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Trajectory tracking control and vehicle 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. This paper presents an integrated, real-time capable framework for trajectory tracking control and vehicle 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 an extended Kalman filter (EKF) for multi-sensor 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 and physically feasible control inputs for underlying control. The EKF fuses
Fuchs, SörenNeubeck, JensWagner, Andreas
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, AbdelrahmanSchäfers, LukasSchmidt, FabianSchaub, JoschkaLee, Sung-YongAndert, Jakob
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, AndreasBadalian, KevinKoch, LucasLee, Sung-YongAndert, Jakob
The goal of reducing 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 energy efficiency. High importance is gained by the thermal management of the whole system. This research investigates a simulative approach to improve the thermal management and therefore the energy efficiency of an electric city bus. 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 behavior has been validated in previous
Schäfer, HenrikHellberg, TobiasMeywerk, Martin
This paper investigates the integration of Artificial Intelligence (AI) within radar-based perception for Advanced Driver Assistance Systems (ADAS) under safety considerations aligned with ISO 26262 [1] for functional safety and ISO 21448 (SOTIF) [2] for performance-related safety of the intended functionality. The study evaluates a hybrid architecture in which AI-based perception modules are combined with deterministic supervisory mechanisms to maintain safety compliance. A simulation-based case study using CARLA with radar sensor modeling is presented to compare a deterministic radar perception pipeline with an AI-enhanced approach under nominal and degraded environmental conditions. Performance is evaluated using precision, recall, and F1 score metrics. Results indicate improved recall and F1 score under adverse scenarios for the AI-based perception module, accompanied by a moderate increase in false positives. The paper discusses architectural constraints required to limit non
Jain, Yesha
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, JensGuarda, Filippo
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 LeonhardResch, Michael
Hydrogen-fuelled internal combustion engines are a potential carbon-free propulsion solution for high-power applications such as construction machinery and heavy-duty commercial vehicles. However, compared to conventional diesel engines, hydrogen engines exhibit limitations in transient operation and at full load, primarily due to the high reactivity of hydrogen. In spark-ignited hydrogen engines, combustion anomalies represent the main constraint during performance-oriented operation, particularly during transient phases that require mixture enrichment to meet dynamic torque demands. Water injection is investigated in this study as a means to mitigate these limitations. The paper describes the implementation of a port water injection system on a heavy-duty commercial hydrogen engine and evaluates its influence on engine performance with a focus on transient operating conditions. A combustion anomaly evaluation method developed in-house is applied to quantify the effect of water
Schneider, DavidChristoforetti, PaulKappacher, PeterKapeller, DavidSchutting, EberhardEichlseder, HelmutTrapp, Christian
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 the IPLC only once a decade, and because the event was originally begun in the 1980s, turnover of staff and volunteers with each of the organizations 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, as well as its role in highlighting the technical and programmatic progress of V/STOL over the ages.
Hirschberg, Michael J.
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 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 presents a novel approach where an established quality measure – First Time Quality (FTQ) – is used to quantify the impact of software updates in the final assembly. By comparing FTQ values from production
El Asad, AimanKöhler, KatjaHahn, MichaelReuss, Hans-Christian
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 regulations. Standards and guidance, including UNECE UN Regulation No. 157 and ISO/TS 5083, 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 stakeholders and specify their
Liu, XuanhengBairy, AkhilaPaudel, BijayAdolph, LaurenzHeck, MelanieHettich, LennardNägele, Ann-ThereseRudolf, KorbinianBause, KatharinaDüser, TobiasSchwammberger, Maike
The widespread adoption of electric vehicles is currently hindered by long charging durations and limited infrastructure. While fast-charging technologies address these issues, they impose significant thermal loads on high-voltage components. Within this architecture, the Battery Disconnect Unit plays a critical role as it monitors and controls the connection between the battery, powertrain, and charging system. However, the high currents required for fast-charging often drive these units' temperatures beyond safe operating limits, necessitating advanced thermal solutions that do not require extensive redesigns of the vehicle's electrical layout. To address this challenge, this study proposes a passive thermal management solution using Phase Change Material heat transfer devices to enhance the thermal robustness of the component. The methodology employs a dual approach involving initial experimental testing to pinpoint specific thermal hotspots under high-power conditions, followed by
Salameh, GeorgesGoumy, GuillaumeFrecinaux, AnthonyRatajczack, ChristellePalluel, MarlèneNoiseau, PascalLardeux, Sébastien
Knocking combustions in an Internal Combustion Engine (ICE) are engine damaging combustions, and reliable detection of each knocking event is very critical. Engines usually rely on piezo-electric knock sensors to monitor structure-borne noise, which outputs a complex, continuous time series signal. Typically, knock combustions have an additional noise component along with the regular combustion signal, but differentiation of knocking vs non knocking signal (signal to noise ratio) based on visual inspection of this signal alone is challenging and requires computationally intense signal processing such as Fast Fourier Transforms (FFT) or Wavelet transforms followed by manual calibration [1]. In this paper, we propose an alternative to replace traditional knock detection with more reliable time-domain alternative signal decomposition technique. Here we decompose the raw sensor signal into seasonality, trend, and residual, and use the residual component as it is seen to retain
Parulekar, Tushar A.Chilukuri, SandeepMahmood, Haneefa
As automation advances and occupants transition from active drivers to passive passengers, understanding how automated driving behavior is evaluated becomes increasingly important. While longitudinal and lateral vehicle dynamics are known to influence perceived comfort and safety, it remains unclear to what extent motion–perception relationships remain stable across urban traffic contexts. This study compares two real-world investigations of automated driving: a left-turn maneuver at a signalized intersection on a test track and a roundabout maneuver with a shuttle in public traffic. Both datasets include high-resolution vehicle dynamics and structured subjective ratings. A consistent objectification approach was applied to examine the transferability of motion–perception relationships across contexts. However, differences in vehicle platform, automation level, trajectory characteristics, and study design limit direct comparability and require cautious interpretation. Despite partially
Panzer, AnnaStrenge, EmmaIatropoulos, JannesHenze, Roman
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 approach systematically improves simulation models with wind tunnel tests. And CFD results guide efficient test matrix definition. Extended measurement methodologies include automated actuation of active
Jacob, Jan D.
