese, Rudolf, Korbinian, Bause, Katharina, Düser, Tobias, Schwammberger, Maike
Harmonizing Generative AI and ISO 26262 for Safe and Intelligent ADAS Systems2026-01-0785To be published on 07/01/2026
The convergence of Generative AI (GenAI) and Functional Safety (FuSa) under ISO 26262 is reshaping Advanced Driver Assistance Systems (ADAS) by merging adaptive intelligence with stringent safety assurance. GenAI significantly improves perception, object classification, pedestrian intent prediction, and risk assessment, empowering vehicles to navigate highly dynamic, real-world environments. Furthermore, GenAI-driven synthetic data generation accelerates ADAS validation by enabling exhaustive testing of rare and safety-critical scenarios not covered by conventional datasets. However, the integration of GenAI into safety-critical automotive systems introduces major challenges. Its probabilistic nature, limited transparency, and vulnerability to adversarial inputs directly conflict with ISO 26262 principles that demand determinism, traceability, and fault isolation. This paper proposes a co-engineering framework that combines GenAI’s learning-based adaptability with deterministic safety
Jain, Yesha
Route-Based Sensor-Aware Speed Planning and Support for Safety Validation for Level-3 Automated Driving2026-01-0772To be published on 07/01/2026
Level-3 and higher automated driving systems require longitudinal speed strategies that remain consistent with both physical stopping feasibility and realistic sensing constraints. This paper presents a route-based, sensor-aware speed planning method that supports safety validation and explicitly couples longitudinal driving strategy with sensor field-of-view coverage. Based on a concrete route extracted from digital maps and enriched with fleet data, point-wise maximum speeds are computed considering road curvature, speed limits, and comfort constraints. From the resulting drivable speed profile, physically consistent stopping paths and their endpoints are calculated for each route position, accounting for friction limits, scenario-dependent deceleration capabilities, and system delays between perception and braking. The set of stopping paths is aggregated into a region of interest (ROI) representing the spatial area that must be reliably perceived to guarantee safe stopping. This ROI
Kohler, Paul Leonhard, Resch, Michael
Trajectory Tracking Control and State Estimation for Highly Automated Vehicles: A System Framework Implemented on UShift II2026-01-0779To be published on 07/01/2026
Trajectory tracking control and state estimation are core functionalities of highly automated vehicles and must operate reliably under strict real-time constraints as well as in the presence of model uncertainties and limited sensor availability. In particular, low-speed maneuvers pose additional challenges due to weakly excited vehicle dynamics and system delays. This paper presents an integrated, real-time capable framework for trajectory tracking control and state estimation, developed within the UShift II research project and implemented on the highly automated vehicle platform. The framework combines nonlinear model predictive control (NMPC) for trajectory tracking with extended Kalman filter based state estimation within a modular system architecture. The NMPC is based on a vehicle model designed for low-speed automated driving maneuvers and explicitly accounts for actuator constraints. Trajectories are tracked based on local planned reference trajectories while ensuring smooth
Fuchs, Sören, Neubeck, Jens, Wagner, Andreas
First Time Quality as an Indicator for Quantifying the Impact of Software Updates in Automotive Production2026-01-0786To be published on 07/01/2026
Vehicle software updates are released more frequently and in increasingly shorter cycles, which places growing pressure on vehicle quality and final assembly line stability. In production environments, software related issues do not remain limited to the digital domain, since errors introduced by software updates can interrupt flashing and commissioning processes, slow down assembly, and increase rework, thereby directly affecting production throughput. Electronic control units (ECUs) are particularly sensitive to software updates because they are flashed and commissioned during vehicle production under strict timing constraints, and changes to flashing sequences, memory structures, configuration parameters, or function definitions can negatively influence commissioning behavior. This paper shows that the impact of software updates on vehicle production can be quantified in a data-driven manner using First Time Quality (FTQ) as an impact measure. FTQ refers to the percentage of ECUs
El Asad, Aiman, Köhler, Katja, Hahn, Michael, Reuss, Hans-Christian
