This SAE Aerospace Recommended Practice (ARP) provides the user with standardized guidelines for the measurement of effective intensity of short pulse width strobe anticollision lights for aircraft in the laboratory, in maintenance facilities, and in the field. A common source of traceability for calibration of the measurement systems, compensation for known causes of variation in light output such as the use of colored lenses, and recommendations which minimize sources of errors and uncertainties are included in this document. Estimates of uncertainty and error sources for each class of measurement are discussed.

A-20B Exterior Lighting Committee

Testing of Rigid Two- and Three-Wheeled Vehicle Pitch-Over Threshold Models

2026-01-0538

4/7/2026

The phenomenon of bicycle pitch-over is simple in concept, yet determining threshold criteria for pitch-over has yet to be well established, particularly with respect to determining whether or not a bicycle’s front wheel will roll over a particular obstacle or not. Two prior SAE papers have laid out two different analytical approaches to predict this threshold – the Moment-Inversion and Brach Pitch-Over Threshold models - and this paper proposes a modification to the Moment-Inversion model to account for tire deflection. Testing began by measuring the center of gravity locations and moments of inertia for a bicycle with weights and training wheels and for a test rider on a bicycle and tricycle. These physical measurements were used to calculate the predicted pitch-over height for each system for each model. The test systems were then ridden over a series of progressively taller square edge obstacles until they transitioned from rolling over to stopping or pitching over. From this

Sweet, David Michael, O'Brien, Nathan, Bretting, Gerald

Impedance Characterization of an Automotive Battery Pack

2026-01-0381

4/7/2026

Parasitic inductance and capacitance of the battery pack can affect the performance of the electric powertrains. Characterizing these parasitic phenomena in an automotive battery pack is therefore crucial to ensuring performance and reliability. In this work, geometric models of a production automotive battery pack are developed to simulate the parasitic inductance of the busbar system, the parasitic inductance of individual modules, and other critical components. For these simulations, several assumptions and simplifications are introduced to reduce model complexity, while preserving the main electromagnetic behavior of the system. The impact of the different components on the battery pack impedance is investigated to evaluate parasitic capacitances, thereby simulating the worst-case scenario. Laboratory procedures are developed to accurately measure parasitic impedance, providing a reliable comparison between experimental data and analytical models and supporting the overall validity

Misley, Marco, D'Arpino, Matilde, Zhu, Di, Zhang, Liwen

The Development of an Experimental Methodology for the Measurement of Aeroelastic Components

2026-01-0648

4/7/2026

In high-end motorsport engineering, aerodynamic devices such as front and rear wings are prone to aeroelastic deformations under certain conditions, which can be exploited for vehicle performance gains. Considering the complex interactions between the aerodynamics and structures, experimental evaluation can prove to be a time-effective approach for design, optimisation, research and development regarding aeroelastic bodies. This study presents the development and experimental validation of a deformation tracking system using depth-sensing LiDAR (Light Detection and Ranging) camera technology. The system is based on the use of reflective markers mounted on a given model of interest; this project, a front wing model with a flexible, 3D printed flap element was used as a benchmark. Surface deformation is captured by post-processing point cloud data to extract three-dimensional displacement vectors. A series of controlled measurement tests were first conducted to assess accuracy and

Altinbas, Koray, Soares, Renan F.

Self-Supervised Gaussian-Based Framework for Robust Static-Dynamic Decomposition and Real-Time Urban Scene Reconstruction

2026-01-0010

4/7/2026

High-fidelity 3D reconstruction of large-scale urban scenes is critical for autonomous driving perception and simulation. Existing neural rendering methods, including NeRF and Gaussian-based variants, often face challenges like unstable geometry, noisy motion segmentation, and poor performance under sparse viewpoints or varying illumination. This paper presents a self-supervised Gaussian-based framework to address these challenges, enabling robust static–dynamic decomposition and real-time scene reconstruction. The proposed method introduces three innovations: (1) a semantic–geometric feature fusion module that combines semantic context and geometric cues for reliable motion prior estimation; (2) a cross-sequence geometric consistency constraint that enforces depth and surface continuity across time and viewpoints; (3) an efficient Gaussian parameter optimization strategy that stabilizes geometry by jointly constraining scale and normal updates. Experiments on the Waymo Open Dataset

Feng, Runlei, Wang, Ning, Zhang, Zhihao

Simulation Study on a Neural Tire Model Based on Grouped Feature Learning

2026-01-0645

4/7/2026

The tire model is a crucial component in the design of the K-characteristic of FSAE racing car suspensions, and directly influences the achievement of maximum cornering lateral force. Not only do the slip angle, vertical load, tire pressure, and camber angle affect the mechanical characteristics of the tire, but temperature is also an important influencing factor when FSAE vehicle tires operate at high speeds. However, the modeling process of traditional tire models based on temperature characteristics is often very complex. The FSAE tire test code (FSAE TTC) already has a large amount of official sample data, which provides a basis for data-driven neural network models. This study implemented a hybrid modeling methodology, constructing two cascaded feedforward neural networks that combine the physical interpretability of the Magic Formula tire model with the nonlinear approximation capabilities of neural networks. The first network model uses slip angle, vertical load, tire pressure

