Technical Papers - SAE Mobilus

SAE Technical Papers are written and peer-reviewed by experts in the automotive, aerospace, and commercial vehicle industries and provide the latest advances in technical research and applied technical engineering information.

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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
Revisiting the Boeing YC-14 and Ball-Bartoe Jetwing USB Powered Lift Aircraft: Lessons Learned2026-01-0663To be published on 07/01/2026
This paper will revisit an area of STOL operations and Powered Lift aircraft design that has been limited in scope, and at best, very specialized when it comes to research, aircraft manufacture, and experimentation. Five aircraft have been flown successfully using the Upper Surface Blowing (USB) powered lift configuration: The Ball-Bartoe Jetwing, the Boeing YC-14, the Antonov An-72, the NASA / Boeing QSRA experimental aircraft, and the National Aerospace Laboratory Aska STOL Research aircraft. A background of the technology will be given, what connects them as far as USB technology, discuss the main lessons learned, and briefly dwell on other configurations that are close relatives of the USB configuration. An extensive review of the existing USB literature will be fundamental for the development of this paper. Although all the unclassified USB projects that have been constructed and tested will be discussed (successful and unsuccessful), the main thrust of the paper will be the Ball
Pinero, Erasmo
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
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
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
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
CFD Optimization of Multi-Pass Serpentine Flow Distributors for PEM Fuel Cells2026-01-0746To be published on 07/01/2026
Polymer electrolyte membrane (PEM) fuel cells represent one of the most promising solutions for decarbonizing powertrain technologies, as they can be employed as carbon-free electrical power source. However, performance degradation during their operating lifetime - caused among other factors by non-uniform reactant distribution and improper membrane humidification, which may lead to the formation of local hot spots - remains a significant challenge. Computational fluid dynamics (CFD) tools represent an effective approach for investigating the transport of oxygen and hydrogen within the cell and for optimizing the geometry of PEM fuel cell flow distributors. Thus, they can be exploited in order to improve the uniformity of current density and temperature distributions over the cell active area. In this work, a serpentine flow field PEM fuel cell is considered as test case. The distributor consists of a multi-pass serpentine flow-field composed of repeated sets of five parallel channels
Bulgarini, Margherita, Della Torre, Augusto, Montenegro, Gianluca, Baricci, Andrea, Mereu, Riccardo, De La Morena, Joaquin, Lalangui Gallegos, Jose A.
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
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 PhD, Tobias, Koehnlechner, Benjamin, Zimmer PhD, Markus
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 lng, Marwan, Sodre, Jose Ricardo
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
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
Efficient Testing of Vehicle-Integrated Thermal Management Systems on a Chassis Dynamometer Using an innovative energy efficient approach2026-01-0761To be published on 07/01/2026
Hybrid-electric (xHEV) and fuel cell electric vehicles (FCEVs) are expected to play a crucial role in the transition towards sustainable mobility in both the individual and commercial transportation sectors. As their market share increases, there is a need for advanced research to enhance overall vehicle efficiency – particularly through optimized energy management systems. For FCEVs, an optimal energy management strategy is essential to ensure safe and durable operation. For xHEVs, thermal management serves as a central lever for improving efficiency and controlling emissions, making it an integral part of the overall powertrain development process. Considering today’s regulatory landscape, these aspects must be addressed early in development. Consequently, a holistic methodological framework is required, enabling not only technical robustness but also economic benefits, such as reducing engineering effort through effective frontloading. This methodology is composed of integrated
Lavall, Philipp, Beidl, Christian, Fiore, Luis, Papavasileiou, Ioannis, Hohenberg, Günter, Kalski, Christian
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