Cybersecurity Risk Assessment and Experimental Validation of Automotive Ultrasonic Sensors2026-01-0782To be published on 07/01/2026
Ultrasonic sensors are widely deployed in automotive driver assistance systems for near-range environment perception and provide safety-relevant inputs for functions such as parking assistance and automated parking. With increasing vehicle automation, the integrity and availability of ultrasonic sensor data become more critical, as compromised measurements may lead to incorrect vehicle decisions and hazardous behavior. While prior research has extensively studied physical attacks on ultrasonic sensors, a structured cybersecurity risk analysis in accordance with automotive cybersecurity standards, combined with experimental validation, is largely missing. In particular, the serial communication interface between ultrasonic sensors and control units has received limited attention despite its relevance as a potential attack surface. This paper presents a systematic security analysis of an automotive ultrasonic sensing system based on a demonstrator setup. The work applies a Threat
Gahm, Sebastian, Haller, Jonathan, Kriesten, Reiner
From Wind Tunnels to an AI-Supported Data-Driven Development Ecosystem How Software Transforms Aerodynamic Testing into a Strategic Asset2026-01-0767To be published on 07/01/2026
Despite advances in CFD, wind tunnel testing remains indispensable for aerodynamic validation, correlation, and homologation. Increasing configuration complexity, shortened development cycles, and stringent result robustness and documentation requirements demand a shift from isolated facilities to integrated, data-driven ecosystems within the overall development and company-wide test processes. We present a software-centric approach integrating wind tunnel operations into a strategic element of the Digital Thread. By orchestrating test planning, execution, data acquisition, and documentation within a unified framework, experimental data becomes reusable across projects and traceable for compliance and homologation. The interaction between CFD and physical testing is important. Such systematically improves simulation models with wind tunnel tests. And CFD results guide efficient test matrix definition. Extended measurement methodologies include automated actuation of active aerodynamic
Jacob, Jan D.
An Application-Oriented Process Model for Selecting Uncertainty Quantification Methods in Machine Learning2026-01-0773To be published on 07/01/2026
This paper presents an application-oriented process model for selecting uncertainty quantification methods in machine learning. The work is motivated by the increasing relevance of uncertainty quantification in product engineering applications, including the automotive domain, where reliable decision making under uncertainty is essential. In this context, machine learning is widely used in data mining to derive knowledge about systems and user behavior from data and to make this knowledge accessible to product developers. As these applications increasingly influence engineering decisions, the assessment and communication of uncertainty become critical requirements. Each data mining task is unique and no single algorithm is optimal for all tasks. This observation motivates the need for a structured process model to support the selection of appropriate uncertainty quantification methods in machine learning. The proposed process model builds on established problem-solving methods and
Holderied, Niklas, Hörtling, Stefan, Bause, Katharina, Düser, Tobias
Kolmogorov-Arnold Networks in Industrial Application of Truck Development2026-01-0760To be published on 07/01/2026
Kolmogorov-Arnold Networks (KANs) are a novel mathematical method to generate data-driven AI surrogate models. Compared to neural networks based on the MLP standard (Multi-Layer Perceptron), these offer further mathematical interpretability and thus allow improved validation of AI for industrial applications. In this paper, we use KANs to generate an AI vehicle model of a truck as a mathematically precise AI surrogate model. To do this, we combine the KAN approach with the approach of Neural Ordinary Differential Equations (Neural ODEs) to generate predictions for the time-series of the truck’s velocity. Furthermore, we compare the results of the AI based on KANs with the traditional approach using MLP in terms of model size, accuracy, and computational time in order to evaluate advantages and disadvantages of the KAN approach. The best AI-KAN vehicle model identified in this way is then embedded in a co-simulation via the Functional Mockup Interface standard, thus opening up a wide
Vaudrevange, Patrick K.S., Halverson, James, Ruehle, Fabian, Fabcic, Tomaz, Dingler, Christian, Piskala Dilipkumar, Santhoshkumar, Ibrahim, Muhammed, Herrnberger, Michael, Kasper, Johanna, Türk, Lars, Keckeisen, Michael