Liu, Xiyuan, Wang, Shenyao, Li, Mingyuan, Huang, Jiayu

Application of Reliability Growth Methods in Conjunction with Design for Reliability Framework to Validate Heavy Truck Design

2026-01-0137

4/7/2026

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

An Enhanced Canny-Based Vision System for Vanishing Point Detection in Camera Calibration and Sun Glare Mitigation for ADAS

2026-01-0016

4/7/2026

Edge detection is fundamental for intelligent vehicle applications, directly supporting ADAS functions such as lane detection, obstacle recognition, and scene understanding. The conventional Canny edge detection method exhibits notable shortcomings, especially in color-image processing, adaptive threshold selection, and preserving edge integrity under noisy conditions. In this study, we present an enhanced Canny edge detection framework tailored for ADAS-oriented intelligent vehicle systems, incorporating a quaternion-based weighted averaging scheme for color preservation, adaptive thresholds derived from gradient-amplitude histograms, multiscale edge localization via scale multiplication, and a novel gravitational-field-intensity operator for improved gradient robustness. Moreover, we extend the method to vanishing-point estimation an essential ADAS capability by performing precise intersection calculations combined with clustering techniques such as DBSCAN and RANSAC. Experimental

Uppala, Rohit Raj, Kaye, Murali, Zadeh, Mehrdad, Tan, Teik-Khoon

Methodology for Generating Off-Road Tracks for Powertrain Simulation of Off-Road Passenger Vehicles

2026-01-0275

4/7/2026

For off-road driving, particularly on steep grades and over barriers, the engine torque is a key design criterion of off-road vehicles. In conventional powertrains with combustion engines, mechanical all-wheel-drive systems combined with differential locks are used to distribute the torque demand between the front and the rear axle based on wheel-specific traction. With the growing market share of electric powertrains, off-road applications are becoming increasingly relevant for electric passenger cars. In comparison to conventional powertrains, electric all-wheel-drive configurations do not have a mechanical torque transfer between the two axles. If one axle experiences low traction, the second axle can rely on its own torque capability only. Transfer of unused torque of the slipping axle to the other one is not possible. The challenge, therefore, is to specify the right torque requirements for each axle for off-road driving while avoiding over-dimensioning and high powertrain costs

Martin, Michael, Winkelheide, Jonas, Hartmann, Lukas, Sturm, Axel, Henze, Roman

A Comparison of Road Grade Preview Signals from Lidar and Maps

2026-01-0026

4/7/2026

Road grade can impact the energy efficiency, safety, and comfort associated with automated vehicle control systems. Currently, control systems that attempt to compensate for road grade are designed with one of two assumptions. Either the grade is only known once the vehicle is driving over the road segment through proprioception, or complete knowledge of the oncoming road grade is known from a pre-made map. Both assumptions limit the performance of a control system, as not having a preview signal prevents proactive grade compensation, whereas relying only on map data potentially subjects the control system to missing or outdated information. These limits can be avoided by measuring the oncoming grade in real-time using on-board lidar sensors. In this work, we use point returns accumulated during travel to estimate the grade at each waypoint along a path. The estimated grade is defined as the difference in height between the front and rear wheelbase at a given waypoint. Kalman filtering

Schexnaydre, Logan, Poovalappil, Aman, Robinette, Darrell, Bos, Jeremy

From Static Ground to Moving Ground Wind Tunnels: Feature Selection for Reliable Estimation of Drag Coefficient Discrepancies

2026-01-0203

4/7/2026

Moving ground wind tunnels offer a more accurate test environment for ground vehicle drag coefficient measurement due to their highly realistic representation of the boundary layer phenomenon. However, historically most vehicles have been tested on static ground wind tunnels. As a result, the measured drag coefficient of these vehicles may not be sufficiently realistic for certification purposes. Therefore, it is valuable to build statistical models to estimate moving ground wind tunnel drag coefficient by using information from a static ground wind tunnel and other relevant vehicle characteristics such as presence of aerodynamic devices (spoilers, air dams, etc.). However, to build accurate statistical models, appropriate predictive features must be identified as a first step. In this paper, an aerodynamic feature selection study has been conducted to identify vehicle characteristics that contribute to drag coefficient estimation discrepancies between a static- and a moving ground

Singh, Yuvraj, Jayakumar, Adithya, Rizzoni, Giorgio

Automotive Crash Dynamics Modeling Accelerated with Machine Learning

2026-01-0568

4/7/2026

Crashworthiness assessment is a critical aspect of automotive design, traditionally relying on high-fidelity finite element (FE) simulations that are computationally expensive and time-consuming. This work presents an exploratory comparative study on developing machine learning-based surrogate models for efficient prediction of structural deformation in crash scenarios using the NVIDIA PhysicsNeMo framework. Given the limited prior work applying machine learning to structural crash dynamics, the primary contribution lies in demonstrating the feasibility and engineering utility of the various modeling approaches explored in this work. We investigate two state-of-the-art neural network architectures for modeling crash dynamics: MeshGraphNet, a graph neural network that is widely employed in physics-based simulations, and Transolver, a transformer-based architecture with a physics-aware attention mechanism designed to maintain linear computational complexity with respect to geometric

