Semi-active suspension systems enhance ride comfort and handling performance by adaptively modulating damping characteristics. However, conventional model-based controllers often fail to maintain optimal performance under uncertain and time-varying vehicle conditions. This article proposes Bayesian Optimization–Tuned Proximal Policy Optimization with Non-Parametric Rewards (BO-NRPPO), a novel reinforcement learning (RL) framework that integrates Bayesian Optimization (BO) with Proximal Policy Optimization (PPO) and a non-parametric reward function (NRF). The proposed approach enables adaptive self-tuning, data-driven reward shaping, and uncertainty-aware policy learning. Moreover, a Trapezoidal Simple Moving Average (TSMA)–based reward normalization scheme is introduced to accelerate convergence and stabilize training. Simulation results across diverse driving scenarios demonstrate that BO-NRPPO outperforms the passive suspension, the classical Linear Quadratic Regulator (LQR), and PPO

Chen, Guoying, Wang, Xinyu, Wang, Jiaqi, Zhan, Xinwang, Bi, Chenxiao, Cong, Shiqi, Hua, Min, Sun, Tianjun, Gao, Zhenhai

Spatial Optimisation of Inner Dash Acoustic Performance Using Reciprocal Holography

2026-01-0712

6/20/2026

As acoustic requirements for NVH trim components become increasingly constrained by mass, cost, and sustainability targets, traditional approaches to inner dash design based on spatially averaged Transmission Loss (TL) metrics are reaching their practical limits. In fully built vehicles, the acoustic performance of the inner dash is governed by its global insulation capability but also by strong spatial heterogeneity and its interaction with spatially distributed noise sources such as the power unit, gearbox, and tyre-road excitation. This paper presents a test-based methodology for the spatial optimisation of inner dash acoustic performance using reciprocal holography. By applying a calibrated sound power source within the vehicle cabin and measuring the reciprocal response in the engine bay and wheel-arch regions, a high-resolution spatial Transmission Loss “hologram” of the inner dash is obtained under in-situ conditions. The resulting spatial data enables the identification of

Harry, Evan, Eandi, Giacomo

Vibro-Acoustic Simulation for Vehicle Audio Design

2026-01-0670

6/20/2026

Acoustic user interfaces and audio experiences are among the leading comfort factors in new vehicle interior designs. OEMs are more and more focusing on loudspeaker design and positioning, to provide the most immersive experience to the customers. The industrial target is to be able to predict the performance of an audio system in early design phases. This paper presents an integrated vibro-acoustic methodology enabling early-stage prediction of loudspeaker performance in real vehicle conditions. The approach combines electromechanical characterization, a hybrid loudspeaker calibrated model valid across the audible range and coupled FEM/BEM/SEA simulations to capture the loudspeaker response in the vehicle’s cabin considering door-installation effects and cabin acoustics. The method is validated experimentally on a rear-door loudspeaker installed in a production vehicle, showing strong correlation with measured SPL. A final application case demonstrates its capability to assess the

Zerrad, Mehdi, Errico, Fabrizio, Mordillat, Philippe

Comparison of Detailed Chemistry and Flamelet-Based Models for Hydrogen Combustion in a Heavy-Duty PFI Engine

2026-37-0014

6/9/2026

Addressing climate change requires substantial reductions in CO2 emissions from the transportation sector, where alternative fuels for internal combustion engines play a crucial role. Hydrogen stands out as a compelling energy carrier capable of enabling low-carbon combustion while leveraging existing engine technologies. Its adoption can support a transition toward fuel-flexible powertrains and deliver rapid decreases in exhaust carbon emissions. This approach is particularly relevant for hard-to-abate segments, where full electrification remains challenging. Building on this perspective, this numerical study investigates the modelling behaviour of a heavy-duty port fuel injection (PFI) internal combustion engine fuelled with hydrogen. Initially, the mixture was assumed to be fully premixed to avoid uncertainties related to injection and mixing processes and to significantly reduce computational cost; this assumption was subsequently validated through selected injection simulations. A