Integrated DC Boost Charger in Post-800V Architecture with Open-End Winding Machine – Magnetic-Domain Modelling and Torque Analysis2026-01-0739To be published on 07/01/2026
Next-generation powertrain architectures proposed within EU Horizon projects adopt operating voltages above 800 V, providing improvements in efficiency as well as reductions in copper usage and system weight. However, post-800 V vehicles must remain backward compatible with existing 400 V and 800 V charging infrastructure, which requires the installation of an additional onboard DC boost charging unit on the vehicle. This paper proposes an integrated DC boost charging solution that reutilizes the open-end winding electric machine and the traction inverter of the electric powertrain, enabling backward compatibility while further reducing system cost and weight. In charging mode, the electric machine is repurposed as a passive inductive component, imposing a strict requirement of stationary operation with zero torque generation, which fundamentally differs from the driving mode characterized by rotor rotation and electromagnetic torque production. Consequently, conventional electric
Wang, Haoran, Kallur-Krishnamoorthy, Rajesh, Neuhaus, Christoph, Andert, Jakob
Context-Dependent Assessment of Driving Dynamics and User Perception in Automated Driving2026-01-0757To be published on 07/01/2026
Automated driving systems are increasingly operating in complex urban environments, where driving behavior is shaped not only by technical requirements but also by how users perceive the vehicle’s motion. While many studies evaluate automated driving performance in isolated maneuvers or specific traffic situations, little empirical evidence exists on whether similar driving dynamics are perceived consistently across different urban contexts. This work investigates whether comparable motion patterns, such as acceleration profiles or steering behavior, are evaluated similarly or differently when occurring in roundabouts and intersections using data from real-world driving studies. The empirical basis consists of two user studies. In the first study, 24 participants experienced automated driving in roundabout scenarios in public traffic. In the second study, 20 participants experienced automated driving through intersections on a test track under defined boundary conditions. In both
Panzer, Anna, Strenge, Emma, Iatropoulos, Jannes, Henze, Roman
Impact of Network Latency on the Lateral Control in Automated Vehicle Marshalling2026-01-0781To be published on 07/01/2026
Automated Vehicle Marshalling (AVM) is the first functionally safe Level 4 automated driving system. It consists of the wireless control of unoccupied vehicles at low speed in well-defined environments, such as parking facilities or manufacturing plants. The driverless operation in an AVM system is achieved by transmitting control messages between connected vehicles and intelligent infrastructure. Similar to other wireless applications, network reliability poses a major challenge to ensuring safe automated driving. An AVM system must provide uninterrupted communication between the vehicle and the infrastructure at a stable frequency. However, wireless systems usually suffer from varying latencies and network disturbances. In this context, international organizations and automotive industry contributors have defined requirements specifying network performance, communication interfaces, and message formats for different AVM use cases. These requirements cover communication aspects
Mejri, Mohamed Amine, Münchhausen, Henrik, Flormann, Maximilian, Sturm, Axel, Henze, Roman
Methodology for Battery System Optimization and Evaluation Using Format Flexible Pouch Cells2026-01-0749To be published on 07/01/2026
Electrification using battery systems is one of the most relevant solutions regarding ecological challenges within multiple application cases such as mobility, tools or stationary power supply. Nonetheless besides recent achievements in some cases battery systems are still lacking behind operational requirements compared to conventional propulsion systems, therefore limiting the potential of electrification. Especially when purpose design possibilities are limited. Besides improving properties of cell materials, better usage of the available installation space offers potential for overall optimization of the battery system. The development of battery systems is complex, as it involves multiple system levels and domains, along with a wide range of design options and architectures. Battery cells that can be manufactured in flexible formats offer the potential to make more efficient use of available installation spaces. At the same time, these additional degrees of freedom increase design
