Browse Topic: Product development

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A Self-Learning Framework for NVH CAE Analysis2026-01-0718To be published on 06/20/2026
The vibro-acoustic performance of a vehicle is a critical factor in customer perception of quality and comfort. Optimizing for Noise, Vibration, and Harshness (NVH), particularly road noise, presents a persistent challenge in the automotive development cycle. While modern FEM analysis is essential, the increasing complexity and sheer volume of CAE simulation data often overwhelm manual interpretation, potentially leading to prolonged development times or sacrifices in resulting comfort quality. To address these challenges, this paper introduces the application of CDH/ACE (Autonomous Computational Experiments), a framework that combines conventional CAE simulation workflows with machine learning in an iterative cyclic process. This creates a self-learning and self-correcting system that autonomously defines, performs, and learns from computational experiments. By building predictive models from simulation data, the framework intelligently guides the design exploration to achieve
Visser, Rene
AI-Based Package Tray Design Method for Controlling Acoustic Modes in a Sedan2026-01-0716To be published on 06/20/2026
• In this study, we propose a methodology for predicting the acoustic modes and natural frequencies of a sedan using artificial intelligence and demonstrate the feasibility of controlling its acoustic characteristics by modifying the hole distribution of the package tray. In typical sedan structures, the cabin cavity and trunk cavity are acoustically coupled through holes in the package tray. The distribution of these holes significantly affects the natural acoustic modes and frequencies of the vehicle. However, once the exterior shape of the vehicle is finalized during the design phase, options for structural modifications to mitigate noise issues caused by these modes become extremely limited. To address this challenge efficiently, we develop a deep learning-based neural network model trained on data derived from a simplified acoustic analysis model of a sedan that includes a package tray. Finite element analysis is performed to generate acoustic modes and natural frequencies, which
Lee, Jin Woo, Cho, Jaeho, Nam, Younsic, Han, Yongha
Validation of an adaptive order Finite Element model for transmission loss simulation of trimmed automotive structures2026-01-0707To be published on 06/20/2026
Vehicle electrification and increasing demands for driving comfort present significant challenges for designing effective noise control treatments in modern vehicles. Lightweight, low-emission designs often compromise acoustic efficiency. One way of compensating this is through the use of multi-layer ‘trim’ material configurations to mitigate noise across a wider frequency range. Traditional 3D finite element models, while accurate and even needed to capture the full dynamic behaviour, become computationally prohibitive for complex automotive structures like firewalls, which feature intricate shapes, high curvature, and material compression. This computational burden limits design exploration and timely noise performance predictions. This paper introduces the use of an innovative adaptive higher-order finite element method to address these challenges. The method utilizes a fixed finite element discretization of trim components and surrounding acoustic fluid, incorporating an automated
Van Genechten, Bert, Vansant, Koen, Purohit, Bimal, Effinger, Veronika
Bridging Simulation And Reality: Incorporating Seal Contact Detachment Into Stick–Slip Prediction2026-01-0714To be published on 06/20/2026
Preventing stick–slip in virtual vehicle development combines virtual displacement data with experimental sliding tests. If the calculated relative displacement at a contact exceeds the distance of two stick–slip impulses, that contact could be a potential noise source. Such risks can be mitigated only by design changes or alternative sealing concepts. Stick–slip tests provide the experimental reference, showing how often a frictional pairing produces impulses over a defined sliding distance. The basis for this assessment is Multibody Dynamics (MBD) simulations, which compute component deformations during virtual drives and derive relative displacements at contact interfaces (e.g. in the sealing gap between body and closure), representing the theoretical sliding distance that enables stick–slip effects. Yet, this combined approach has overlooked a critical factor: the deformability of automotive seals. Due to their elastomeric properties and geometry, seals can elastically follow
Strangfeld, Martin, Fritz, Susanne, Weber, Jens, Rosell, Anneli
An open-source framework to Automate Virtual Optimization of Internal Combustion Engine Calibration Maps through shape-based strategy2026-37-0001To be published on 06/09/2026
The automotive industry is facing increasingly stringent regulatory constraints, driving the need for faster and more efficient powertrain development. This results in higher systems complexity, making internal combustion engine calibration progressively more challenging to meet performance and emissions targets. This, combined with the manual nature of traditional calibration workflows, leads to a time‑consuming process that heavily relies on human expertise. Although virtualization can reduce development time and costs, the overall workflow remains largely dependent on manual decision‑making and iterative refinement. In this context, this work presents a virtual calibration framework based on a genetic algorithm, aimed at the automated optimization of engine calibration maps to satisfy performance and emissions constraints, while reducing manual effort. Each calibration map is represented through a polynomial parameterization. Specifically, a generic three‑dimensional polynomial with
Romano, Gianvito, Aglietti, Filippo, Spedicato, Tonio, cozza, Ivan Flaminio, Capra, Andrea
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 06/01/2026
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
Vision-Language Model Training for Code Generation from Requirements and Model-Based Artifacts2026-01-0770To 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
Multi-Agent LLM Pipeline for Systems Engineering Automation2026-01-0777To 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
Development of a digital twin of an electrical powertrain in a testbench environment for efficiency estimations2026-01-0744To 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 Asset2026-01-0767To 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.