Scopelliti, Alex, Misul, Daniela Anna, Baratta, Mirko, Gallo, Alessandro, Rapetto, Nicola, Vargiu, Luca

Leveraging True Altitude Data to Improve Static Pressure Calibration from Dynamic Manoeuvres

2026-26-0729

6/1/2026

The paper presents a method for enhancing the static pressure calibration of a high-performance aircraft. Despite the pre-flight calibration using CFD and Wind Tunnel techniques, position errors are generally observed in the free stream parameters, which necessitate further calibration of air data sensors using flight test data. In the present research, the pressure coefficient is estimated as a time-varying parameter in the flight path reconstruction environment implemented using the Extended Kalman Filtering technique. Aircraft kinematic equations were used for the implementation of the state and measurement models, and flight test data from full flight sorties were used in the estimation process. An extensive validation of the on-board air data calibration tables was conducted. Mean values of the static pressure coefficient were updated using data from multiple sorties, each including computed mean errors from three independent sensors. A comparative analysis between the pre

TK, Khadeeja Nusrath, Patel, Dr. Ambalal V, J, Prabhavathi Bhai

Reducing Test Campaigns through Advanced Yield Surface Modeling for New Aircraft Metal Grades

2026-26-0755

6/1/2026

Qualification of new aerospace alloys requires extensive mechanical testing to capture anisotropy and ensure reliable performance under complex loading conditions. This process is costly and time-consuming, particularly with emerging manufacturing routes such as additive manufacturing. Advanced yield surface prediction offers a route to reduce test campaigns by linking microstructural features to macroscopic constitutive models. In this work, Digimat is employed as a multi-scale material modeling platform to generate yield surfaces of polycrystalline metals using computational homogenization. Representative volume elements (RVEs) are constructed from experimental texture and grain morphology data, and their response under multiaxial loading is simulated using a crystal plasticity framework. The computed yield loci are then fitted with phenomenological functions (e.g. Yld2000-2D), enabling calibration of anisotropic yield models from virtual testing. As a case study, an AA6016-T4 sheet

Padhan, Manas, Uppaluri, Rohith, Lemoine, Guerric, Soni, Ganesh

An Integrated Workflow for Structural Dynamics, Comfort, and Airbag Deployment for Virtual Seat Design

F-0082-2026-0329

5/5/2026

This paper presents an integrated simulation workflow for aircraft seat development that combines (i) structural dynamics and certification load cases, (ii) occupant comfort and living-space assessment using finite-element digital humans, and (iii) airbag folding, deployment, and calibration using a coupled gas-dynamics solver suited to early-time transients. The workflow is built around a single manufacturing-aware, as-built seat model that is reused across comfort, certification, and restraint-system studies, allowing design iterations to move upstream before design freeze. Each stage is paired with validation or industrial case examples, and the airbag-calibration process is accelerated through reduced-order modeling (ROM) of parameter identification. The result is a practical virtual-seat-development methodology that is sufficiently predictive to de-risk physical testing while remaining fast enough for concept iteration and late-stage compliance support.

Dwarampudi, Ramesh, Vaz, Ignatius

An Experimental and Numerical Correlation Study on the Bird Strike Response of a Helicopter Composite Cowling Structure

F-0082-2026-0279

5/5/2026

The bird strike performance of the flight critical components of a rotorcraft is to be proved. The study investigates the bird strike performance of the cowling structure through experiments and simulations by considering a Building Block Approach. Based on this approach, bird impact tests on a rigid plate and composite panels are performed to validate Smoothed Particle Hydrodynamics method (SPH) bird model and composite material model in LS-DYNA. The composite material properties are obtained from the coupon level test results. After the composite material model is calibrated and validated, the bird strike performance of the cowling structure at critical locations is assessed. A good correlation between the experimental and numerical results was obtained at coupon, sub-component and component levels. The developed composite material modeling technique and validated bird models may be used in showing bird resistances of other airframe components of similar structure of the rotorcraft.

