Browse Topic: Electrical, Electronics, and Avionics

Items (57,641)

Conceptual Design and Aerodynamic Configuration of the Relithia Nova: A 2000kg Hybrid-Electric STOL Aircraft

2026-01-0660To be published on 07/01/2026

This paper presents the conceptual design of the Relithia Nova, a hybrid-electric Short Take-Off and Landing (STOL) aircraft engineered to provide essential connectivity to remote regions with limited aviation infrastructure, such as the South Pacific and Caribbean. Designed with a Maximum Take-Off Mass (MTOM) of 2,000 kg, the aircraft accommodates six occupants, including the pilot, and is optimized for operations on short, unprepared airstrips up to 2,000 ft elevation. The Relithia Nova employs a series-hybrid propulsion architecture featuring two wing-mounted turboshaft engines coupled with electric generators that drive propulsive rotors via a high-capacity lithium-ion energy storage system. This configuration ensures sustained power for high-lift generation while reducing emissions. Aerodynamically, the aircraft utilizes a high-aspect-ratio wing (AR=8.67) based on the NACA 23012 airfoil, chosen for its favorable lift-to-drag characteristics in subsonic flow (Mach 0.23 cruise

Chikwanha, Hendrick Tafadzwa

Active Learning Supported Metadata Extraction from NVH Simulations

2026-01-0720To 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 PhD, Sören

AI-Based Package Tray Design Method for Controlling Acoustic Modes in a Sedan

2026-01-0716To 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

A Self-Learning Framework for NVH CAE Analysis

2026-01-0718To 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

Best-in-Class NVH Optimisation for Electric Powertrains, Holistic Front-Loading Approaches

2026-01-0689To 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

NoiseSphere: An AI-Based Approach for Dynamic and Large-Scale Noise Mapping

2026-01-0719To 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

An Experimental Setup for Vibroacoustic and Fluid-Borne Noise Characterization of Electric Refrigerant Compressors using R1234yf or R744

2026-01-0724To 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

Dynamic characterisation and modelling of Electric Cables under representative load conditions

2026-01-0676To be published on 06/20/2026

Electric high voltage (HV) cables are commonly used in automotive applications and very prominently in electrified vehicles. These cables are potential flanking transmission paths for structure-borne sound in a broad frequency range and must therefore be included in the NVH design process. Electrical high voltage cables exhibit non-linear mechanical characteristics, when exposed to significant bending the internal geometry of the cable will change and a curvature dependent bending stiffness will result. The electrical cables envisaged in the current publication feature a helically wound stranded aluminium wire core. This conductive core is covered by, in sequence, a silicone rubber insulation, a braided aluminium wire shield with aluminium foil to minimize electromagnetic interference and a silicone rubber outer sheath. An extensive measurement campaign was carried out to dynamically characterize cable specimen of different lengths and cross sections in terms of multi-degree of freedom

Nijman, Eugene, Buchegger, Blasius, Böhler, Elmar, Zeller, Bernhard, Rejlek, Jan, Faksa, Lukáš, Lukavsky, David

Regularized Inverse Methods for Tire Noise Source Identification and Pass-by Noise Estimation in Electric Vehicles

2026-01-0709To 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 Charging

2026-01-0703To 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

Development of a Convolutional Autoencoder-Based Unsupervised Classification and Visualization Model for Extracting Feature Frequency in C-EPS Fault Diagnosis

2026-01-0717To be published on 06/20/2026

In the automotive industry, deep learning models such as convolutional neural networks, autoencoders, and transformers have been actively studied to improve defect detection rates in End-of-Line (EOL) processes. However, applying these approaches to the field of Noise, Vibration, and Harshness (NVH) faces several practical challenges. ① Difficulty in securing sufficient training data and risk of overfitting due to longer evaluation times compared to other data types. ② Label imbalance caused by the relatively small amount of defective data. ③ Reduced labeling accuracy due to human error. ④ Decreased model robustness under domain shifts such as jig variations, evaluation environments, signal-to-noise ratio (SNR), and the occurrence of new abnormal noises. ⑤ Constraints in application due to compatibility issues with existing evaluation equipment. To address these issues, this study proposes an unsupervised learning-based method for classifying operating noise data of Column-type