Production-Ready Automated ECU Calibration using Residual Reinforcement Learning2026-01-0780To 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
Integrating Formal Methods into Model Based Systems Engineering(MBSE) to Mitigate Requirement Escapes2026-26-0712To be published on 06/01/2026
The industry is undergoing a significant digital transformation in the way system requirements are defined, communicated, and managed. Major original equipment manufacturers (OEMs) are moving toward fully model-based development processes, with plans to deliver requirements exclusively in the form of system models. It is no longer sufficient to manage requirements using traditional document-based approaches; instead, organizations must adopt tools and processes that enable the consumption, interpretation, and implementation of model-based requirements. Preparing to meet this industry direction is critical to maintaining competitiveness and alignment with our customers’ expectations. Model-Based Systems Engineering (MBSE) plays a central role in this transition. MBSE enables engineers to define, analyze, and manage system requirements, architectures, behaviors, and verification strategies within a cohesive model-based framework. It supports traceability, promotes consistency across
Gupta, Chandan, Nakkeeran, Rupashree
AI-Led Sustainability Strategy: Driving Product Value from Birth to Disposal2026-26-0781To be published on 06/01/2026
Product development in Aerospace, Oil & Gas, Turbomachinery and Aviation sectors are associated with complex engineering challenges and long lead time to materialize products. More so in Aerospace and Aviation sectors, unlike other sectors, they are highly cost intensive, consuming few millions of dollars for introducing minor modifications to billions of dollars for a major modifications along with regulatory constrains. In addition, the product development cycle is spread over years ranging from a couple of years for minor modification to nearly a decade or more for major modifications. The profitability of the product is derived through its Safety, Reliability and Availability in operation. The product is maintained in service until it sustains value in its operation and is discarded if the value deteriorates and the cost of upkeep exceeds the product value. Product Value in the present context is not the price tag but encompasses intangible elements like brand value, reputation etc
Srinivasan, Karthik, G.V.V., Ravi Kumar, Vaderahobli, Devaraja Holla, Bhate, Ujwal, Veluri, Sastry
Interpretable Tire Force Modeling for Formula Student Vehicle Dynamics and Lap Time Applications2026-01-0762To be published on 06/01/2026
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
Application of Deterministic and AI-Based Methods to Accelerate the Component-Level Development Process in Electric Vehicles2026-01-0766To be published on 06/01/2026
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
Digitalization of Mechanical Labs for Enhanced Traceability and Virtual Verification2026-26-0715To be published on 06/01/2026
Digitalization is the process of leveraging digital technologies to transform business operations, processes, and models, enabling organizations to improve efficiency, create new value, and enhance customer experiences. It is essential as it enables data-driven decisions and reduces product development time. It’s easier to Digitalize new products however, transforming existing products and processes is a challenging task, as constituents are in various phases of lifecycle. Also, the existing/ legacy data acts as a starting point for future programs. Currently, teams are spending hours to weeks finding the right processes and data, costing $~14,000 per test based on labor hours. To tackle this challenge, Mechanical labs are digitalizing their data and processes alongside physical tests via 3DEXPERIENCE application to capture data in digital models and ensure traceability for which Requirement Functional Logical Physical (RFLP) framework is leveraged. This traces Requirements to its
Karpur, Anoop, Inapakolla, Bharat Kumar, Harris, Jason
Risk-Driven Program Management Framework - Abstract2026-26-0716To be published on 06/01/2026
Avionics programs face increasing complexity due to evolving safety, certification, and cybersecurity requirements. Conventional program management approaches, focused mainly on cost and schedule, often overlook the dynamic and interdependent risks inherent in developing safety-critical systems. This gap results in late surprises, inefficient resource use, and challenges in achieving certification milestones. This paper introduces a risk-driven program management framework tailored for avionics, where risk identification, assessment, and mitigation are embedded within the governance model rather than treated as a parallel activity. The framework integrates industry standards such as DO-178C, DO-254, DO-326A, and ARP4754A, ensuring alignment with regulatory expectations while promoting a holistic view of program health. A key feature of the approach is the use of digital dashboards and predictive analytics to transform static risk registers into actionable tools. These dashboards
Rahul, Saurabh, Benikireddy, Raghunatha
Hybrid Digital Twin for Aero-Engine Bearing Prognostics: A Physics-Informed Machine Learning Approach to RUL Prediction2026-26-0718To be published on 06/01/2026
Unscheduled maintenance due to the failure of critical components, such as aero-engine rolling element bearings, is a leading cause of costly Aircraft-on-Ground (AOG) events; consequently, current time-based maintenance practices are inefficient and prone to risk. This paper develops a resource-efficient Hybrid Digital Twin (HDT) model for an engine bearing, focusing on the dynamic prediction of spall growth due to Rolling Contact Fatigue (RCF), thereby enabling a condition-based maintenance paradigm. The HDT architecture integrates two core models: (1) a physics-informed model that uses established life and fatigue theory to define initial degradation thresholds and constrain the failure envelope, and (2) a data-driven Recurrent Neural Network (RNN) (specifically, an LSTM) for dynamic degradation rate modeling. The methodology utilizes a Monte Carlo simulation coupled with RCF progression equations to generate a large, synthetic run-to-failure dataset under varying operational loads