Nabian, Mohammad Amin, Chavare, Sudeep, Akhare, Deepak, Ranade, Rishikesh, Cherukuri, Ram, Tadepalli, Srinivas

Quantifying Accuracy and Robustness to Guide Fidelity Selection for Multi-Fidelity Vehicle Dynamics Models

2026-01-0149

4/7/2026

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

Experimental Validation of a Multi-Row Tapered Roller Bearing Analytical Model for Relating Preload to Frequency Response Characteristics

2026-01-0008

4/7/2026

Roller bearings are used in many rotating power transmission systems in the automotive industry. During the assembly process of the power transmission system, some types of roller bearings (e.g., tapered roller bearings) require a compressive preload force. Those bearings' rolling resistance and lifespan strongly depend on the preload set during the installation process. Therefore, accurate setting of the preload can improve bearing efficiency, increase bearing lifespan and reduce maintenance costs over the life of the vehicle. A new method for bearing preload measurement has shown potential for both high accuracy and fast cycle time using the frequency response characteristics of the power transmission system. An open problem is experimental validation of the multi-row tapered roller bearing analytical model. After validation, the analytical model can be used to predict the assembled system damped natural frequency for a desired bearing preload. This work presents the experimental

Gruzwalski, David, Mynderse, James

Defect Depth Characterization via Thermally and Pneumatically Induced Shearography for Component Inspection in the Automotive Sector

2026-01-0205

4/7/2026

The following approach introduces a novel method for defect depth characterization using digital Shearography, which is a non-contact, full-field, and material-independent optical interferometric method that enables fast and nondestructive testing (NDT) of components, especially in industrial environments such as the automotive sector. While traditional techniques like computed-tomography, ultrasonic-testing, or thermography can offer depth approximations but they often involve high costs, longer testing times, or limited accessibility. In contrast, the method introduced utilizes various excitation methods in combination with shearographic evaluation to derive procedures for depth estimation of subsurface defects. Recent developments in Shearography have enhanced the method’s robustness and industrial applicability. By detecting the surface deformation behavior in the nanometer range under defined loading, depth-related characteristics of hidden defects can be extracted. Loading can be

Bastgen, Valentin, Plaßmann, Jessica, Petry, Christopher, von Freymann, Georg, Schuth, Michael

Investigation of the Blockage Phenomenon in Closed Wall Wind Tunnels during the Testing of Bluff Automotive Bodies with Small Wakes in Yawed Configurations

2026-01-0607

4/7/2026

The difficulties of testing a bluff automotive body of sufficient scale to match the on-road vehicle Reynolds number in a closed wall wind tunnel has led to many approaches being taken to adjust the resulting data for the inherent interference effects. But it has been difficult to experimentally analyze the effects that are occurring on and around the vehicle when these blockage interferences are taking place. The present study is an extension of earlier works by the authors and similarly to those studies uses the computational fluid dynamics analysis of five bodies that generate small wakes to examine the interference phenomena in solid wall wind tunnels. This focuses on the effects on the pressures, and forces experienced by the vehicle model when it is in yawed conditions up to 20 degrees. This is accomplished by executing a series of CFD configurations with varying sized cross sections from approximately 0.4% to 14% blockage enabling an approximation of free air conditions as

Gleason, Mark, Riegel, Eugen

Conjugate Heat Transfer Modeling for Predicting Thermal Behavior of X76 Emotor Windings

2026-01-0118

4/7/2026

A computational study based on a conjugate heat transfer (CHT) method in SimericsMP+ was performed to predict the winding temperatures in an X76 emotor. In this study, the thermal load was represented in the simulation through the solution of electromagnetic equations in SimericsMP+, where heat generation was driven by root-mean-square (RMS) current, while liquid cooling was applied at flow rates ranging from 1 LPM to 6 LPM. Simulations were conducted to measure the temperature on three thermocouple locations on each side of the winding crown and weld regions under steady operation. The computational strategy employed a loosely coupled approach. A fluid-only simulation was first carried out to establish stable flow conditions, followed by coupling with solid conduction where the winding acted as the heat source. The predicted temperature distributions were then compared with test data. Results obtained show good agreement, with differences remaining within an acceptable range, thereby

Jia, Kun, Schlautman, Jeff, Srinivasan, Chiranth

Pre-Crash Scenario Analysis and Generation for Integrated Safety System Virtual Testing with Real Accident Data