Müller-Welt, Philip, Bause, Katharina, Spohn, Hannes, Albers, Albert
Multi-Scale Modelling of Localized Battery Degradation in Heavy-Duty Series Hybrids: From Electrochemical Impedance Spectroscopy to Powertrain and Battery Pack Performance2026-01-0747To be published on 07/01/2026
The operational efficiency of heavy-duty hybrid electric vehicles (HEVs) is intrinsically linked to the health and power capability of the battery pack. Traditional battery management systems often rely on bulk State-of-Health (SOH) metrics, failing to account for localized degradation and the resulting current imbalances within parallel-connected modules. Recent literature emphasizes that cell-to-cell variations and thermal gradients can lead to a 6% reduction in accessible energy and a 5.2% acceleration in degradation rates due to heterogeneous current distribution. This study addresses these gaps by presenting a multi-scale modelling framework that translates cell-level electrochemical degradation features into system-level powertrain optimization. Initially, a thorough experimental campaign was conducted on LFP cells, using accelerated cyclic and thermal aging at 25°C and 60°C. High-fidelity battery parameters were extracted from EIS data using fractional-order equivalent circuit
Safavi, Seyed Reza, Homayouni, Hooman, Shoa, Tina, Wang, Jason, McTaggart-Cowan, Gordon
A Comprehensive CFD‑Driven Approach to Battery Thermal Modeling and Cooling System Optimization2026-01-0752To be published on 07/01/2026
In recent years, the thermal management batteries for automotive applications has become a key factor to further improve their performance and ensure their efficiency, durability and safety. As a matter of the fact, an overheat of these devices can result in various issues, including degradation of insulation materials, decreased efficiency, shortened lifespan, and even failure and consequent thermal runaway. In this context, this work aims to improve the comprehension of heat dissipation in batteries and to model the cooling circuit through the integration of CFD, in order to provide guidelines for the optimization of the overall system. To this scope, a novel OpenFOAM solver is introduced, capable of computing electric quantities along with losses inside the battery cells, consequently assessing heat generation across various components. The combined thermodynamic and electric analysis of the battery, which predicts the heat sources and temperature distribution under the effective
Montenegro, Gianluca, Onorati, Angelo, Della Torre, Augusto, Tariq, Muhammad Hasnain, Bonetti, Elisa
Application of Deterministic and AI-Based Methods to Accelerate the Component-Level Development Process in Electric Vehicles2026-01-0766To be published on 07/01/2026
The global automotive landscape is undergoing a significant paradigm shift driven by the rapid development cycles of emerging competitors, leaving traditional European OEMs with a critical time-to-market gap. To bridge this gap, automotive engineering must pivot from traditional hardware-based processes toward agile, digital data-driven methodologies. This paper presents a comprehensive feasibility study on the implementation of data-centric approaches, using both Artificial Intelligence and deterministic methodologies, with focus on accelerating component development. It provides a detailed assessment of various AI-based and deterministic methodologies at specific stages of the product development process, targeting both: 1. Product design 2. Product development process The aim is to enhance component-level performance, quality, and functionality, while simultaneously optimizing workflows, increasing process and development efficiency, and facilitating knowledge reuse throughout the
Bode, Jana Pascal, Kröll, Sarah, Vohwinkel, Nikolaus, Paetzold-Byhain, Kristin
Generating Machine-Processable Specifications from Natural Language Using Large Language Models in Automotive Commissioning and Testing2026-01-0768To be published on 07/01/2026
The commissioning of electric and electronic components contributes significantly to adding value in vehicle production. Moreover, testing these components and digital vehicle functions secures this value and ensures high-quality products. The emergence of software-defined vehicles, however, leads to an increased scope and complexity of commissioning and testing processes as software functions depend on electric and electronic components that take on perception, execution, and processing tasks. Nevertheless, assembly planning must still meet the requirement of providing thorough, error-free, yet cost-effective processes. In this context, this paper tackles a common challenge: Software that is deployed in vehicle production to implement commissioning and testing processes is developed upon specifications that define prerequisites, procedures, and target end results in unstructured natural language. Therefore, extensive manual translation into executable code is needed, being susceptible