Mohamed, Abbas
Zero Waste Manufacturing in Aerospace Circular Design Approaches from Concept to End-of-Life2026-26-0775To be published on 06/01/2026
Achieving zero waste in aerospace manufacturing demands a fundamental shift from traditional end-of-pipe solutions to circular design principles embedded from the earliest stages of product development. This paper presents a practical framework for integrating circularity into aerospace systems, emphasizing design strategies that minimize material waste, facilitate disassembly, and enable component recovery throughout the product lifecycle. The approach is structured around five key pillars: 1. Design for Modularity and Disassembly 2. Material Substitution to Enhance Recyclability 3. Waste Segregation and Characterization in Manufacturing Processes 4. Component-Level Circularity Readiness Scoring 5. Collaborative Supplier Engagement to Establish End-of-Life Pathways To support implementation, a Circularity Readiness Assessment Tool has been developed, enabling engineers to evaluate designs against circularity criteria such as disassembly ease, material recyclability, and supplier
S, Chaitra
AI Model Creation and System Verification of “Telemotion” Infrastructure-Cooperative Autonomous Driving System Using Digital Twin2026-01-02554/7/2026
The concept of the vehicle has changed as a result of many innovations over the last decade in the fields of connected, autonomous/automated, shared, and electric (CASE) technologies. At the same time, labor shortages in Japan are becoming more serious due to a decline in the working population. To help resolve these issues, a remote-controlled autonomous vehicle driving system called Telemotion has been developed that automates the movement of vehicles in production plants. This system is an autonomous driving and transportation system in which the recognition, judgment, and operation functions of driving are handled by a control system outside the vehicle that communicates wirelessly with the vehicle. This system utilizes artificial intelligence (AI) and other advanced technologies to realize safe unmanned autonomous driving, and is already in operation in production plants. Currently, efforts are under way to build a digital twin environment and conduct AI learning using computer
Hatano, Yasuyoshi, Iwazaki, Noritsugu, Nagafuchi, Yuhei, Iwahori, Kento, Tanaka, Atsushi, Uezu, Satoru, Kanou, Takeshi, Inoue, Go, Okamoto, Yuki, Oka, Yuhei, Kakuma, Daisuke, Chiba, Hiroya, Egashira, Kazuki, Ishikuro, Megumi, Sawano, Takuro
Data-Driven Calibration of Vehicle Simulations for Accurate Control Response in Development and Testing2026-01-00584/7/2026
With the steady increase in autonomous driving (AD) and advanced driver-assistance systems (ADAS) aimed at improving road safety and navigation efficiency, simulation tools have become a critical part of the development process, allowing systems to be tested while mitigating the risk of physical injury or property damage upon failure. Physics-based simulators are central to virtual vehicle development, yet their control responses often differ from real vehicles, potentially limiting the transfer of controllers and algorithms developed in simulation. As these simulations play an important role in the vehicle design and validation process, a critical question is how well their predicted behavior translates to real-world physical systems. This paper presents a calibration framework for an autonomous vehicle platform that learns the motion characteristics of an experimental vehicle and uses that knowledge to correct the actuator response of a simulation model. The model is trained by
Soloiu, Valentin, Sutton, Timothy, Mehrzed, Shaen, Lange, Robin, Zimmerman, Charles, Peralta Lopez, Guillermo
An Integrated Methodology for Sustainable Mobility Infrastructure Planning: The Atlanta Aerotropolis Case Study2026-01-00974/7/2026
By the early 2020s, more than 4.5 billion people have been living in urban areas worldwide, compared to just 1 billion in 1960. Rising growth in urban populations present challenges to infrastructure and transportation systems. Higher traffic levels and reliance on conventional vehicles have contributed to heightened greenhouse gas (GHG) emissions, rising global temperatures, and irreversible environmental degradation. In response, emerging transportation solutions—including intelligent ridesharing, autonomous vehicles, zero-tailpipe-emission transport, and urban air mobility—offer opportunities for safer and more sustainable transportation ecosystems. However, their widespread adoption depends not only on technological performance and efficiency, but also on integration with current infrastructure, safety, resilience to unexpected disruptions, and economic viability. A dynamic agent-based System-of-Systems (SoS) transportation model is developed to simulate vehicle traffic and human
Rana, Vishva, Balchanos, Michael, Mavris, Dimitri, Valenzuela Del Rio, Jose
Simulink DevOps Evolution: Bridging Interactive Platforms for Accelerated Model-Based Workflows2026-01-00804/7/2026
This paper builds on last year’s paper presenting DevOps automation in the context of model-based development. Following that paper, we interviewed Simulink users in passenger automotive, motorsports, commercial vehicles, aviation, rocketry, and industrial automation. We discovered that much of the benefit of DevOps platforms to reduce product development cycle time relies on their interactive features. We prototyped new tools to bridge interactive DevOps Git-based platforms with model-based development workflows, and then gathered reactions from another round of interviews. Here we present these interactive DevOps workflows with the feedback from these interviews to contextualize how engineering teams could adopt them to accelerate their own model-based workflows.