The aim of this work is to develop a modular, real-time-capable digital twin of an electric powertrain based on machine learning (ML)-based model structures and a systematic, component-oriented architecture with a focus on efficiency estimation in test bench environments. 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 measured and derived test bench data, 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 consistent predictions of key variables such as torque, speed, power factors, and subsystem as well as overall drivetrain efficiency. The methodology enables a systematic comparison of black box, dark
Kopp, LennartProksch, DanielOckert, NielsKarthaus, CarstenKley, Markus
Current lithium-ion batteries should generally only be charged above 0 °C, as charging below this temperature can promote lithium plating and irreversible degradation. However, conventional pack-level heating elements increase system mass and design complexity. In addition, heat is transferred from outside into the cell, causing the temperature inside the cell to rise slowly. This study evaluates internal Joule heating of cylindrical Li-ion cells using a zero-mean square-wave current excitation and quantifies the associated aging impact. LG INR21700-M50L cells were tested at 0 °C, −10 °C, and −20 °C with three excitation frequencies (50 Hz, 1 Hz, 10 mHz) at 5 A amplitude. Each cycle consisted of 30 min heating followed by 60 min cooling; reference capacity-based state of health (SOH) was assessed every 50 cycles up to 400 cycles. A maximum surface temperature rise of 14.3 K was achieved, with larger temperature rise at lower ambient temperature and lower excitation frequency. Capacity
Raiber, StefanAllmendinger, FrankDegler, DavidParschau, Anke
The mitigation of Greenhouse Gas (GHG) emissions poses a major challenge for the transportation sector, driving the need for renewable fuels. Bioethanol represents a promising fuel for Spark-Ignition (SI) engines, combining a reduced life-cycle CO₂ impact with advantageous combustion properties. However, despite its proven performance under steady-state conditions, the widespread of fuels with high ethanol content is still constrained by significant difficulties during engine cold-start operation. This study aims to experimentally assess the effect of ethanol concentration on cold-start performance and warm-up transient behavior of a Naturally Aspirated (NA), Port Fuel Injected (PFI) SI engine. Warm-up tests were conducted at an operating condition of 2000 rpm engine speed and 20 Nm torque using three fuels with increasing ethanol content: commercial gasoline (E5), E30 and E60. In addition, dedicated startability tests were carried out for E60 and neat ethanol (E100) at different
Falbo, LuigiFalbo, BiagioPerrone, DiegoCastiglione, Teresa
This paper assesses the efficiency limits of light-duty vehicle propulsion systems based on reciprocating internal combustion engines (ICE) in the current state of the art and in the next five-year horizon, considering their combination with technologies such as electric turbocharging and hybridization, while excluding plug-in hybrid configurations so that fuel remains the primary onboard energy source. A systematic methodology is applied to evaluate the influence of key variables—heat transfer, air–fuel ratio, and compression ratio—on engine performance, integrating these variations into a simulation model to capture their interactions and effects. The resulting parametric study enables the generation of new engine maps that exploit synergies between parameters and enhance the prediction of engine behaviour across different operating conditions, forming the basis for assessing potential advancements in hybrid powertrain architectures. These maps are then used to define performance
Pla, BenjaminDolz, VicenteSerrano, Jose R.Gómez-Vilanova, AlejandroOliva, FerminCardenas, MariaAriztegui, Javier
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. The hardware prototype has been designed and built, but has not yet been tested, which is why the results are based on simulations. Unlike mechanical winding reconfiguration 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
Oestreicher, RaphaelSchneider, Jörgvon Ohlen, DavidFuchs, PatrickKulzer, André Casal
Hydrogen Internal Combustion Engines have emerged as an option for decarbonizing heavy-duty transportation. However, injecting high-pressure hydrogen gas into pressurized combustion chambers induces complex compressible flow phenomena, including choked flow and under-expanded supersonic jet structures, which challenge conventional modeling approaches 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 with a density-based solver, coupled with the k-ω SST turbulence model and species transport equations, simulations were performed at injection pressures of 6 MPa and 10 MPa into a 1 MPa ambient chamber. The simulation successfully captured fundamental compressible physics, including Mach disk formation and significant
Castilla Batun, Uriel IsaacAlzahrani, Fahad
The rapid adoption of electric vehicles (EVs) with longer driving range 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. A multi-agent adaptive control strategy is 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 possible 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 participating DC/DC converters at each time instance during the charging session. In addition to that
Salah, AliaAbu Mohareb, Omar
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, HaoranKallur-Krishnamoorthy, RajeshNeuhaus, ChristophAndert, Jakob
Recent advancements in Vision-Language Models have opened new possibilities for bridging the gap between Systems Engineering artifacts and automated code generation. Traditional Large Language Models 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 Vision-Language Model-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, MarcelKulzer, Andre CasalGuerocak, Erol
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, JohannesBause, KatharinaAlbers, Albert