Köhler, Katja, El Asad, Aiman, Hahn, Michael, Reuss, Hans-Christian
Mind the Gap: Quantifying Behavioral Fidelity in CARLA using Naturalistic Drone Data2026-01-0771To be published on 07/01/2026
The rapid advancement of autonomous driving systems, particularly SAE Levels 3 and 4, requires rigorous validation strategies to ensure safety during handover and fallback procedures. Although virtual environments provide the necessary scalability, the simulation-reality gap poses a significant risk to the reliability of human-in-the-loop (HITL) assessments. This paper presents a comprehensive, empirical investigation into the transferability of human driving behavior from the Car-Learning-to-Act (CARLA) simulation environment to real-world traffic situations. Using the exiD dataset, which comprises high-precision, drone-recorded, naturalistic trajectories from German highways, we systematically reconstructed distinct traffic scenarios. These scenarios included lane changes, cut-in maneuvers, and curve navigation. Twenty-five participants navigated these scenarios in a driving simulator. The study employed a robust, multidimensional analytical framework to compare the simulated
Rebling, Patrick, Alphan, Metehan, Nenninger, Philipp
Who’s Riding? Machine Learning-Powered Rider Identification from E-Bike CAN Data2026-01-0778To be published on 07/01/2026
This study investigates the feasibility of identifying individual e-bike riders based on Controller Area Network (CAN) data using machine learning techniques. Datasets from 12 test riders performing various predefined cycling tasks on a dynamometer test bench are collected and used to ensure controlled and reproducible conditions. The recorded CAN data includes various sensor signals, such as power output, cadence, torque, and the used support mode. After pre-processing, two different methods of feature extraction are tested and compared, one based on snapshots of the data and one based on driving events such as braking and accelerating, measured by calculating statistics of the riding data over sliding windows. A range of machine learning models is employed to classify riders based on their distinct riding patterns using the extracted features. The evaluated models comprise k-Nearest Neighbors (KNN), Random Forest and Naive Bayes. The findings demonstrate the efficacy of machine
Simmann, Gabriel, Rauch, Yannick, Beißert, Florian, Kriesten, Reiner
An Analysis and Comparison of Free-Space Detection Approaches and Their Evaluation for Heavy-Duty Vehicles2026-01-0763To be published on 07/01/2026
The detection of free space plays a fundamental role in ensuring the safe and efficient operation of heavy-duty vehicles, particularly in environments where the available area to maneuver is severely constrained, such as construction zones, rest areas, or loading docks. An accurate estimation of free space is essential to prevent collisions, maintaining operational continuity and minimizing vehicle downtime. As observed from the reviewed literature, despite the large number of proposed free-space detection methods, there is no concise and established definition about how free space should be determined, represented, and inferred, nor agreement on the semantic classes to be considered. This heterogeneity complicates systematic comparison and benchmarking across approaches. This paper presents a structured survey and methodological analysis of recent free-space detection and semantic segmentation approaches across automotive LiDAR-, camera-, and radar-based perception systems, as well as
Martinez, Cristian, Peters, Steven
UMV Peoplemover: Towards a modular fully automated vehicle research platform2026-01-0784To be published on 07/01/2026
The UMV Peoplemover 2+2 is part of a modular vehicle family (Urban Modular Vehicle) that includes derivatives for passenger and cargo transport in urban environments. The platform supports automated movers as well as conventionally controlled vehicles with a human driver, ensuring high flexibility across applications. The modular platform enables the extensive use of common parts, allowing the efficient and cost-effective realization of multiple vehicle variants. The increased share of common parts also improves sustainability by reducing derivative-specific parts, material usage, and production complexity. A drivable demonstrator of the UMV Peoplemover 2+2 has already been realized. The vehicle is designed for the automated transport of up to four occupants in a 2+2 vis-à-vis seating arrangement and is targeted at demand-oriented shuttle services. While the drivable demonstrator validated the proof of concept, it lacked the core Level 4 hardware and software stack for automated