Mathews, Jon, Ferrero, Sergio, Tamrawi, Ahmed, Sauceda, Jeremias
Enhancing Track Vehicle Design Engineering Through Digital Twins: A Data-Driven Approach2026-01-02664/7/2026
Digital Twin technology can significantly improve the engineering product design process, especially when considering ground vehicle applications. Data-driven computer studies can assist engineers and key stakeholders in evaluating performance, durability, and other system design tradeoffs. To enable this process, the availability of relevant, numerically generated, laboratory, and/or field data is required. Proper data use enables the digital exploration of “what-if” scenarios, reducing necessary field testing and allowing for the examination of hard-to-test operating conditions. When considering the Digital Twin toolset, a collection of models and simulations are assembled to supplement virtual testing endeavors. These models include surrogate, CAD/CAE, and others. In this paper, an off-road track vehicle design is reviewed through the fusion of numerical and field data to evaluate future design enhancements. Preliminary results demonstrate that subtle feature upgrades can produce
Suber II, Darryl, Bradley, Andrew, Singh, Shubhendra, Turner, Cameron, Castanier, Matthew P., Wagner, John
Low Speed Bumper System Impact Variation Study2026-01-04744/7/2026
Variation studies are an important part of the product development process. They help to understand and estimate real-world deviation from nominal design parameters, optimize designs for robustness, reliability, and cost-efficiency. CAE and Virtual tools enable us to simulate variation types and capture the full bandwidth of actual field performance- rather than the validation from a limited number of physical tests. In this study, the effects of various factors on vehicle performance during low-speed impacts, utilizing a Design of Experiments (DOE) approach have been investigated through virtual simulation. Low-speed impacts, typically defined as collisions occurring at speeds less than 2.5 mph, are critical for understanding vehicle insurability and compliance with regulatory standards. The factors examined include vehicle impactor position, impact speed, angle of collision, part thickness variation, material property variation. The DOE methodology allowed for a systematic analysis
Suravaram, Raghu Mohan Reddy, Islam, ABM Iftekharul, Larson, John, Tehrani, Babak, Koch, Lisa, Mathur, Mohit Sain
Elucidation of Flow Mechanisms Modulating Vehicle Wind Noise by Using Large-Scale CFD Simulation2026-01-06144/7/2026
In vehicle development, noise reduction is critical for ensuring passenger comfort. As electric vehicles become prevalent and engine noise is minimized, wind noise becomes more noticeable. Modulated wind noise, which causes a sense of fluctuation due to atmospheric turbulence, wind gusts, and preceding vehicle wakes, can cause significant discomfort. This noise is characterized as a high frequency sound above 1 kHz, modulated at low frequencies owing to the wind velocity and direction fluctuating at several Hz. The mechanisms behind wind noise modulation are not fully understood, and no established countermeasures have been developed. This is because wind noise perceived through the side window is primarily caused by the A-pillar vortex and door mirror wake, which coexist as complex turbulent flows around the vehicle. Therefore, identifying the source of modulated wind noise around vehicles under fluctuating wind conditions is difficult. This study aims to identify the source of the
Tajima, Atsushi, Hirata, Takumi, Ikeda, Jun, Kamiwaki, Takahiro, Wakamatsu, Junichi, Tsubokura, Makoto
Application of Reliability Growth Methods in Conjunction with Design for Reliability Framework to Validate Heavy Truck Design2026-01-01374/7/2026
Why field campaigns in the automotive industry have been going up over the years despite the strong development of technical knowledge, computational design tools and techniques to secure higher reliability standards since early stages of development phases? Uncertainties created by product complexity have been a factor that affects the ability of the manufacturers to prevent design failures before the product launch. Another factor is the shorter product development time, less test time to validate the product means that the new design will not have enough exposure to the real truck application and so some failures may not be able to be detected during the project. To deal effectively with uncertainties this study shows an application of reliability growth techniques in conjunction with DfR- Design for Reliability framework to validate the truck design in the customer application. The Crow - AMSAA method is applied to measure the reliability growth of the complete vehicle in various
Coitinho, Marcos
Vehicle Test Platform with Experimentally Correlated Tire Model for Torque Vectoring Control2026-01-06224/7/2026
This paper presents a testing platform for the development of lateral stability control systems in independent motor electric vehicles (EVs). A 10 degree of freedom (DOF) vehicle simulation and a radio control test vehicle are constructed to enable controls validation scalable to full size vehicles. These vehicle simulations, or ‘digital twins’, have been widely adopted throughout the automotive industry due to their lower operating costs and ease of implementation. Virtual models are not perfect representations of reality, however, and physical testing is still necessary to validate systems for use in the real world. This is especially true when testing safety-critical features such as stability control. As a result, a simulation environment working in conjunction with a test vehicle represents an optimal hybrid approach. In this work, a high fidelity vehicle model is constructed in the Matlab/Simulink environment. To capture the effect of suspension, the digital twin is capable of
Petersen, Nicholas Conner, Robinette, Darrell
A Real-Time Capable, Three-dimensional, and Nonlinear Multibody Model of the Mercedes-Benz G-Class for an Efficient Assessment of Off-Road Performance in Early Stages of Vehicle Development2026-01-02174/7/2026