Park, Jun-Seo, Jo, Hyeon-Choel, Cho, In-Je, Seo, Jae-Yong, Yoo, Seong-Sik

Experimental Validation of Frame-Dependent Gyroscopic Frequency Splitting in a Freely Rotating Tire Using Wireless MEMS Accelerometers and Scanning Laser Doppler Vibrometer

2026-01-0700To be published on 06/20/2026

Gyroscopic effects split circumferential traveling-wave resonances of rotating structures into forward and backward branches. This work first analyzes the splitting in the co-rotating (Lagrangian) frame to provide physical intuition for the evolution of the two branches with spin speed. A transformation to the inertial (Eulerian) frame is then derived, showing that the observed frequencies are shifted by a kinematic Doppler-like term that acts with opposite sign on the forward and backward waves, leading to different Campbell-diagram slopes depending on the observation frame. The resulting framework is validated experimentally on a freely rotating, unloaded tire using two complementary sensing modalities: wireless on-tire accelerometers (co-rotating view) and a scanning laser Doppler vibrometer (inertial view). A frequency-domain SVD-based identification (FDD/ODS-SVD) is used to extract poles and deformation patterns over a range of spin speeds, enabling Campbell diagrams in both

del Fresno Zarza, Javier, Naets, Frank

Automating Component NVH Characterization: A Systematic Approach for Component Test Bench Testing

2026-01-0704To 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

Single microphone sound source localisation in partially known environments using sequential spatial sampling

2026-01-0681To be published on 06/20/2026

Sound source localization is a fundamental capability for environmental awareness in a wide range of applications, including automotive or automated vehicles. Microphone-array-based signal processing techniques are widely used for this task. However, achieving sufficient localization accuracy often requires a large number of microphones and wide array apertures, which can be incompatible with limited installation space and cost constraints. Moreover, standard array-processing methods often rely on free-field transfer functions. In environments with reflections, diffraction, and scattering, particularly under non-line-of-sight conditions, this mismatch can degrade both accuracy and interpretability. This paper presents a methodology for sound source localization in partially known environments that addresses these challenges by combining two ideas. First, the method reduces sensor requirements by exploiting sequential pressure measurements acquired at different spatial locations along a

Pirro, Giovanni Battista, Nijman, Eugene, Deckers, Elke, Denayer, Hervé

Towards Realistic Stick-Slip Testing: Efficiently Mapping Material Behaviour across a wide Range of Loading Conditions

2026-01-0713To be published on 06/20/2026

To minimise noise caused by interior components rubbing against each other, automotive materials are usually tested in advance with the established stick-slip method according to VDA standard 230-206. This procedure is widely used for soft materials, upholstery and plastics. However, it is limited to constant climatic and selected loading conditions. Contrary, in real application, changing climates and dynamic excitations can nevertheless trigger noise issues even in materials rated as suitable in the prior tests. To address this gap, a new test method has been developed that evaluates the stick-slip behaviour of material combinations for a wide range of loading and climatic conditions. Conducted in a climate chamber with a standard stick-slip bench, the procedure applies sinusoidal excitations, dynamic climatic shifts and advanced data analysis. In addition to the usual results the new method also evaluates realistic scenarios such as starting a vehicle in different seasons or sudden

Fritz, Susanne, Strangfeld, Martin

Vibro-Acoustic simulation for vehicle audio design

2026-01-0670To be published on 06/20/2026

Acoustic user interfaces and audio experiences are among the leading comfort factors in new vehicle interior designs. OEMs are more and more focusing on loudspeaker design and positioning, to provide the most immersive experience to the customers. The industrial target is to be able to predict the performance of an audio system in early design phases. This paper presents a methodology to model source-receiver transfer functions in a full-scale vehicle cabin and investigates the installation effect of the speakers in a real car door. A hybrid methodology is developed to be able to model the loudspeaker in different environment.