Key Modeling Considerations for Reliable FEM-Based Demagnetization Evaluation of IPMSM2026-01-0741To be published on 07/01/2026
The reduction of heavy rare earth elements such as dysprosium and terbium, which are associated with high cost, geopolitical risk, and sustainability concerns, is a key objective in the electromagnetic design of IPMSM for traction applications. As these elements are the primary contributors to magnet intrinsic coercivity, their minimization increases the risk of irreversible demagnetization of the permanent magnets. In IPMSM designs with reduced heavy rare earth content, it is therefore necessary to operate close to the demagnetization limit of the permanent magnets and accurately identify them. Consequently, precise and reliable finite element based prediction of demagnetization robustness is essential for systematic and material efficient machine design. This paper investigates the key factors required for reliable assessment of demagnetization robustness in IPMSM using electromagnetic FEM. Unlike existing literature, which typically neglects the accuracy of magnet material data
Malner, Max, Naumoski, Hristian, Gretzinger, Stefan, Izquierdo, Patrick, Kulzer, Andre Casal
Estimating Driver Field of View in Active Safety Systems2026-01-0775To be published on 07/01/2026
Driver Monitoring Systems (DMS) are an important component of active safety systems, continuously evaluating the driver’s state and issuing real-time warnings. As defined by the SAE Levels of Automation, driving tasks are increasingly transferred from the driver to the vehicle at Level 2, however, the driver remains fully responsible for monitoring the driving environment. Current implementations, such as Driver Drowsiness and Attention Warning (DDAW), assess driver alertness, while Advanced Driver Distraction Warning (ADDW) ensures that the driver maintains visual focus. Nevertheless, these systems do not identify the specific objects or regions the driver is observing. This limitation motivates the presented research question: can an in-car monitoring system be integrated with external environment perception sensors to infer the driver’s Field of View (FoV)? This paper presents a system consisting of a driver-facing camera and a front-view camera. Facial features, including gaze
Ji, Dejie, Lausch, Hendryk, Flormann, Maximilian, Henze, Roman
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
NOx Formation Characteristics of Lean Hydrogen Combustion Based on 3D-CFD Virtual Engine Development2026-01-0732To be published on 07/01/2026
The transition toward climate-neutral transportation requires powertrain concepts that combine high efficiency with low pollutant emissions. In this context, hydrogen-fueled internal combustion engines represent a promising solution when hydrogen is produced from renewable energy sources. Owing to its specific molecular properties, hydrogen offers new possibilities for influencing and optimizing the combustion process and reducing the emission formation. This paper presents a numerical approach for characterizing the NOx formation in a single-cylinder research engine equipped with port fuel injection and a passive pre-chamber ignition system. The single-cylinder is operated over a wide range of engine loads and speeds, covering air-to-fuel ratios from λ = 1.5 to 2.5 and achieving up to 23 bar indicated mean effective pressure. The study focuses on the influence of engine load, mixture composition and ignition system configuration on NOx emissions. A dedicated look-up table approach in
Gal, Thomas, Vacca, Antonino, Chiodi, Marco, Schmelcher, Robin, Kulzer, Andre Casal
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
Biofuel Effect on Regeneration Capability and Soot Reactivity in SCRoF Systems2026-01-0750To be published on 07/01/2026
Biodiesel blends (B7, B20, B100) were evaluated in a Stage V-compliant SCR on Filter (SCRoF) system for heavy-duty applications to quantify soot reactivity and filter regeneration capability. Compared to conventional diesel (B7), B20 showed slightly faster regeneration performance under real-driving conditions, while B100 resulted in reduced particulate formation and higher soot reactivity, with more intense exothermic events requiring careful management. These differences are attributed to the distinct physical-chemical properties of the fuels (oxygen content, lower heating value) and their interaction with Diesel Oxidation Catalyst (DOC)/SCRoF. All tests were conducted on an engine dynamometer with a Cursor 9 FPT (Fiat Powertrain). Findings are discussed in the context of EU Stage V limits and practical control strategies for heavy-duty applications.