This article deals with the development of a real-time capable, three-dimensional model of the Mercedes-Benz G-Class with flexible ladder frame that considers nonlinear suspension kinematics and force elements. The shift to new drivetrain technologies often results in a significant increase in vehicle weight and requires corresponding design modifications – also applying to off-road vehicles. These modifications result in changed stiffness of elements such as the ladder frame or anti-roll bar, which significantly affect vehicle dynamics and off-road performance. Therefore, strategic, efficient assessments must be made in early development stages, where no detailed information about individual systems and components is available yet, to detect and avoid potential massive, costly changes in later stages. This requires a “handmade” vehicle simulation model specifically tailored to this particular application, since the use of commercial multi-purpose simulation packages is not effective
Riebler, Sandro, Pernsteiner, Samuel, Granitz, Christina, Schabauer, Martin
Integrating Optimization and Model Fidelity Selection Methods for Real-Time Driver-in-the-Loop Vehicle Simulators2026-01-06214/7/2026
Automotive OEMs perform extensive prototype testing to configure vehicles for objective criteria (performance), and subjective criteria (handling and comfort). To reduce testing time and costs, OEMs rely on real-time Driver-In-the-Loop Simulators (DIL) running complex Multi-Body Dynamics (MBD) models. Recent advances in simulation technology have increased model accuracy but also operating costs, possibly limiting the viability of real-time DIL applications. Running high fidelity MBD models in real-time is computationally intensive and often requires re-configuration, CAE model de-contenting, and solver setting optimization, which can introduce significant analysis errors. This presents a core challenge: selecting model fidelity levels that result in computationally efficient simulations, while maintaining sufficient predictive accuracy. This study introduces a methodology that integrates optimization algorithms with decision-making techniques to select the right fidelity within a
Balchanos, Michael, Emara, Mariam, Zarate Villazon, Angel, Mavris, Dimitri
Standardization of On-Vehicle Measurement of BEV Thermal System Energy Consumption and Range Impacts2026-01-01324/7/2026
This paper presents research and digital twin modeling results to support work on a methodology to properly account for the energy consumed by the thermal system of a BEV, for use within both existing Petroleum-Equivalent Fuel Economy (PEFE) calculations, and the proposed addition of hot and cold weather range values to the consumer-facing Monroney label [1]. Properly accounting for thermal system impacts would incentivize minimizing energy consumption of these systems, since 1) BEV PEFE is a direct input to an OEMs overall CAFE performance, and 2) the values on the Monroney label has some impact on consumer vehicle choice. The impetus for this work was Final Rules issued by the EPA and NHTSA in early 2024 eliminating A/C Efficiency Credits for BEVs from the 2027 MY, thus eliminating regulatory incentives to minimize energy consumption of these systems. Higher energy consumption will produce a number of negative secondary effects, including higher real-world greenhouse gas emissions
Taylor, Dwayne
A System of System Design Framework for Multi-Domain Vehicles2026-01-02634/7/2026
Traditionally, ground vehicle design is based on identifying engineering solutions that fulfil the requirements and specifications put forth by the stakeholders. Although a vehicle is a single entity, it is composed of many subsystems and thousands of parts that must operate together in unison to meet all design goals. A System of Systems (SoS) design approach enables the consideration of subsystem performance within a framework of overall system operation, which includes possible tradeoffs. This collaborative approach to subsystem and primary system design draws upon modelling, optimization, tradespace analysis and virtual studies. In this paper, a system of system design approach will be investigated for a collection of multi-domain vehicles assembled to undertake coordinated search and rescue operations on land and water. A host ground vehicle, an unmanned aerial drone, an unmanned marine drone and an unmanned tracked vehicle constitute the family of multi-domain vehicles which will
Somanchi, Anang, Abeynayake, Chandima, Deshmukh, Mrunal, Suresh, Johir, Ramnath, Satchit, Turner, Cameron, Schmid, Matthias, Castanier, Matthew P., Rapp, Stephen, Jaczkowski, Jeffrey J., Wagner, John
Quantifying Accuracy and Robustness to Guide Fidelity Selection for Multi-Fidelity Vehicle Dynamics Models2026-01-01494/7/2026
Accurate and reliable simulation models are essential for design, development, and performance evaluation during virtual vehicle testing. However, fidelity assessment and validation remain a challenge. While error metrics are used to evaluate simulations, they alone do not capture how reliable predictions are, or how robust models are to varying driving scenarios and modeling assumptions. This work develops a systematic quantitative approach for evaluating vehicle dynamics model fidelity, moving beyond traditional visual or qualitative comparisons. A dimensionless fidelity metric is proposed that integrates error and uncertainty into a single measure, enabling objective accuracy assessment of variable-fidelity simulations. This framework supports fidelity selection in vehicle dynamics, providing clearer insight into tradeoffs between computational cost and achievable accuracy, and advancing the goal of reliable virtual testing. This approach is demonstrated on an open-loop vehicle
Emara, Mariam, Balchanos, Michael, Mavris, Dimitri
Lap Time Performance Sensitivity to Tire Model Parameters: An Optimal Control Framework for Model Selection and Calibration2026-01-02144/7/2026
The push for vehicle development through virtual prototyping and testing in motorsports highlights the critical challenge of tire model selection and calibration, especially when vehicle dynamics must be accurately captured. The calibration process for tire models such as the Pacejka Magic Formula (MF) relies on parameter identification and experimental data fitting. While optimization algorithms have been implemented to calibrate tire models, few studies explore the effects of parameter selection on overall vehicle performance, complicating prioritization for the vehicle’s modeling and simulation strategy. To bridge this gap, this paper leverages optimal control methods to quantify how the variability of MF tire model parameters propagates to the overall vehicle model and impacts lap time prediction accuracy. To achieve this, a subset of parameters critical to combined slip of the MF tire model are varied through a Design of Experiments (DOE). These variations are executed on a flat