Zerrad, Mehdi, Errico, Fabrizio, Mordillat, Philippe

Improving reliability of an automotive power electronic unit using Locally Resonant Metamaterials

2026-01-0701To 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

Development of a Diesel Combustion System for Next‑Generation Heavy‑Duty Engines Using a Synergistic Analysis‑and‑Testing Approach

2026-37-0004To be published on 06/09/2026

Regulators and policymakers have introduced increasingly stringent limits on tailpipe CO₂ and pollutant emissions to accelerate the decarbonization of heavy-duty vehicle applications. The development of innovative propulsion technologies — such as advanced combustion systems, low-friction reciprocating components, and improved aftertreatment solutions — combined with hybridization and the adoption of alternative fuels (e.g., biogas, HVO, green hydrogen), represents a key pathway for meeting future emission and GHG targets. In this study, advanced combustion systems were developed for a 13‑liter diesel engine for heavy-duty truck applications, with the objective of complying with forthcoming Euro VII regulations while maximizing thermal efficiency. The combustion system architecture—including open-bowl geometry with high aspect ratio, injector nozzle with wider spray opening angle, and reduced swirl ratio—was optimized using a Machine Learning–algorithm trained on high-fidelity 3D CFD

Belgiorno, Giacomo, Centini, Maria Pia, Pezza, Vincenzo, Cozza, Ivan F., Pesce, Francesco C., Vassallo, Alberto, Colombo, Giovanni, Gallo, Alessandro, Mirzaeian, Mohsen, Borg, Jonathan

Enabling Flexibility Services through Interoperable V2X Infrastructures: Advanced Modeling and Operational Validation in the FLEXV2X Project

2026-37-0024To be published on 06/09/2026

The integration of Electric Vehicles (EVs) as active grid resources represents a pivotal shift towards decarbonisation. However, the implementation of effective Vehicle-to-Everything (V2X) services faces technical challenges regarding interoperability, predictive management, and battery health preservation. This work presents a comprehensive system design and research methodology developed within the framework of the FLEXV2X project, aimed at addressing interdependencies within a unified bidirectional charging ecosystem. The proposed scientific framework addresses two complementary timescales. At the device level, the study details the modelling and optimisation of bidirectional converters, focusing on control algorithms designed to ensure robust dynamic response and efficiency. Building upon this hardware foundation, the paper describes a system-level optimisation strategy. By employing open-source cyber-physical modelling, the architecture simulates complex EV-grid interactions. This

Lutzemberger, Giovanni, Barater, Davide, Ceraolo, Massimo, Fera, Cesare, Leaver, Ian, Pasini, Gianluca

Forecasting Electric Vehicle Fleet Growth and Energy Requirements in the UK

2026-37-0043To be published on 06/09/2026

With the United Kingdom (UK) goal to achieve a fully decarbonised energy sector by 2035 and achieve net zero greenhouse gas emissions by 2050, the transition of the UK’s passenger car fleet to battery electric vehicles (BEVs) plays a crucial role in reaching this goal. This study evaluates the environmental and energy impact of large-scale BEV adoption by modelling future uptake scenarios using historical fleet data combined with assumed impact of future policy such as the 2030 ban on the sale of new petrol and diesel vehicles. The results have shown a large-scale adoption is possible by 2040, predicting almost 37 million BEVs on the road. However, the associated electricity demand is predicted to rise to nearly 110 TWh annually, signifying the need for rapid development in renewable energy generation. Although BEVs significantly reduce transport sector emissions, the overall climate impact is dependent on a continued effort of grid decarbonisation.

Burke, Bradley, Kateregga, Sunny, Sodre, Jose Ricardo

Impact of Compression Ratio and Stroke on Hydrogen Requirement for Emission Optimized Operation of an Ammonia-Fuelled Spark-Ignition Engine

2026-37-0030To be published on 06/09/2026

Ammonia-fuelled engines have emerged as a promising route toward net-zero emission targets due to ammonia’s carbon-free nature, ease of storage, and established handling infrastructure. However, the low burning speed and narrow flammability limits of ammonia pose a significant combustion challenge, which can be addressed through hydrogen co-fuelling. For practical implementation, on-board hydrogen production via thermal catalytic cracking of ammonia is an attractive solution, as it eliminates the need for external hydrogen storage and associated handling and capital costs. Previous studies by the present authors identified a lean operating strategy that achieves an equal molar ratio of NOx and unburned ammonia (NH3), enabling complete conversion to nitrogen and water vapour when coupled with a Selective Catalytic Reduction (SCR) system. This strategy was further validated using cracked ammonia-derived hydrogen in place of bottled H2 through an on-board cracker, thereby representing a