2026-01-0062

4/7/2026

Integrated active and passive safety protection systems have made substantial contributions to reducing traffic accidents and mitigating human injuries. However, assessing such systems through vehicle collision tests is limited, as this approach cannot cover the wide range of accident scenarios. To address this gap, identifying and generating representative pre-crash scenarios from real-world accidents provides key boundary conditions for the setup of virtual test scenarios. In this study, we used the Future Mobile Traffic Accident Scenario Study (FASS) dataset to reconstruct 112 two-wheeler accidents. For each case, we extracted pre-crash dynamic information, static attributes, and environmental context. An autoencoder was employed to encode high-dimensional features of scenarios, and K-means clustering was applied to categorize the accidents into eight representative pre-crash scenarios. For each scenario, we examined the motion states of participants and further compared the

Wang, Guojie, Gao, Xin, Liu, Siyuan, Liu, Jiaxin, Li, Quan, Shi, Liangliang, Nie, Bingbing

AirCARE: Assessing the Impact of an Integrated Air Purification System on Vehicle Thermal Management

2026-01-0272

4/7/2026

The increasing concentration of atmospheric pollutants in urban environments necessitates innovative solutions to mitigate their impact on public health and the environment. This work presents the AirCARE project, which investigates the integration of a catalytic converter and a particulate filter with a vehicle's radiator to create an active air purification system. The primary objective is to evaluate the feasibility and performance implications of this integrated system on the vehicle's thermal management. A comprehensive methodology combining computational modeling and experimental testing was employed. A 1D longitudinal vehicle model was developed to simulate the powertrain's heat generation and the cooling system's performance under various representative driving conditions. This model allows for a parametric study of the radiator, assessing the impact of the additional components on its heat exchange efficiency. Concurrently, experimental tests were conducted on a radiator to

de Carvalho Pinheiro, Henrique, Sartoretti, Enrico

Thermal Management of Gasoline Fuel Tanks

2026-01-0114

4/7/2026

This study presents a fully integrated, vehicle-level thermal management model for gasoline fuel tanks, designed to predict transient fuel temperatures, tank wall heating, and vapor generation under real-world driving conditions. The model simulates coupled thermal contributions from exhaust radiation, transient underbody airflow, conductive heat transfer, in-tank pump heating, and dynamic changes in fuel composition and level. Validation against on-road measurements shows strong agreement for fuel temperature and vapor flow profiles. Results confirm that exhaust radiative heating is the dominant thermal load, particularly during the post-shutdown heat soak period. A well-designed heat shield reduced peak tank wall temperature by approximately 27 °C, significantly lowering fuel heating and evaporation. Parametric analysis indicates that while fuel Reid Vapor Pressure (RVP) and tank material influence evaporation, their effect is secondary to external heat mitigation. While this model

El-Sharkawy, Alaa, Asar, Mona, Taha, Nahla, Sheta, Mai

A Correction Method to Estimate Wake Effects for Aerodynamic Performance Metrics

2026-01-0601

4/7/2026

Wake effects modify the aerodynamic performance of a road vehicle when driving in traffic. Analysis of wind-tunnel measurements conducted in flows with wake characteristics, using a traffic-wake-simulation system, suggests that conventional uniform-wind performance coefficients can be scaled, using wake-flow-field information, to predict the influence of wake effects. This paper presents a flow-field-averaging method that estimates a dynamic-pressure correction and yaw-angle correction for application to uniform-wind data, to account for changes in performance due to wake effects. This first-order method is shown to provide reasonably-good accuracy when reverse correcting the wind-tunnel wake-effects measurements. Drag-coefficient data for light-duty-vehicle models, which showed wake effects exceeding 20%, were corrected to within 5% of uniform-wind values, while data for heavy-duty-vehicle models, which showed wake effects exceeding 15%, were corrected to within 2% of uniform-wind

McAuliffe, Brian

Aerodynamic Proximity Effects between the DrivAer and AeroSUV Standard Models, Part 1 – Same-Lane Interactions

2026-01-0605

4/7/2026

In this paper, the effects of aerodynamic interactions on the drag of a longitudinally-arranged two-vehicle system are examined by considering the influence of separation distance, cross winds, vehicle size and shape. Testing was undertaken at 30% scale in a large wind tunnel with road-representative freestream turbulence. Separation distances of 0.5, 1.0, and 2.0 vehicle lengths (L) were examined over a range of yaw angles between ±15°. A highlight of the current study is the characterization of platoon drag-reduction benefits for different sizes and shapes of the lead and follower models, by using a DrivAer model and an Aero-SUV model, each with slant-back (Notchback or Fastback) and square-back (Estateback) variants, providing four distinct model pairings. Drag reduction for the lead model appears to be affected mainly by the size of the follower model, while the follower model shows a much greater sensitivity to shape of the lead model. Larger drag reductions were observed at most