Zarate Villazon, Angel M., Brown, Ian, Balchanos, Michael, Mavris, Dimitri
Digital Twin Aerodynamics Model Validation for HALO Wind Tunnel2026-01-06104/7/2026
A simulation-based aerodynamics model of the Honda Automotive Laboratories of Ohio (HALO) Wind Tunnel, a three-quarter open-jet (ground plane) configuration opened in 2022 for full-scale automotive testing, was initiated to support data fusion for more accurate surrogate models in vehicle engineering programs. The objective was to demonstrate that a matched set of boundary values between the physical wind tunnel and the three-dimensional numerical model yield correct responses for several key flow field quantities, starting with the baseline empty tunnel case: (1) streamwise static pressure distribution, (2) evolution of the free shear layers downstream of the nozzle exit plane, and (3) ground-plane boundary layer development. Pressure-based measurement probes were deployed in these regions using a four-axis overhead traverse to acquire validation data in the large facility, including instrument verification between a 14-hole probe and Pitot-static rake. Detached eddy simulation (DES
Patel, Sajan, Disotell, Kevin, Eagles, Naethan
In-Use Life Digital Twin (Redundant Storage) and Swappable Use Case for Battery Durability2026-01-03974/7/2026
As regulatory frameworks for zero-emission vehicles (ZEVs) and battery electric vehicles (BEVs) continue to evolve, there is growing emphasis on monitoring battery durability and usage throughout the vehicle lifecycle. These regulations increasingly specify the use of data monitors and tracking mechanisms to assess battery health and performance. In addition, regulations require anti tampering mechanisms especially for monitors that have external write access. Historically, regulations focused primarily on vehicle warranty; however, with the introduction of battery durability monitors, clarity is needed for the new battery durability monitors. More specifically if the battery durability monitors track with the lifetime of the vehicle or if they follow the lifetime of the battery. Furthermore, current regulations provide no guidance on high-voltage (HV) traction battery service strategies or methods to protect monitors from tampering by external customers. This paper will classify
Laskowsky, Patricia, Bunnell, Justin, Zettel, Andrew, Albarran, Josue
MBSE-Driven XIL Workflow for Energy Evaluation of Automated Vehicles: From Simulation to VIL Testing2026-01-02894/7/2026
With the increasing market penetration of automated vehicles, there is a critical need for credible and repeatable methods to quantify their energy impacts. This paper presents a Model-Based Systems Engineering (MBSE)-driven Anything-in-the-Loop (XIL) methodology for quantifying the powertrain energy consumption and potential savings from various controls for automated vehicles in realistic road scenarios while preserving high-fidelity powertrain behavior. The novelty of this approach lies in its use of a unified MBSE backbone (AMBER: Argonne National Laboratory’s [Argonne’s] MBSE-centric platform for transportation energy analysis) to automate the seamless and traceable progression from pure simulation to Vehicle-in-the-Loop (VIL) testing. This work utilizes Argonne's multi-vehicle simulation tool, RoadRunner, which automatically constructs closed-loop road scenarios (road geometry, vehicle sensors, other vehicles, and traffic controls) and connects them to Argonne’s validated, high
Jeong, Jongryeol, Sharer, Phillip, Di Russo, Miriam, Das, Debashis, Zhang, Yaozhong, Karbowski, Dominik
Development of Technology to Distinguish Contaminants using LiDAR Near-Field Point Cloud based on Real-Field Data2026-01-00204/7/2026
Reliable environmental perception under adverse and contaminated conditions is a critical requirement for autonomous driving systems. Although LiDAR sensors play a central role in such perception, their performance is significantly degraded by surface contamination caused by environmental factors such as rain, snow, dust, anti-icing materials, and bug splatter impacts. However, most existing public datasets and prior studies rely on simulated or laboratory-generated contamination scenarios, which limit their applicability to real-world autonomous driving. To address this gap, we construct a large-scale real-world dataset collected from approximately 22,000 km of on-road driving across diverse regions of the United States, covering a wide range of naturally occurring environmental contamination conditions. The dataset was acquired using a multimodal sensing platform integrating LiDAR, perception RGB cameras, infrared camera sensors, and external monitoring systems, enabling
Kim, Hunjae
Optimizing Virtual Scenario Testing for Autonomous Vehicles with Large-Scale Scenario Generation2026-01-00504/7/2026
The validation of Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) Systems, especially at higher automation levels such as SAE Level 3 or 4, demands the testing of a vast array of scenario variants far exceeding the scope of standard safety specifications like Euro NCAP (The European New Car Assessment Programme). Autonomous vehicles require thorough real-world testing to ensure automotive safety. However, public road tests are costly and risky. Instead, virtual scenarios - digital twins of real environments - offer a safe, cost-effective testing alternative. Exhaustive simulation across this high-dimensional scenario space, which includes variations in actor behavior, environmental conditions, and event characteristics, is computationally infeasible. We propose a constraint-solving approach to address this challenge that leverages mathematical and geometric techniques to analytically assess the existence and validity of scenario variants prior to simulation. Two
Karve, Omkar, Saurav, Saket, Purwar, Prabhanshu