costa, simone
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
Numerical Analysis of Pre-Ignition Factors in Hydrogen Engines2026-01-0740To be published on 07/01/2026
Numerical analysis was conducted to investigate abnormal combustion, a major challenge in efforts to improve hydrogen engine efficiency. Focusing on two factors that induce abnormal combustion—surface reactions and lubricating oil—numerical analysis examined the potential for each to trigger abnormal combustion. Furthermore, since it was confirmed that the self-ignition prediction using a detailed chemical reaction mechanism deviates from experiments at temperatures around 800K, attempts were made to improve this issue. As a result, it was confirmed that surface reactions affect the chemical species ratio near the wall surface but have little effect on flame propagation. Regarding lubricating oil, two possibilities were investigated: the lubricating oil itself self-igniting and becoming an ignition source for the hydrogen mixture, and deposits generated from the lubricating oil generating heat and becoming an ignition source. We investigated two possibilities: the lubricating oil
Moriyoshi, Yasuo, Yamane, Taichi, Wang, Zhiyuan, Kuboyama, Tatsuya
The History of the International Powered Lift Conference Across Five Decades of V/STOL Progress2026-01-0662To be published on 07/01/2026
This year, SAE International hosts the 2026 edition of the International Powered Lift Conference (IPLC), which focuses on the latest developments in vertical and/or short takeoff and landing (V/STOL) aircraft research, concepts and programs. IPLC is a joint technical meeting, held approximately biennially, co-sponsored by the American Institute of Aeronautics & Astronautics (AIAA), the Royal Aeronautical Society (RAeS), SAE International and the Vertical Flight Society (VFS). Because each technical society hosts IPLC only once a decade, staff turnover at the societies creates a lack of knowledge and historical context of the event. This paper provides a formal record of the history and legacy of the IPLC, and its progress in highlighting the technical and programmatic progress of V/STOL over the ages.
Hirschberg, Michael J.
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
Improved Thermal Management for Operating a Battery Electric City Bus under Real Driving Conditions2026-01-0758To be published on 07/01/2026
The goal of reducing the global CO2 emissions requires actions especially for the transportation sector. To achieve the goal, electric traction motors are frequently implemented in passenger vehicles as well as in commercial vehicles like heavy-duty trucks or buses. Particularly electric city buses have the potential to reduce the local emissions in urban areas and provide local exhaust-emission-free mobility. While their number of registrations rises, research focusses on the improvement of the overall system in order to increase the energy efficiency. High importance is gained by the thermal management of the whole system. This research describes and investigates a simulative approach to improve the thermal management and therefore the energy efficiency of an electric bus system. The different thermal components of an electric city bus like drive system, battery system and heating, ventilation and air conditioning system (HVAC system) are modelled. Their thermal behaviour has been
Schäfer, Henrik, Hellberg, Tobias, Meywerk, Martin
A Model-Based, Iterative Framework for Developing Reconfigurable Validation Environments for PEMFC Systems in Mobile Applications2026-01-0756To be published on 07/01/2026
Fuel cell electric vehicles are described on cell, stack and system levels. In driving operation, multi-physics coupling across subsystems (reactant supply, humidification, thermal management, etc.) reshapes cell- and stack-level boundary conditions, impacting performance and degradation mechanisms. Isolated single-topic approaches on one specific level may have limited transferability, as cross-level interdependencies under changing operating conditions can negate improvements or shift limiting factors. This underscores the development of validation environments (VEs) that represent cross-level interactions and evolve as experimental evidence redirects research questions. Models such as the V-Model provide phase-oriented logic for developing VEs when validation scope, boundary conditions and acceptance criteria can be specified upfront and remain stable. However, in PEMFC VE development, experimental conclusions frequently reshape hypotheses, operating conditions and research topics
Knaier, Johannes, Bause, Katharina, Albers, Albert
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
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
Numerical Analysis of Directly Injected Hydrogen into a High-Pressure Chamber2026-01-0745To be published on 07/01/2026