Kambur, Çağdaş, Bayhan, Mesut

Adaptive Outer-Loop Control of Quadrotors via Reinforcement Learning

F-0082-2026-0298

5/5/2026

Deep Reinforcement Learning (DRL) for quadrotor flight control typically relies on Domain Randomization (DR) for sim-to-real transfer, resulting in overly conservative policies that struggle with dynamic disturbances. To overcome this, we propose a novel adaptive control architecture that actively perceives and reacts to instantaneous perturbations. First, we train an optimal outer-loop policy, then replace its reliance on ground-truth disturbance data with a Residual Dynamics Predictor (RDP). The RDP estimates the external forces and moments acting on the aircraft in flight online using only the history of states and control actions. For seamless hardware transfer, we introduce a data-efficient linear calibration bridge and an online thrust correction mechanism that align the simulated latent space with reality using mere seconds of flight data. Real-world validations on a Crazyflie micro-quadrotor demonstrate that our adaptive controller significantly outperforms baselines

Saj, Vishnu, Benedict, Moble, Kalathil, Dileep, Vemuri, Sushil

Machine Learning–Driven Safety Analysis of Automated Vehicles in Simulated Mixed Traffic Environments

09-14-01-0021

5/4/2026

Automated Vehicles (AV) pose new challenges in road safety, multimodal interaction, and urban planning, requiring a holistic approach that prioritizes sustainability and protects all road users. The KASSA.AST project addresses this by deploying and evaluating an automated shuttle in southern Austria on three routes. The study area is a Park & Ride zone near a train station, enabling seamless transfers and higher transit use. To assess the safety impacts of the automated shuttle, four Mobility Observation Boxes (MOBs) were deployed. These AI-based systems detect and classify road users, track their trajectories and geospatial coordinates, and identify safety-critical events via Surrogate Safety Measures (SSMs). Over 10 days, a trajectory dataset captured interactions among vehicles and the shuttle. The resulting real-world dataset is a core contribution. This dataset underpins microscopic behavior modeling. Trajectory pairs yield car-following and interaction metrics (relative distance

Losada Arias, Ángel, Rosenkranz, Paul, Hula, Andreas, Aleksa, Michael, Saleh, Peter, Erdelean, Isabela

Integrated Design and Validation of a Robust Lane Centering Controller for Automated Driving

2026-01-0037

4/7/2026

Lane centering is a critical active safety feature whose effectiveness depends on robust design and validation across diverse driving conditions. This paper presents the development of a Lane Centering Controller (LCC) using a structured model-based design workflow in MATLAB and Simulink. A kinematic bicycle model was employed to simulate vehicle dynamics and evaluate an angle based steering controller integrating both feedforward and feedback control paths. The controller was tested across multiple road geometries and speeds up to 65 mph to ensure tracking consistency and stability under nominal and perturbed conditions. Perception noise models for lane curvature and curvature rate were extracted from onboard camera data under controlled conditions, revealing Gaussian characteristics. No filtering was applied, allowing direct evaluation of the controller’s inherent robustness to raw signal variability. The LCC maintained a peak lateral offset within ±0.35 m and lateral jerk within ±9

Bijinepalli, Ravi Teja, Tambolkar, Pooja, Midlam-Mohler, Shawn

The Stellantis North America Aero-Acoustic Wind Tunnel Upgrade

2026-01-0609

4/7/2026

The Stellantis North America Aero-Acoustic Wind Tunnel (AAWT) has been upgraded with a cutting-edge 5-belt Moving Ground Plane (MGP) system, featuring an 8.5-meter center belt and four Wheel Spinning Unit (WSU) belts with advanced coatings for durability and visibility. The expanded 9.4-meter turntable enables ±90° yaw and supports vehicles with wheelbases from 1800 mm to 4500 mm and weights up to 5000 kg, accommodating the full Stellantis North America product range. The original 2-stage boundary layer control system was retained, with new tertiary slots added for improved flow quality. A high-stiffness, six-component Horiba balance with integrated calibration weights and tractive force measurement ensures accurate and precise measurements. Facility enhancements include a 550 m2 building addition for equipment and vehicle prep, a dedicated compressor container for clean air supply, and a vehicle underbody wash booth for efficient cleaning. Commissioning confirmed that flow quality