A Cloud-Vehicle Integrated Closed-Loop Testing Framework for Intelligent Chassis2026-01-00514/7/2026
Vehicle system testing serves as a critical phase in obtaining road certification for prototype vehicles. While direct road testing with physical vehicles yields the most authentic data, this approach entails significant costs, challenges in reproducing extreme scenarios, and inherent safety risks. In contrast, virtual vehicle-based testing technologies represent advanced simulation methodologies for enhancing development efficiency and quality, effectively mitigating risks associated with complex real-world operating conditions and hazardous physical testing. However, virtual vehicle models often rely on idealized parameters, limiting their ability to reflect real-world dynamics and resulting in lower credibility of test outcomes. Furthermore, as evidenced in current mainstream virtual testing software, environmental simulations predominantly remain confined to the visual domain, with limited direct interaction between dynamic environmental changes and virtual vehicle responses. To
Liao, Yinsheng, Cheng, Qing Hua, Qu, Wenying, Wang, Zhenfeng, Wu, Yan, He, Chengkun, Zhang, Junzhi, Lu, Yukun
CAE Acceleration with Machine Learning for Results Prediction2026-01-04884/7/2026
Industries are following a tedious product development cycle for developing their product. In product development major steps includes design ideas, Drawings, CAD, CAE, Testing and design improvement cycle. This is a monotonous process and takes time which impacts on its time to deliver product and cost on development. Now a days industries are fast growing and targeting to reduce development cycle time and cost. AI&ML is impacting almost all areas in the industry and significantly reducing efforts time and cost. To make use of AI&ML in CAE, Altair Physics AI is an effective tool. To ensure the design of product traditional way is to develop a CAD of the product, develop, perform CAE and analyze performance. If we consider CAE procedure it is time consuming process which includes FEA model build, applying boundary conditions, running simulation and analyzing results which could take minutes to hours. By using ML with Physics AI we can make predictions on new design of the product in
Dangare, Anand Manohar, Kulkarni, Mandar
Maintaining a Healthy Safety Culture in Autonomous Vehicle Development and Operations2026-01-05284/7/2026
Safety isn’t just the absence of accidents - it’s the presence of trust, empowerment, and accountability at every level. The result is a high-trust culture where process becomes practice and safety is a shared achievement. When people closest to the work feel supported to act on what they see, safety becomes the standard. Thus, the deployment of autonomous driving systems (ADSs) requires not only technical rigor but also a resilient organizational safety culture that supports continuous learning, accountability, and transparent communication. This paper examines how safety culture can be operationalized in ADS development and operations by integrating guidance from standards such as UL 4600 and best practices from SAE AVSC. UL 4600’s requirements for systematic hazard analysis, safety case maintenance, and safety performance indicators (SPIs) are used as a foundation for quantifying organizational behavior within a Just Culture framework. This work draws on Human and Organizational
Wagner, Michael, Gittleman, Michele
Collaborative Development Framework for Electric-Based Software-Defined Vehicles – The European Research Project CODE4EV2026-01-01134/7/2026
The automotive industry is subject to major transformation initiated by societal and economical pull (reducing emissions, zero fatalities, European competitiveness) and accelerated by technology push (electrification, Cooperative, Connected and Automated Mobility (CCAM), and Cooperative Intelligent Transport Systems (C-ITS)). Following this trend, the Software-Defined Vehicle (SDV) targets the integration of software (SW) development methodologies for vehicle development as well as the value delivery shift toward customers along the entire lifecycle. It promises to create benefits for the car manufacturers in terms of faster time to market, easier update – as well as for the car users (private persons, fleet operators) in terms of personalized user experience, upgradability. At the same time, SDV requires a much more integrated and continuous development framework to enable different experts to efficiently develop and validate concurrently the different parts of the vehicles, to gather
Armengaud, Eric, Permann, Robert, Joergler, Sabrina, Barcelona, Miguel Angel, García, Laura, Rodriguez, José Manuel, Ivanov, Valentin, Li, Zhenqian, Nguyen Quoc, Trieu, Rodrigues, Sandy, Kowalczyk, Bogdan, Avdić Čaušević, Amra
Design and Performance Evaluation of a Dust Contamination Simulation Test Apparatus for Automotive Cameras2026-01-00594/7/2026
This study presents the development and validation of a muddy water spray apparatus designed to simulate dust contamination on vehicle sensors for sensor cleaning system testing. It is important to have a constant and quantifiable test environment for the vehicle development process. For verifying the apparatus, muddy water, prepared by mixing standardized dust powder, salt, and water to maintain constant contamination test conditions, was sprayed onto glass specimens to evaluate equipment consistency. Deposited dust weight and thickness were measured across multiple spray cycles, with statistical analyses confirming consistent and reliable deposition. Paired t-tests indicated no significant difference between sample positions, demonstrating uniform spray distribution. The apparatus was further applied to individual infrared (IR) cameras to observe performance degradation under dry and wet contamination conditions showing statistically consistent increases in contamination levels
Jinhyeok, Gong
Acoustic Simulation for a Vehicle Audio System Across the Full Audible Frequency2026-01-02264/7/2026