McAuliffe, Brian, Ghorbanishohrat, Faegheh

Data-Driven Objective Road-Driving Classification for Durable Design of Vehicles

2026-01-0181

4/7/2026

Design for durability in the automotive industry depends on a clear understanding of how road surfaces and driving characteristics affect structural road loads and fatigue. Traditionally, road surface classification has been subjective (e.g., city, highway, rural), and done through driving instrumented vehicles over a small selection of roads. The variations in driving characteristics that are often consequent to the road surface quality are rarely accounted for in designing vehicle level durability tests. This makes it difficult to establish targets for durability testing that accurately match the wide variations in real-world roads and driving. This paper presents a data-driven approach to objectively classify road surface and driving characteristics using metrics derived from existing road response metrics like Vibration Dose Value (VDV) and statistical estimates of vehicle speed and acceleration. Data collected at the proving grounds on gravel roads, smooth roads, city-like roads

Shaurya, Shubham, Ramakrishnan, Sankaran, Demiri, Albion, Khapane, Prashant

Digital Twin Aerodynamics Model Validation for HALO Wind Tunnel

2026-01-0610

4/7/2026

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

A New Analytical Method for Calculating Transient Lube Oil Consumption

2026-01-5018

3/6/2026

Oil consumption is a major concern for all engine manufacturers, both from an environmental and engine durability standpoint. Understanding how oil consumption is affected by key design parameters has traditionally been established during the validation phase of an engine development program using both steady-state and transient lube oil consumption (LOC) measurements. Cost and time pressures are driving this development to be performed virtually, where many more parameters can be assessed and understood prior to design verification testing. This paper presents a new analytical method that is capable of predicting transient phenomena of the ring pack that would not normally be captured by steady-state methods, providing a toolset that reduces engine development testing and cost and aid troubleshooting. Implemented in Realis1 RINGPAK, this new transient method has been validated against transient LOC measurements for a 2.0 L 4-cylinder GTDI engine. Different transient load/speed

Bell, David, Zhang, Shasha, Shen, Cong, Tisch, Dan, Brezina, Michal, Huang, Yun

A Thin-Film Flexible Temperature-Strain Multimodal Sensor for Lithium-Ion Battery Monitoring Based of Laser Direct Writing

2026-01-7026

2/27/2026

Multimodal sensors, capable of simultaneously acquiring multiple physical or chemical signals, have shown broad application potential in fields such as health monitoring, soft robotics, and energy systems. However, current multimodal sensors often suffer from complex fabrication processes and signal decoupling challenges, which limit their practical deployment. To address these issues, this work presents a thin-film temperature–strain multimodal sensor (FTSMS) fabricated via laser processing. The temperature-sensing unit, based on the Seebeck effect, achieves a sensitivity of 9.08 μV/°C, while the strain-sensing unit, utilizing BaTiO₃/AlN@PDMS as the sensitive layer, exhibits a gauge factor (GF) of 43.2. By integrating distinct sensing mechanisms (thermovoltage for temperature and capacitance change for strain), the FTSMS enables self-decoupled measurements over 20–90 °C. Applied in LIB monitoring, it successfully captures real-time temperature and strain variations during charge

Wang, Ziwei, Li, Zhenglin, Gao, Yang, Xuan, Fuzhen

Experimental Vibroacoustic Measurement of Thin Steel Panels Coated with Different Liquid-Applied Spray Damping Layer Thickness

10-10-02-0017

2/23/2026

This study investigates the effect of liquid-applied spray damping (LASD) thickness on the vibration and sound radiation of thin steel panels. Although LASD is widely used to enhance structural damping, its influence on radiated sound and the role of coating thickness have not been systematically studied. Five steel panels with varying LASD thicknesses were evaluated using two experimental approaches. An impact-based method in a hemi-anechoic chamber measured the structural mobility and noise transfer functions, while a reciprocal method in a reverberation chamber under acoustic excitation measured the radiated sound power transfer function. A thickness ratio was found beyond which additional LASD thickness yielded diminishing improvements in noise and vibration reductions. The effect of LASD thickness on radiation efficiency was also assessed in both narrowband and one-third octave bands.

Neihguk, David, Suh, Sam, Herrin, David W.

Experimental Investigations on Appendages of a Racing Motorcycle

2026-01-5011

2/20/2026

In this experimental work, a detailed analysis of the wind tunnel measurements on scaled motorbike models equipped with different front wings was performed considering four wing configurations operating at different Reynolds numbers and roll angles. Global forces acting on the models were measured by a high-resolution dynamometric balance, while velocity fields in the wake were measured by means of the Particle Image Velocimetry technique. Throughout the paper, overall models’ performances are investigated, demonstrating similar behavior for drag coefficients and various trends for lift coefficients. The without- and single-wing configurations were shown to have positive sign, and conversely, the double- and closed-wing cases—with negative sign—generated downforce due to the presence of significant upward velocities, which in turn modified the wake shape. Furthermore, the improvements in closed-wing configuration compared to without- and single-wing ones were noticeable, while slight

Moscato, Giorgio, Romano, Giovanni Paolo

AI Smart Timer-Based Power Control and Energy Monitoring System for Home Appliances