Due to the historical use of fossil fuels, the transportation sector has been one of the major producers of greenhouse gas (GHG) emissions and harmful pollutants. Because of its high specific energy content and the potential for zero carbon-based emissions, Hydrogen Internal Combustion Engines have emerged as an option for decarbonizing heavy-duty transportation. However, injecting high-pressure gas into pressurized combustion chambers induces complex compressible flow phenomena, including choked flow and under-expanded supersonic jet structures, which challenge conventional modeling approaches. Accurate prediction of the resulting fuel-oxidizer mixing process is critical for optimizing engine performance and emissions. This study conducts a numerical investigation of transient hydrogen injection into a high-pressure argon environment, benchmarking a 2D axisymmetric Computational Fluid Dynamics (CFD) model against high-fidelity experimental optical measurements. Utilizing Ansys Fluent
Castilla Batun, Uriel Isaac, Alzahrani, Fahad
Methodological framework for the derivation of objective criteria for ride comfort assessment2026-01-0765To be published on 07/01/2026
In recent years, the automotive industry has faced increasing pressure to accelerate development cycles and reduce associated costs, while simultaneously meeting rising standards of ride comfort driven by the ongoing integration of autonomous driving functionalities. Consequently, it has become essential to ensure that ride comfort attains a high degree of maturity at the very early stages of the automotive development process. This necessitates the establishment of objective criteria that enable the reliable estimation of subjective ride comfort, utilizing simulation-based assessment methods. This study introduces a methodological framework designed to systematically translate the manufacturer specific subjective perception and assessment of ride comfort into objective descriptions using a dynamic driving simulator. The framework is conceived as a generic approach, enabling the comprehensive application to a wide spectrum of subjective ride comfort phenomena, while being specifically
Stroesser, Simon, Zwosta, Tobias, Angrick, Christian, Neubeck, Jens, Wagner, Andreas
Experimental study of warm-up and startability operations in a spark ignition engine fueled with ethanol gasoline blends2026-01-0738To be published on 07/01/2026
Thermal transients during warm-up phase influence fuel consumption and combustion stability. Moreover, during engine warm-up, tailpipe emissions are the highest since the catalytic converter has not reached the light-off temperature yet. Therefore, reducing warm-up time would be beneficial for both performance and emissions. In this work, an experimental investigation is carried out on a naturally aspirated four-cylinder spark ignition engine with a displacement of 1.2 liter, equipped with a port fuel injection system. The study compares conventional gasoline with an E30 (i.e. 30% ethanol in gasoline). Tests are performed starting from cold engine conditions and stopped when a fully warmed state is reached. The experimental campaign is conducted at a fixed operating condition of 2000 rpm and 20 Nm load, representative of typical warm-up operation. The analysis focuses on the warm-up duration, defined as the time required to reach steady thermal conditions, and on engine performance
Falbo, Luigi, Falbo, Biagio, Perrone, Diego, Castiglione, Teresa
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
Efficient Powertrain Control via Stochastic Signal Decomposition: Utilizing ARIMA Constituents for Real-Time knock detection in Internal combustion engines.2026-01-0737To be published on 07/01/2026
Knocking combustions in an Internal Combustion engine (ICE) are engine damaging combustions and hence reliable detection of every knocking event is very important. Engines typically rely on piezo-electric knock sensors to monitor structure-borne noise; which outputs a complex, continuous time series signal, thus differentiation of knocking vs knocking signal (signal to noise ratio) is difficult without computation intense signal processing such as Fast Fourier Transforms(FFT) or Wavelet transforms followed by manual calibration. In this research we propose an alternative to replace traditional knock detection with more reliable time-domain alternative using Autoregressive Integrated Moving Average (ARIMA) technique. Here we decompose the raw sensor signal into seasonality, trend and residue, and analyze the residue signal to differentiate knocking or non-knocking combustion, as the residual component is seen to retain abnormalities in the signal during knocking combustion. Further
Parulekar, Tushar A., Chilukuri, Sandeep, Mahmood, Haneefa
Optimization of a Halbach array for torque ripple reduction in a Yokeless And Segmented Armature axial flux machine2026-01-0754To be published on 07/01/2026
This paper presents the optimization of a Halbach magnet array applied to an axial flux machine (AFM) in a 12-pole, 18-slots yokeless and segmented armature (YASA) topology, evaluated in the torque–speed characteristics diagram. AFMs offer significant advantages in terms of compact design and high torque density compared to other permanent magnet machine topologies. However, noise, vibration, and harshness (NVH) performance is strongly influenced by cogging torque, electromagnetic torque ripple, and tooth forces. While Halbach magnet arrays are well established in high-performance radial flux machines, only limited research has investigated their influence in AFMs. A Halbach array concentrates magnetic flux on one side of the magnet arrangement, leading to increased air-gap flux density and a more sinusoidal magnetic field distribution. By using a Halbach array, the magnetic field distribution in the air gap becomes more sinusoidal, thereby reducing harmonic components. Previous