The Audio system is an important part of the design of a vehicle cabin. In the vehicle development process, the audio system needs to be tuned for optimal acoustic performance. Traditionally, this process is performed physically on vehicles. In this paper, a methodology is developed to numerically simulate the acoustic performance of the audio system across the full audible frequency range. To provide validation of the method, the p/v acoustic transfer functions (ie., the sound pressure p at the passengers’ ears divided by the voltage inputs v) are measured for different speakers in a production vehicle. As the sound perceived by the passengers depends on both the source and the path, the method development is split into two parts: (a) characterization of parameters that describe the loudspeaker as a source and (b) representation of the vehicle cabin as a path. The speaker parameters are characterized from sound radiation data measured in a 2pi chamber. To represent the vehicle cabin
Yang, Wenlong, Patra, Suresh, Hawes, David, Shorter, Phil
Designing Military Vehicles for a Battlefield Characterized by Drones2026-01-02624/7/2026
The modern battlefield is increasingly characterized by the use of small drones. As such, military vehicles must now be designed to account for this threat. This paper presents a model-based systems engineering approach to identify vehicle vulnerabilities and generate new vehicle requirements to mitigate them. This approach uses a standard set of System Modeling Language diagrams. A vehicle’s primary roles are captured in a series of use cases. Each use case is characterized by a sequence of activities performed by the vehicle. These activity sequences are captured in an activity diagram, which are used to wargame how a drone can exploit the vehicle at each phase. Each potential exploitation is assigned likelihood and severity scores, which feed into a risk index. This risk index is then used to prioritize each vulnerability. From these vulnerabilities, a set of operational requirements are derived, which then informs the development of system requirements. As the system matures, the
Ells, Alec, Werntz, Bryson, Saulsberry, Taylor, Wilkinson, Cooper, Mittal, Vikram
Structuring Cross-Disciplinary Development: A Systems Engineering Framework for ASPICE-Compliant Mechatronic and Cyber-Physical Engineering2026-01-50254/2/2026
Mechatronic and cyber-physical systems emerge from interdisciplinary design efforts, integrating software (SW), electronics, and mechanical components. Developing such systems places high demands on organizations and processes, particularly regarding efficient collaboration across domains. A key challenge lies in establishing organizational structures and workflows which allow cross-discipline work and at the same time ensure compliance with regulations and adherence to standards such as Automotive Software Process Improvement and Capability Determination (ASPICE). In response, the authors have developed an Engineering Process Framework (EPF) grounded in International Council on Systems Engineering (INCOSE) systems engineering principles. The EPF provides a structured approach for system development and therefore defines company-wide processes and methods. This paper presents the development of the EPF’s functional logic and its implementation within a tool landscape. Furthermore, a
Gehrt, Jan-Jöran, Granrath, Christian, Caglayan, Ebru, Reckeweg, Thomas, Richert, Felix
The Study on Alternative Switching Noise Caused by Pulsating Magnetic Field Off Direct Current Ripple Induced by the Invertor Switching2026-01-50223/30/2026
As already well-understood/enormous engineering practices, the inverter AC-side NVH phenomena/mechanisms/measures for motor-equipped vehicle, are already pretty clear. In addition to inverter AC side–induced NVH issues, DC ripple induced by PE switching leads to NVH issues manifesting on the capacitor, inductor, and conductor in terms of reverse piezoelectricity, electrostriction, magnetostriction, Laplace force, and so forth. These DC-side NVH issues are already literally analyzed by a couple of literatures, and mechanisms/measures are explored/applied to electric drive development. And yet, the phenomenon that a pulsating magnetic field inside a battery pack induced by DC current ripple off PE switching brings noise at switching frequency inside the vehicle cabin is newly captured/analyzed by our research, and that has been barely searched during the literature survey. This newly discovered phenomenon is the pivotal point in this paper. Although the noise features like the
Zhao, Qian, Zhao, Yihan, Niu, Haolong, Li, Qiwei, Zhang, Wenchao, Xue, Hongbin, Cheng, Yanan, Li, Jing, Kang, Ming
SAE TOMORROW TODAY - SAE J3311: Smarter Vehicle Power Management135603/25/2026
When it comes to today's vehicles, every bit of energy matters. Wasted power can reduce EV driving range, add weight, and increase costs across the supply chain. That's where SAE Standard J3311, comes in. Instead of constantly running systems at full power -- or each automaker using its own proprietary strategy -- SAE J3311 promotes efficient, fine-tuned energy use across the entire vehicle. Listen in as we sit down with SAE J3311 committee members Donald Gignac, Automotive Solutions Architect, Silicon Mobility; Maria Soledad Elli, Sr. Data Scientist, Torc Robotics; and Simone Palombi, Senior Systems Engineer, General Motors, to discuss how creating a common, industry-wide approach to smarter power management can unlock longer range, lighter vehicles, lower costs, and faster innovation across EVs and internal combustion engines alike. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to
Patterson, Lori
SAE TOMORROW TODAY - Building an End-to-End Autonomous Driving System135573/2/2026
Join us as we sit down with Richard Chelminski, Senior Vice President, Head of SDV Platform, at 42dot, an autonomous driving software and mobility platform development startup from Hyundai Motor Group. We discuss how the company is pushing boundaries with its end-to-end autonomous driving system, Atria AI, which is focused on Level 4 autonomy and designed for seamless integration into consumer vehicles -- no LIDAR required! You'll also learn why camera-first and radar technologies are reshaping the industry, how software is now at the heart of vehicle development, and what the future holds for Transportation as a Service (TaaS). If you're curious about the next wave of smart, software-defined vehicles and autonomous driving innovation, this episode is a must-listen. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging
Patterson, Lori
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