2026-28-0060

2/12/2026

The paper presents the design and implementation of an AI-enabled smart timer-based power control and energy monitoring solution for household appliances. The proposed system integrates real-time sensing of electrical device parameters with cloud artificial intelligence for predictive analytics and automatic control. Continuous measurement of voltage, current and power consumption of the connected appliances are performed for analysis of the usage patterns. The appliance operation is completely automated by choosing between the best option which is the user-defined schedule or the load shifted schedule recommended by AI. The AI recommendation depends on peak demand of the day and the current load requirement thereby aiding approximate smoothening of daily load curve and improving load factor. The data collected is transmitted to the cloud for real-time and historical data collection, for prediction of consumption patterns, anomaly detection, and clustering appliances according to their

D, Anitha, D, Suchitra, Jain, Utsav, Maity, Souvik, Dinda, Atish

SUPPLIER EYE

26AUTP02_08

2/1/2026

Recent geopolitical events in Venezuela, Ukraine and other hot spots are a stark reminder that the long-term planning environment is fraught with challenges and opportunities that suppliers cannot control. The initiation of U.S. tariffs on its trading partners and various embargos also underscored that we have to be flexible in how we dole out capital and the risk we are assuming. The supply base is at the end of that chain. Any issues upstream will reverberate exponentially. It is obvious that the automotive world is re-regionalizing, and quickly. Why the concern? Some context. Until the '70s, every region essentially rowed its own boat. While there were some exports from one major region to another, there were regional OEMs that were sponsored by national governments due to job creation, tax base considerations and bragging rights. The U.S, France, Italy, Germany, Japan, South Korea and a host of others wanted to build national OEMs that could drive scale and become a global force.

Rewriting the engineer's playbook: What OEMs must do to spin the AI flywheel

26AUTP02_04

2/1/2026

The automotive industry's future hinges on a new AI-native engineering workflow that accelerates iteration, strengthens system thinking, and preserves human judgment. Automotive development cycles are compressing at a pace the industry has never seen. The shift to all-electric fleets of software-defined vehicles is moving faster than traditional processes can absorb. In parallel, regulatory pressure and customer expectations keep rising, demanding greater performance, higher safety, better energy efficiency, and sharper competitiveness. In this environment, OEMs R&D competitiveness depends on three factors: How quickly teams can explore and iterate on design choices while delivering differentiated value, product performance, and cost efficiency. How early system-level interactions can be detected, before they turn into delivery friction or costly late-stage failures. How effectively a company can encode and scale its internal engineering know-how into lean development processes.

Allard, Théophile

Comprehensive Evaluation of Bolster Behaviour: Field Data Insights for Design Modifications

2026-26-0497

1/16/2026

Bogie suspension systems are becoming increasingly popular in tipper vehicles to enhance their performance and durability, especially in demanding environments like construction and mining areas [1]. Bolsters contribute significantly to the overall performance and durability of the bogie suspension systems of tipper vehicles by evenly distributing the loads across the whole suspension system. They act as shock absorbers and negate the impact caused by the rough terrains and heavy loads, thereby reducing stress on individual components and maintaining the structural integrity of the vehicle. Bolsters also help in improving the ride comfort and to maintain the position of the suspension system [2]. This study focuses on the comprehensive testing and evaluation of bolsters to understand their modes and displacement data derived from field data. The primary objective is to analyse the performance and behaviour of bolsters under various operational conditions. Critical manners of

V Dhage, Yogesh, Kolage, Vikas

Reliability of Rubber Grommets by Stochastic Analysis of Geometric Tolerances

2026-26-0501

1/16/2026

Manufacturing tolerances play a critical role in the quality and functionality of components, particularly those made from rubber. Even slight deviations in dimensions can cause significant issues such as improper fit and reduced performance, leading to increased costs and project delays. This is especially true for rubber grommets, which are nonlinear elastic components commonly used as sealants, gaskets, and insulation covers in automotive and industrial applications. Typically manufactured from EPDM rubber with varying Shore hardness, grommets must maintain precise geometry to ensure sealing integrity and protect adjacent parts. Dimensional inaccuracies can result in failures such as buckling or misalignment, compromising both functionality and durability. This study proposes a digital simulation methodology for early-stage evaluation of grommet robustness, reducing reliance on physical prototypes. Using a stochastic design of experiments (DOE) approach, the influence of critical

Beesetti, Siva, Hattarke, Mallikarjun, James Aricatt, John, Pathan, Eram

Optimizing Vehicle Dynamics: A Methodology for Reducing Correlation Gaps

2026-26-0091

1/16/2026

Final design choices are frequently made early in the product development cycle in the fiercely competitive automotive sector. However, because of manufacturing tolerances design tolerances stiffness element fitment and other noise factors physical prototypes might show variations from nominal specifications. Significant performance differences (correlation gaps) between the digital twin representation produced during the design phase and real-world performance may result from these deviations. Measuring every system parameter repeatedly to take these variations into account can be expensive and impractical. The goal of this study is to identify important system parameters from system characteristic data produced by controlled dynamic testing to close the gap between digital and physical models. Dynamic load cases are carried out with a 4-poster test rig where vehicle responses are captured under controlled circumstances at different suspension locations. An ideal set of digital model