Müller, Karsten, Schulz, Fabian, Bremer, Martin, Burkhardt, Yves, De Gersem, Herbert
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
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 PhD, Carsten, Kley, Markus
Feasibility study on the mechanical adjustment of the vibration behaviour of a powertrain test rig for validating longitudinal vehicle shuffle2026-01-0735To be published on 07/01/2026
Vehicle manufacturers use Hardware-in-the-loop (HiL) approaches to validate overall vehicle characteristics, including those dependent on the powertrain, at an early stage of vehicle development. A powertrain test rig is a typical example. In the specific setup, the vehicle engine and side shafts are mechanically coupled to the load machines of the test rig, eliminating the physical influence of the rims, tires and vehicle body. Adapting a specimen to the test rig changes some characteristics. This affects the specimen's vibration behaviour, making it more challenging to validate comfort-related characteristics. A particular example is longitudinal vehicle shuffle; the powertrain's first torsional natural frequency causes it. The natural frequencies of the real vehicle and device under test differ significantly, so a road-matching approach is not directly feasible. To account not only for tire-road contact but also for the missing vehicle mass, some scientific studies propose a purely
Hübner, Carl, Prokop, Günther
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
Methodology for Developing a Learning-Based Energy Management Strategy for Hybrid Electric Vehicles via Soft Actor-Critic (SAC) Deep Reinforcement Learning Algorithm2026-01-0774To be published on 07/01/2026
The optimization of energy management strategies for hybrid electric vehicles is crucial for minimizing fuel and electrical energy consumption while maintaining the energetic stability of the electrical system. Conventional heuristic, rule-based approaches typically rely on classical optimization techniques and manual calibration by experienced engineers. These methods often suffer from simplified assumptions, sub-optimality, and are increasingly time-consuming given the growing complexity of modern hybrid powertrain architectures. This research proposes a novel methodology for the development of a learning energy management strategy based on deep reinforcement learning (DRL) to transition toward highly automated, data-based, and optimization-based development approaches. The methodology utilizes the Soft Actor-Critic (SAC) algorithm, an off-policy actor-critic method, to train an agent through experiences by interacting with an environment. The environment consists of a backward
Metzler, Sebastian, Winke, Florian, Jungen, Mario, Schmiedler, Stefan, Hofmann, Peter, Geringer, Bernhard
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
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
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
Internal Low-Temperature Heating of Lithium-Ion Cells Using Alternating Current: Thermal Performance and Aging Behavior2026-01-0753To 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
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
Representative Driving Cycles for Light Commercial Vehicles Based on Their Usage Profiles2026-01-0751To be published on 07/01/2026
This study describes a methodology for synthesizing representative driving cycles for light commercial vehicles. The focus is on taking the usage profiles of these vehicles into account in the driving cycle synthesis. In this methodology, representative routes are simulated using the example of light commercial vehicles in the craft sector. The results of these simulations are representative speed distributions and representative height fluctuations. These results are then used as target values for the actual driving cycle synthesis. Furthermore, measurement runs are carried out with a light commercial vehicle to create a database of real-world driving data. The measurement runs include different urban, rural, and motorway sections and cover a total distance of approximately 510 km. Routes with flatter and more challenging elevation profiles are driven. During the measurement runs, the speed signal and the altitude signal are measured. These signals are then processed and cut into
Heilmann, Oliver, Grabow, Andreas, Cortès, Sven, Schlick, Michael, Stoll, Tobias, Kulzer, André Casal
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 lng, Vignesh
Multi-Agent LLM Pipeline 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, Andre Casal, Guerocak, Erol
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
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