Yadav, Neeraj Kumar, Ambalakatte, Ajith, Geng, Sikai, Gopakumar Suja, Gagan, Birch, Alexander, Cairns, Alasdair, Harrington, Anthony, Hall, Jonathan

Passenger Car Powertrain for Voltages above 800V

2026-37-0020To be published on 06/09/2026

The EU-funded innovation project High-Voltage fast-charging Efficient electric vehicle Powertrains (HiVEP) develops new technologies for mass-market electric vehicles (EVs) by advancing architectures operating above 800 V. These architectures integrate silicon carbide (SiC)-based power electronics, rare-earth-free electric machines with active winding reconfiguration, high C-rate batteries, and optimised thermal management systems. HiVEP aims to deliver sub-10-minute fast charging, reduce energy consumption by at least 25%, extend range by 20%, and cut system costs by up to 20% in volume production. This article expands on the project objectives, methodological approach and expected key innovations as well as technical, environmental, and societal impacts. The discussion situates HiVEP within the European research and innovation landscape, emphasising its role in accelerating adoption of sustainable mobility solutions.

Schernus, Christof, Nada, Shady, Neuhaus, Christoph, Ewald, Jens, Swierc, Daniel, Kallur-Krishnamoorthy, Rajesh, Vasiliadis, Harilaos

Thermal and Emission Characterization of Cylindrical Lithium-Ion Batteries under Thermal Abuse Using a Climate Chamber

2026-37-0023To be published on 06/09/2026

Thermal safety in lithium-ion batteries is a critical aspect due to their increasing use in energy storage systems and electric vehicles. To investigate thermal abuse conditions, numerous studies on cylindrical cells employ specialized equipment to accurately measure temperature and pressure during thermal runaway events. However, such tests typically require robust, dedicated, and high-cost facilities, which limit their availability in conventional laboratory environments. In this context, the present study proposes and evaluates an experimental methodology based on the use of a climate chamber combined with an instrumented reduced-volume container, to reproduce severe external heating conditions. The thermal behavior and gas emissions associated with thermal runaway events were characterized in six cylindrical lithium-ion batteries of two different chemistries. Initially, the experimental setup was validated using a reference cell to verify the methodology and the measurement systems

Penagos Vásquez, Diego Alejandro, Marco-Gimeno, Javier, Monsalve-Serrano, Javier, Garcia, Antonio, Perez Balastegui, Jose

Analysis of a sorption energy storage system for cabin air heating and dehumidification in electric vehicles

2026-37-0034To be published on 06/09/2026

This work investigates the integration of a Sorption Thermal Energy Storage (TES) into the Air Conditioning (AC) system of electric vehicles. The proposed device reduces the energy demand for cabin heating under winter conditions, leading to a driving range increase. The TES dehumidifies the cabin air through a desiccant bed (zeolite 4A), preventing window fogging, enabling higher air recirculation rates, and consequently reducing the required heating power. An experimentally validated numerical model was used to analyze the adsorption and regeneration processes and to identify suitable operating conditions. Regeneration was found to be effective at moderate temperatures (around 120°C), with a counter-current airflow configuration providing faster and more efficient desorption compared to parallel-flow operation. A simplified model integrating TES, AC unit and cabin was developed and used to compare different configurations. Heating energy consumption with and without TES under

Verlingieri, Rebecca, Calabrese, Luigi, Freni, Angelo, Marocco, Luca, Scudeler, Gabriele, De Antonellis, Stefano

Rapid Fuel Distribution Prediction in RCCI Marine Engines Using CFD-Informed, Active Learning Framework