Pohl, Eric, Schmid, Fabian, Münster, Marco, Siefkes, Tjark, Stuebler, Tillmann, Mohammed, Shawan
Towards a Smarter E-Bike: Enhancing Energy Efficiency through Adaptive Speed Control2026-01-0736To be published on 07/01/2026
This paper presents the development of a speed controller for e-bikes, designed as part of an energy-adaptive assistance system. The controller provides riders with appropriate support along planned routes, based on the available battery capacity. The control concept is intended for integration into existing commercial e-bikes without requiring extensive modifications to the drive system. Therefore, the rider remains part of the control loop, adjusting the support mode according to instructions from the controller. The speed controller is implemented as a rule-based state machine, enabling comprehensible design and parameterization Since the rider must manually switch between support modes while riding, the control logic incorporates hysteresis and dead times to ensure stability, prevent oscillations and avoid frequent mode switching. The user interface is a smartphone application that issues visual and audio instructions for switching support modes. An initial, system-independent
Rauch, Yannick, Simmann, Gabriel, Schneider, Manuel, Goss, Christian, Kriesten, Reiner
Model-Based Sensitivity Study of Thermal Runaway Onset and Severity in Lithium-Ion Cells: Effects of Geometry and Thermal Boundary Conditions2026-01-0755To be published on 07/01/2026
Thermal runaway assessment in automotive battery development is still largely driven by single-point abuse tests, while design decisions often require a quantitative understanding of how cell geometry, material thresholds, and thermal boundary conditions influence TR onset and severity. This paper presents a systematic parameter study using a coupled electrochemical–thermal model (SPMe combined with a lumped thermal model) augmented by Arrhenius-type decomposition reactions to represent the key exothermic pathways leading to Thermal Runaway. Two trigger modes are considered: (i) a nail-induced internal short circuit and (ii) an external heat-trigger scenario. Four parameter groups are investigated: cell length scaling, separator decomposition temperature, external trigger power, and convective heat transfer coefficient to the environment. For the nail-triggered internal short circuit, larger cells exhibit lower peak temperatures but longer times to reach the maximum, highlighting a
Ceylan, Deniz, Kulzer, André Casal, Winterholler, Nina, Giek, Michael, Weinmann, Johannes
Interpretable Tire Force Modeling for Formula Student Vehicle Dynamics and Lap Time Applications2026-01-0762To be published on 07/01/2026
Accurate tire models are a key enabler for vehicle dynamics simulation, control design, and lap time optimization, particularly in the context of Formula Student race cars, where vehicle setups and tire characteristics differ significantly from production vehicles. State-of-the-art tire models, such as Pacejka’s Magic Formula, provide high accuracy but rely on predefined functional structures and a large number of parameters, which limits interpretability and adaptability to new tire compounds. This paper presents a data-driven approach for deriving compact and physically interpretable tire force models using symbolic regression. The proposed method employs an intelligent tree search to systematically explore the space of mathematical expressions and identify models that optimally balance prediction accuracy and structural simplicity. In contrast to black-box machine learning approaches, the resulting models consist of explicit mathematical expressions that enable physical
Anselment, Marcel, Borowski, Julian, Rudolph, Stephan
Best-in-Class NVH Optimisation for Electric Powertrains, Holistic Front-Loading Approaches2026-01-0689To be published on 06/20/2026
Achieving best-in-class Noise, Vibration, and Harshness (NVH) in electric powertrains demands a paradigm shift in development methodology. This paper presents a holistic, front-loaded approach to NVH optimisation, leveraging simulation models, machine learning, and target cascading to deliver superior results. By attacking NVH at the source, and integrating data-driven techniques early in the design cycle, significant reductions in development time and reliance on traditional testbed loops are realised. Machine learning algorithms are utilised to accelerate root cause analysis and refine simulation accuracy, while target cascading ensures alignment of system-level performance objectives down to subsystem contributions. Combined, these strategies enable rapid and robust NVH optimisation, setting the benchmark for next-generation electric powertrain development. several application and real life exmaple are shown.