Verma, Rahul Ranjan, Goli, Naga Aswani Kumar, Prasad, Tej Pratap

Nexifi11D: A Comprehensive Multidimensional Framework for Futuristic Manufacturing Ecosystems

2026-26-0282

1/16/2026

This paper presents Nexifi11D, a simulation-driven, real-time Digital Twin framework that models and demonstrates eleven critical dimensions of a futuristic manufacturing ecosystem. Developed using Unity for 3D simulation, Python for orchestration and AI inference, Prometheus for real-time metric capture, and Grafana for dynamic visualization, the system functions both as a live testbed and a scalable industrial prototype. To handle the complexity of real-world manufacturing data, the current model uses simulation to emulate dynamic shopfloor scenarios; however, it is architected for direct integration with physical assets via industry-standard edge protocols such as MQTT, OPC UA, and RESTful APIs. This enables seamless bi-directional data flow between the factory floor and the digital environment. Nexifi11D implements 3D spatial modeling of multi-type motor flow across machines and conveyors; 4D machine state transitions (idle, processing, waiting, downtime); 5D operational cost

Kumar, Rahul, Singh, Randhir

A Case Study of Emission Optimization using Machine Learning

2026-26-0229

1/16/2026

The Exhaust Emission Control is a vital part of automotive development aimed at ensuring effective control of pollutants such as NOx, CO, and HC. The traditional method of calibrating emission control strategies is a highly time-consuming process, which requires extensive vehicle testing under a variety of operating conditions. The frequent updates in emission legislation requires a high-efficiency process to achieve a faster time-to-market. The use of Machine Learning (ML) in the domain of emission calibration is the need of the hour to proactively improve the process efficiency and achieve a faster time-to-market. This paper attempts to explores emerging trend of Machine Learning (ML) based data analysis that have improved the overall process efficiency of emission control calibration. The data generated by automated programs could be used directly in data analysis with minimal or no need for data cleaning. The Machine Learning (ML) models could be trained by historical data from

Dhayanidhi, Hukumdeen, Balasubramanian, Karthick, A, Akash

Effects of Battery Integration in Modal Behaviour of Electric Two Wheeler Frame

2026-26-0310

1/16/2026

This research investigates the dynamic characteristics of an electric two-wheeler chassis through a combined experimental and numerical approach, and understands the contribution of battery towards overall behaviour of the frame in a structural manner. The study commences with the development of a detailed CAD model, which serves as the basis for Finite Element Analysis (FEA) to predict the chassis's natural frequencies and mode shapes. These numerical simulations offer initial insights into the structural vibration behavior crucial for ensuring vehicle stability and rider comfort. To validate the FEA predictions, experimental modal analysis is performed on a physical prototype of the electric two-wheeler chassis using impact hammer excitation. Multiple response measurements are acquired via accelerometers, and the resulting data is processed to extract experimental modal parameters. The correlation between the simulated and experimental mode shapes is quantitatively assessed using the

Das Sharma, Aritrya, Iyer, Siddharth, Prasad, Sathish, Anandh, Sudheep

A Method to Improve the System Efficiency of an Electronic Drive Unit: Variable Switching Frequency-Loss Optimization

2026-26-0062

1/16/2026

The design and improvement of electric motor and inverter systems is crucial for numerous industrial applications in electrical engineering. Accurately quantifying the amount of power lost during operation is a substantial challenge, despite the flexibility and widespread usage of these systems. Although it is typically used to assess the system’s efficiency, this does not adequately explain how or why power outages occur within these systems. This paper presents a new way to study power losses without focusing on efficiency. The goal is to explore and analyze the complex reasons behind power losses in both inverters and electric motors. The goal of this methodology is to systematically analyze the effect of the switching frequency on current ripple under varying operating conditions (i.e., different combinations of current and speed) and subsequently identify the optimum switching frequency for each case. In the end, the paper creates a complete model for understanding power losses

Banda, Gururaj, Sengar, Bhan

Development of an Optimized XCP Software Stack for Real-Time Validation of Vehicle Control Units Using Next-Generation Communication Protocols

2026-26-0190

1/16/2026

With the rapid adoption of electric vehicles (EVs), ensuring the reliability, safety, and cost-effectiveness of power electronic subsystems such as onboard chargers, DC-DC converters, and vehicle control units (VCUs) has become a critical engineering focus. These components require thorough validation using precise calibration and communication protocols. This paper presents the development and implementation of an optimized software stack for the Universal Measurement and Calibration Protocol (XCP), aimed at real-time validation of VCUs using next-generation communication methods such as CAN, CAN-FD, and Ethernet. The stack facilitates read/write access to the ECU’s internal memory in runtime, enabling efficient diagnostics, calibration, and parameter tuning without hardware modifications. It is designed to be modular, platform-independent, and compatible with microcontrollers across different EV platforms. By utilizing the ASAM-compliant protocol architecture, the proposed system