2026-37-0017To be published on 06/09/2026

Accurate prediction of in-cylinder fuel distribution (FD) is fundamental to reduced-order combustion modeling and emissions prediction, yet remains computationally prohibitive with high-fidelity CFD alone. This work develops a CFD-informed machine-learning surrogate for spatial FD in a large-bore diesel engine, based on a Wärtsilä W20 injector and representative engine conditions. A fully coupled injector–spray–engine CFD framework under engine-like RCCI inert conditions determines the needle-lift profile and resolves the combined effects of injector geometry, needle dynamics, and operating conditions on in-cylinder flow, capturing physical phenomena not reproducible by isolated free-spray simulations. A high-fidelity database is generated using Latin Hypercube Sampling, from which FD is extracted at 15 CAD before top dead center within an annular multi-zone (MZ) representation consistent with reduced-order combustion models. A multi-output Random Forest (RF) surrogate, augmented with

Moradi, Jamshid, Salahi, Mahdi, Heidarabadi, Shadab, Andwari, Amin, Konno, Juho, Wik, Christer, Mikulski, Maciej

Low-Voltage Electric Vehicle with a Reconfigurable Powertrain Architecture for Urban and Extra-Urban Mobility

2026-37-0025To be published on 06/09/2026

The growing penetration of electric mobility in urban environments calls for vehicle solutions that balance accessibility, safety, and operational flexibility. However, high upfront costs and the widespread oversizing of battery systems remain significant barriers to large-scale adoption, particularly for short-range use cases. To address these limitations, this paper presents the Low Voltage Reconfigurable Electric Vehicle (LVREV), a modular vehicle concept designed to bridge the gap between L7e and M1 categories. The proposed architecture integrates a low-voltage electrical system (<60 V), a reconfigurable powertrain, and a modular, swappable battery, enabling mission-adaptive performance while reducing cost, weight, and energy consumption. The vehicle architecture features a dual 52 V inverter system coupled with a six-phase permanent magnet synchronous motor, optimized through model-based design and genetic algorithms. Energy storage consists of two independent swappable battery

Tramacere, Eugenio, Favelli, Stefano, Galluzzi, Renato, Tonoli, Andrea

Automated Design Sustainability Evaluation Framework: a CAD-Integrated Tool for Real-Time Costing and LCA in Automotive Engineering

2026-37-0044To be published on 06/09/2026

As the automotive industry faces increasingly rigorous environmental regulations and an approaching obligation for Digital Product Passports (DPPs), incorporating sustainability metrics into the early design phase has become a necessity. Traditionally, Life Cycle Assessment (LCA) and manufacturing cost estimation are performed during or after the design phase using specific methods and tools, resulting in costly iterations and delayed decision-making. This paper introduces a preliminary computational tool that combines 3D CAD and spreadsheet software via VBA integration. The framework automates the generation of an “Extended Bill of Materials” by extracting geometric and manufacturing data directly from CAD models. This tool’s classification logic is a key innovation that intelligently processes CAD features to identify component categories, such as sheet metal, machined parts, or plastic injections. This automated recognition allows the framework to implement specific algorithmic

Guadagno, Maurizio, Cecconi, Leonardo, Berzi, Lorenzo, Delogu, Massimo

Experimental investigation on cold start and warm-up phases for an ethanol fueled SI engine.

2026-37-0031To be published on 06/09/2026

Engine operation during the initial warm-up period is characterized by pronounced thermal non-uniformities, which have a direct impact on fuel efficiency, combustion stability, tailpipe emissions and engine long-term reliability. For these reasons, accelerating the warm-up process represents an effective strategy to improve both environmental impact and overall engine performance. This paper presents an experimental study carried out on a 1.2 liter, naturally aspiered, four-cylinder SI engine equipped with a Port Fuel Injection (PFI) system. The engine is fueled with standard gasoline and an ethanol-gasoline blend containing 30% ethanol by volume (E30), with the aim of assessing the influence of fuel formulation on thermal evolution after a cold start. Each test begins with the engine at ambient temperature and is continued until thermal equilibrium is established. Experiments are carried out at a single steady operating point of 2000 rpm and 20 Nm, selected to reproduce representative

Falbo, Luigi, Falbo, Biagio, Perrone, Diego, Castiglione, Teresa

Integration of energy consumption simulation in optimal charging planning for battery electric long-haul trucks