Mehrgou, Mehdi, Garcia de Madinabeitia, Inigo, Graf, Bernhard, Gojo, Josef
An Experimental Setup for Vibroacoustic and Fluid-Borne Noise Characterization of Electric Refrigerant Compressors using R1234yf or R7442026-01-0724To be published on 06/20/2026
The increasing electrification of vehicles leads to a broader range of tasks for heating, ventila-tion and air conditioning systems and a shift in its priority assessment. Air conditioning systems in electric cars are no longer primarily responsible for cooling the passenger compartment, but also have to temperature-control the battery, especially during rapid charging, which repre-sents a high-load operating point. Additionally, the elimination of the combustion engine as a major noise source brings the NVH behaviour of the refrigerant compressor a higher priority in product selection. In order to investigate the vibration and acoustic behaviour, as well as fluid dynamic forces resulting from the cyclic compression principle of an electric refrigerant compressor, a test rig was previously developed, allowing compressors to be operated and measured isolated in an anechoic chamber under various defined operating points. This test rig has been expanded in two ways within the scope of
Beer, Gabriel, Saur, Lukas, Schwarz, Manuel, Zemsch, Stefan, Becker, Stefan
AI-Based Package Tray Design Method for Controlling Acoustic Modes in a Sedan2026-01-0716To be published on 06/20/2026
• In this study, we propose a methodology for predicting the acoustic modes and natural frequencies of a sedan using artificial intelligence and demonstrate the feasibility of controlling its acoustic characteristics by modifying the hole distribution of the package tray. In typical sedan structures, the cabin cavity and trunk cavity are acoustically coupled through holes in the package tray. The distribution of these holes significantly affects the natural acoustic modes and frequencies of the vehicle. However, once the exterior shape of the vehicle is finalized during the design phase, options for structural modifications to mitigate noise issues caused by these modes become extremely limited. To address this challenge efficiently, we develop a deep learning-based neural network model trained on data derived from a simplified acoustic analysis model of a sedan that includes a package tray. Finite element analysis is performed to generate acoustic modes and natural frequencies, which
Lee, Jin Woo, Cho, Jaeho, Nam, Younsic, Han, Yongha
Active Learning Supported Metadata Extraction from NVH Simulations2026-01-0720To be published on 06/20/2026
Simulations can only be searched, reused and leveraged as trainings data for machine learning methods if they possess suitable metadata. Manually obtaining this metadata is time-consuming and requires expert knowledge. Consequently, there is often a lack of metadata, and this prohibits the reutilisation of simulation data. Therefore, automated frame works for metadata extraction are essential to quickly, effortlessly and cost-efficiently obtain this information. At present, there are no existing toolboxes for Finite-Element-Simulation data and current Large Language Models are not usable. Nevertheless, machine learning methods are a promising solution for this task. However, classical supervised machine learning methods cannot be used due to the high effort for labelling. Therefore, classical rule-based extraction algorithms are an alternative for fundamental metadata extraction. For more enhanced tasks, where more context is necessary, they are not usable. Active Learning is the
Luegmair, Marinus, Gröttrup, Sören
A Self-Learning Framework for NVH CAE Analysis2026-01-0718To be published on 06/20/2026
The vibro-acoustic performance of a vehicle is a critical factor in customer perception of quality and comfort. Optimizing for Noise, Vibration, and Harshness (NVH), particularly road noise, presents a persistent challenge in the automotive development cycle. While modern FEM analysis is essential, the increasing complexity and sheer volume of CAE simulation data often overwhelm manual interpretation, potentially leading to prolonged development times or sacrifices in resulting comfort quality. To address these challenges, this paper introduces the application of CDH/ACE (Autonomous Computational Experiments), a framework that combines conventional CAE simulation workflows with machine learning in an iterative cyclic process. This creates a self-learning and self-correcting system that autonomously defines, performs, and learns from computational experiments. By building predictive models from simulation data, the framework intelligently guides the design exploration to achieve
Visser, Rene
NoiseSphere: An AI-Based Approach for Dynamic and Large-Scale Noise Mapping2026-01-0719To be published on 06/20/2026
Noise pollution is a major environmental and health challenge, yet its strong spatial and temporal variability makes comprehensive mapping highly complex. Current approaches under the European Noise Directive (END) provide only partial coverage and often lack temporal dynamics. The NoiseSphere project, fundet by the Austrian Research Promotion Agency FFG, develops an AI-based methodology for dynamic, large-scale noise prediction and mapping. A machine learning model is trained on heterogeneous data sources, including semantically enriched Sentinel-2 satellite imagery, OpenStreetMap road data, floating car data (traffic density and speed), and existing noise maps. The model is refined through integration of noise emission data and validated using targeted in-situ measurements. Two case studies, one located in an urban environment (Graz) and the other one in a rural region near Linz demonstrate the model’s applicability across contrasting settings. By combining remote sensing, traffic