Uthaman, Sreekumar

26SIAT-1032-Experimental Frequency-Based Substructuring for Full-Vehicle NVH Characterization in Electric Vehicles

2026-26-0309

1/16/2026

The area of electric vehicles (EV) has fully arrived with almost every OEM enhancing electric vehicles in their portfolio. However, regarding its business potential numerous challenging engineering questions have risen. Especially vehicle NVH development needs to be rethought as masking noise from classical internal combustion engines (ICE) are gone. At the same time the frequency content of electric engines falls in the best human audible range, creating high potential for annoying tonal acoustic issues. With NVH design requirements now pushed up into the kilohertz range, many classic development strategies fail or lack efficiency. VIBES Technology’s answer to this challenge is what we call Hybrid Modular Modelling (HMM). This modelling strategy combines test-based and numerical simulation throughout the vehicle development cycle. Using best of both worlds, HMM allows accurate virtual (part / system) design and optimization on full vehicle level. Here HMM is based on the latest

Kohlhofer, Daniel, Pingle, Pawan Sharad, de Klerk, Dennis

Machine Learning Approach to ISO Road Class Prediction Using Multiple Linear Regression

2026-26-0667

1/16/2026

The inertial profiler methodology is traditionally employed in RLDA (Road Load Data Acquisition) to measure road profiles and classify test routes into ISO road classes. However, this approach demands significant time and effort during instrumentation. Also, during data acquisition, laser height sensor data is affected especially during adverse conditions such as rainy seasons or on surfaces with improper reflectivity. Additionally, substantial resources are required for data processing to convert raw measurements into road classifications. To address these challenges, an initial attempt was made to establish a relationship between axle acceleration responses and road profiles, enabling axle acceleration measurements during RLDA to predict ISO road classes. However, this approach relied on a simple linear model that considered only axle acceleration responses, rendering the predictions susceptible to inaccuracies due to varying parameters such as vehicle speed. To overcome these

P, Praveen Kumar, P, Dayalan, Sriramulu, Yoganandam

Development of a Fuel Injector Driver for Accelerated Testing

2026-26-0564

1/16/2026

This paper presents the design of a cost-effective fuel injector driver designed for accelerated testing of injectors. The driver simulates injection patterns across a wide range of vehicle operating conditions and can be programmed with injection maps for different engines, test cycles based on drawing specifications, pre-defined engine running profiles, and manual control, where the user defines PWM frequency and duty cycle. It also enables remote operation through a Wi Fi access point. An injector driver-based test setup was developed to study wear and evaluate leakage tendency in an injector design. To simulate extended field usage in a short timeframe, an accelerated operating cycle was derived using telematics data. Injector samples were tested with periodic leak rate measurements. Conducting such tests at vehicle level or on engine test bench would involve significant time and cost. This setup is an effective tool for rapid comparative analysis across supplier design, enabling

Bhatt, Pancham, Agrawal, Adheesh, Kuchhal, Abhinav

Determining Optimal Suspension & Body in White Mountings to maximize Off-Road Capability of Passenger Vehicles

2026-26-0098

1/16/2026

The automotive market trend is shifting more and more to SUVs and crossovers. This, therefore, means increasing consumer demand for off-road abilities in passenger vehicles. While dedicated off-road platforms provide a path to performance robustness, getting the same level of functionality out of a passenger vehicle with minimal architectural changes proves to be a great feat for engineers. One highly critical performance determinant in the domain of off-road ability is wheel articulation, it requires independent movement capacity of the wheels to keep contact and stability over uneven terrain. Traditional articulations found in passenger car suspensions—created for comfort, packaging, and on-road dynamics—are limited by suspension geometry, damper alignment as well as compliance setup. Damper side loads- were not considered a significant factor in suspension systems that are operating within their original intended design envelope for on-road use. However, when the vehicle is taken

Siddiqui, Arshad, Iqbal, Shoaib, Dwivedi, Sushil

Durability Validation of Full Vehicle Structures Using Strain Correlation and RLDA-Based Simulations

2026-26-0523

1/16/2026

Durability validation of full vehicle structures is crucial to ensure long-term performance and structural integrity under real-world loading conditions. Physical test strain and finite element (FE) strain correlation is vital for accurate fatigue damage predictions. During torture track testing of the prototype vehicle, wheel center loads were measured using wheel force transducers (WFTs). In same prototype strain time histories were recorded at critical structural locations using strain gauges. Preliminary FE analysis was carried out to find out critical stress locations, which provided the basis for placement of strain gauges. Measured loads at wheel centers were then used in Multi Body Dynamics (MBD) simulations to calculate the loads at all suspension mount points on BIW. Using the loads at hard points transient analyses were performed to find out structural stress response. Strain outputs from the FE model were compared with physical measurements. Insights gained from these

Jaju, Mayur, Dokhale, Sandeep, Gadre, Nilesh, Patil, Sanjay

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