2026-37-0019To be published on 06/09/2026

Vehicle fleet decarbonization is a key objective for the coming years, with electrification representing the primary pathway to achieving the targets set by the European Union. The share of battery electric trucks in new registrations has been gradually increasing especially in light and medium size trucks. The replacement rate of diesel long-haul trucks with zero emission trucks is still low due to challenges posed by added complexity and limitations of battery charging. Depot overnight charging is not sufficient to cover the energy needs of a truck covering large distances and careful planning of the route using public charging infrastructure is crucial for an optimized route minimizing extra costs and range anxiety. The current work aims to develop a methodology to propose the optimal charging locations for a given route of a battery electric truck based on nearby stations along the route. Our study uses an open-source optimization algorithm for the fixed route vehicle charging

Perdikopoulos, Michail, Doulgeris, Stylianos, Livitsanos, Georgios, Kazakis, Thomas, Mellios, Giorgos, Ntziachristos, Leonidas

Stakeholder-Oriented Explainability Requirements for Advanced Driver Assistance Systems and Automated Driving Systems: A Structured Analysis

2026-01-0788To be published on 06/01/2026

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

First Time Quality as an Indicator for Quantifying the Impact of Software Updates in Automotive Production

2026-01-0786To be published on 06/01/2026

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

Production-Ready Automated ECU Calibration using Residual Reinforcement Learning

2026-01-0780To be published on 06/01/2026

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 systems

2026-01-0783To be published on 06/01/2026

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

Design and Prototyping of a winding reconfiguration system for electric traction applications

2026-01-0730To be published on 06/01/2026

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

Context-Dependent Assessment of Driving Dynamics and User Perception in Automated Driving

2026-01-0757To be published on 06/01/2026

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

Passive Thermal Management of Battery Disconnect Units for Fast-Charging Electric Vehicles: A Comparative Study of Phase Change Material Heat Transfer Configurations

2026-01-0759To be published on 06/01/2026

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 lng, Christelle, Palluel PhD, Marlène, Noiseau lng, Pascal, LARDEUX, Sébastien

Multi-Agent LLM workflow for Regulatory-Driven Requirement Generation in Automotive Software Development

2026-01-0787To be published on 06/01/2026

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

Vision-Language Model Training for Code Generation from Requirements and Model-Based Artifacts

2026-01-0770To be published on 06/01/2026

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

An Application-Oriented Process Model for Selecting Uncertainty Quantification Methods in Machine Learning

2026-01-0773To be published on 06/01/2026

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

Development of a digital twin of an electrical powertrain in a testbench environment for efficiency estimations

2026-01-0744To be published on 06/01/2026

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

From Wind Tunnels to an AI-Supported Data-Driven Development Ecosystem How Software Transforms Aerodynamic Testing into a Strategic Asset

2026-01-0767To be published on 06/01/2026

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.

Enhancing Efficiency in Megawatt-Scale EV Charging via Adaptive Multi-Agent Control of DC/DC Converters

2026-01-0769To be published on 06/01/2026

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

Cybersecurity Risk Assessment and Experimental Validation of Automotive Ultrasonic Sensors

2026-01-0782To be published on 06/01/2026

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

Stochastic Gradient Pulse Adaptation for Onboard Pulse Charging in Grid-Friendly DC Fast Charging of Battery Electric Vehicles

2026-01-0734To be published on 06/01/2026

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

Internal Low-Temperature Heating of Lithium-Ion Cells Using Alternating Current: Thermal Performance and Aging Behavior

2026-01-0753To be published on 06/01/2026

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

Multi-Agent LLM Pipeline for Systems Engineering Automation

2026-01-0777To be published on 06/01/2026

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

Integrated DC Boost Charger in Post-800V Architecture with Open-End Winding Machine – Magnetic-Domain Modelling and Torque Analysis

2026-01-0739To be published on 06/01/2026

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

Route-Based Sensor-Aware Speed Planning and Support for Safety Validation for Level-3 Automated Driving

2026-01-0772To be published on 06/01/2026

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 II

2026-01-0779To be published on 06/01/2026

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

Kolmogorov-Arnold Networks in Industrial Application of Truck Development

2026-01-0760To be published on 06/01/2026

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

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