Girstmair, Josef
Regularized Inverse Methods for Tire Noise Source Identification and Pass-by Noise Estimation in Electric Vehicles2026-01-0709To be published on 06/20/2026
Tire exterior noise has become increasingly critical in vehicle acoustics due to two key developments: updated pass-by noise regulations, which amplify the relative contribution of tire noise, and the rise of Battery Electric Vehicles (BEVs), which lack traditional powertrain noise. Design trends in BEVs—such as increased vehicle mass from battery packs and the widespread use of large-diameter, wide, low-profile tires—further intensify tire noise due to stiffer constructions and altered contact dynamics. A common method for predicting tire noise is the source-transfer-receiver model, where the tire is represented by a set of monopoles with volume velocity Q derived from near-field measurements. Acoustic propagation is modeled via p/Q transfer functions. Despite its simplifications, this approach is practical for vehicle development, enabling clear separation between source and transfer mechanisms and facilitating targeted noise control strategies. In previous work, we proposed a
Morin, Benjamin, Di Marco, Federico, Horak, Jan, Lafont, Thibault, Kim, Minkyu, Kang, Min Kyoo, Yoo, Ji Woo
NVH Aspects of DC Charging2026-01-0703To be published on 06/20/2026
The deployment of high-power DC charging infrastructure for electric vehicles introduces new challenges in managing noise, particularly in public environments where acoustic comfort and regulatory compliance are essential. Noise emissions from both charging stations and vehicles during charging are a concern for operators of charging parks regarding customer experience and sound immission regulations. AVL employed a structured three-step approach to develop a non-expert tool for assessing the noise radiation of charging stations and vehicles during the charging phase. In a first step, AVL characterized the noise emissions with sound power measurements. Secondly, the measurement results were transferred to the virtual domain. To achieve this, the vehicles and charging station were characterized in the simulation with multiple monopole sources supported by transfer function measurements. This simulation model was validated against the sound power measurement results. After successful
Gojo, Josef, Polanz, Markus, Graf, Bernhard, Langjahr, Paco, Mehrgou, Mehdi
Automating Component NVH Characterization: A Systematic Approach for Component Test Bench Testing2026-01-0704To be published on 06/20/2026
The automotive industry faces increasing pressure as electrification introduces new components and accelerated development cycles shorten time-to-market, leaving limited opportunity to resolve Noise, Vibration, and Harshness (NVH) issues. This environment demands virtual development tools that are fast, precise, and efficient, and that can be seamlessly interchanged between development sites, suppliers, and OEMs; capabilities that are critical for effective target setting. Component-based Transfer Path Analysis (C-TPA), standardized in ISO 20270:2019, enables independent component characterization and integration with virtual models to predict sound and vibration in new assemblies, referred to as Virtual Prototype Assemblies (VPA). However, conventional measurements are labor-intensive and therefore typically restricted to a small number of samples, overlooking production variability. This paper introduces a system for fully automated, ISO 20270 compliant C-TPA on non-rigid test
Sturm, Michael, Wienen, Kevin, Brandstetter, Markus, Sorber, Eric, Corbeels, Patrick, Verrecas, Bart, Gonçalves, Vinícius
Improving reliability of an automotive power electronic unit using Locally Resonant Metamaterials2026-01-0701To be published on 06/20/2026
The shift from internal combustion to electric vehicles necessitates addressing new challenges posed by novel components, materials, manufacturing processes and different operating regimes. This includes assessing new types of excitations and damages from a reliability perspective. This paper investigates a solution to enhance the reliability of the Printed Circuit Board (PCB) within a Power Electronic Unit (PEU). PCBs are critical electronic and mechanical supports for functional components (e.g. diodes, transistors, resistors) in numerous applications. Controlling critical vibration levels is crucial to prevent electronic component breakage and subsequent PEU functionality loss. Existing solutions often focus on improving sensor-PCB bonding, surface treatments, or stiffening components’ support structures. The proposed approach, instead, exploits Locally Resonant Metamaterials (LRMs) to reduce the vibrational load experienced by the PCB. LRMs are created by adding resonating elements
Tincani, Sara, Claeys, Claus, Deckers, Elke, Pandiya, Nimish, Dindorf, Christian
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