Lounsberry, Todd, Ladouceur, Brent, Fadler, Gregory

Simulation Study on Rear-Wheel Steering Control of FSAE Vehicles Based on Temporal Convolutional Network

2026-01-0650

4/7/2026

The Formula SAE (FSAE) race track is characterized by a large number of corners, making cornering performance a key factor affecting lap time. Based on the proportional control strategy for rear-wheel steering angles, this paper proposes a steering angle optimization method using a Temporal Convolutional Network (TCN). The TCN model features a faster training speed than traditional sequential neural networks. In addition, dilated convolutions enable an exponential expansion of the receptive field without increasing computational costs, making it particularly suitable for capturing the temporal dependencies of vehicle states. By processing vehicle dynamic parameters including front-wheel steering angle, vehicle speed, yaw rate and sideslip angle, the model calculates the correction value of the rear-wheel steering angle. This correction value is then superimposed with the reference value of the rear-wheel steering angle derived from the proportional control strategy, which serves as the

Liu, Xiyuan

Data-Driven Calibration of Vehicle Simulations for Accurate Control Response in Development and Testing

2026-01-0058

4/7/2026

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

Backward Fuzzy Driving Control for 6×4 Off-Road Vehicles

2026-01-0148

4/7/2026

Off-road autonomous vehicle systems must be able to operate across unstructured and variable terrain while avoiding obstacles. This presents significant challenges in vehicle and control system design, especially for less conventional platforms such as 6×4 vehicles. While forward driving autonomy has developed and matured in recent years, effective reverse navigation remains an under-explored area of vehicle co-design. Reversing 6×4 vehicles have limited rear steering authority, an extended wheelbase, and asymmetric traction, which introduce complex dynamics into any control system that is used. To address this need, a robust and experimentally validated fuzzy logic control architecture for 6×4 reverse navigation was developed during the course of this project. This architecture incorporates both near-field and long-range path data with adaptive outputs controlling steering and velocity based on a rule base that covers the whole vehicle state space. This method has low computational

Dekhterman, Samuel R., Sreenivas, Ramavarapu S., Norris, William R., Patterson, Albert E., Soylemezoglu, Ahmet, Nottage, Dustin

Multi-Objective Reinforcement Learning Framework for Transmission Shift Schedule Optimization

2026-01-0162

4/7/2026

Building upon previous work that successfully employed a Reinforcement Learning (RL) agent for the autonomous optimization of transmission shift programs to enhance fuel efficiency, this paper addresses a critical limitation of that approach: the neglect of human-centric factors. While the prior methodology achieved substantial fuel consumption reductions by training an RL agent in a Software-in-the-Loop (SiL) environment, it did not explicitly account for aspects such as driver comfort and preferences, which are paramount for real-world user acceptance and drivability. This work presents a multi-objective optimization framework extending the artificial calibrator to simultaneously maximize fuel efficiency and enhance driver comfort. The method introduces a modified RL reward function that penalizes undesirable shift behavior to ensure a smooth driving experience (drivability). This new methodology also incorporates a mechanism to capture and integrate driver preferences, moving beyond

Kengne Dzegou, Thierry Junior, Schober, Florian, Rebesberger, Ron, Henze, Roman, Sturm, Axel

Lap Time Performance Sensitivity to Tire Model Parameters: An Optimal Control Framework for Model Selection and Calibration

2026-01-0214

4/7/2026

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

A Real-Time Spatio-Temporal Configuration Framework for Shared Autonomous Electric Vehicle (SAEV) Fleets Integrating Deep Learning and Multi-Agent Simulation

2026-01-0035

4/7/2026

Shared Autonomous Electric Vehicles (SAEVs) can enhance urban mobility and efficiency. However, their operational performance is often hindered by the spatio-temporal imbalance between vehicle supply and passenger demand, leading to long wait times. This paper develops a novel repositioning framework where a lightweight CNN, informed by computationally intensive multi-agent simulations, enables real-time strategy deployment. The results show that: (1) An optimized repositioning policy, calibrated via multi-agent simulation, effectively cuts the mean passenger waiting time from 12.0 to 3.0 minutes (a 75% reduction). (2) A lightweight CNN surrogate model enables real-time deployment, reducing the policy computation time from ~4 hours to ~5 minutes (>98% faster). (3) The deep learning surrogate achieves this speed with a negligible performance trade-off, increasing the waiting time by only 0.156 minutes (4.9%) compared to the full optimization.

Shang, Kai, Wang, Ning

MBSE-Driven XIL Workflow for Energy Evaluation of Automated Vehicles: From Simulation to VIL Testing

2026-01-0289

4/7/2026

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

Mathematical Modeling of Trust Calibration for Human–Automation Safety

2026-01-0530

4/7/2026

Trust calibration is vital for safe human–automation interaction but remains largely qualitative. This study develops multiple quantitative frameworks modeling trust as a function of automation reliability. Four progressive models of binary, linear, triangular, and logistic formalize the calibrated trust zone, defining where human reliance aligns with system performance. The framework corrects major misconceptions: that trust is purely qualitative, that low trust–low reliability states are acceptable, and that overtrust and distrust pose equal risk. It establishes a minimum reliability threshold for meaningful trust and identifies distrust as the safer default in high-risk contexts. A case study on an empirical observation of 32 AI applications plotted in the trust–reliability space confirms the analysis, revealing a consistent distrust tendency where reliability exceeds user confidence and other observations. By quantifying trust through reliability, the study reframes it as a

Wen, He, Mounir, Adil

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

Development of an ‘Online’ Combustion System Calibration Method using a Non-Dominated Sorting Genetic Algorithm

2026-01-0285

4/7/2026

Calibration is a major resource bottleneck and source of risk in powertrain technology development. A promising alternative to the typical design-of-experiments (DoE) approach is the use of a ‘Non-Dominated Sorting Genetic Algorithm’ (NSGA) calibration method, where an iterative process is used to directly identify the Pareto Fronts between performance metrics, for example, net mean effective pressure (NMEP) and NOx emission. The goal of the present work was to develop and demonstrate a fully ‘online’ combustion system calibration method based on an NSGA, where the algorithm operates directly on experimental data rather than empirical models as is typical in the literature. This was completed by first designing an optimal NSGA for combustion system calibration and then demonstrating its use for an experimental combustion system calibration on a single cylinder gasoline engine at one operating condition. Results from the design process here indicate that ‘online’ NSGAs have a strong

Mansfield, Andrew

In-Situ Raman Measurements of Fuel Dilution of Lubricant Films in a Direct-Injection Spark-Ignition Engine

2026-01-0355

4/7/2026

To measure the fuel proportion within the lubricant film, an in-situ Raman spectroscopy technique was employed in a specially modified single-cylinder direct-injection spark-ignition engine. The engine block was engineered for optical access with a fused silica window, enabling a focused laser beam to probe the lubricant film on the engine liner under motoring conditions. The lubricant used was GTL8 base oil with ZDDP additive, and iso-octane was injected as a model fuel to study fuel-lubricant mixing. A calibration curve was established by recording Raman spectra of known mixtures of GTL8 oil and iso-octane. The Raman intensity ratio of the iso-octane peak to the oil peak was used as a quantitative indicator of fuel concentration. During engine operation, Raman spectra were acquired in real time, on a cycle-by-cycle basis, through the optical window. Upon iso-octane injection, its characteristic Raman peak appeared in the spectrum, and the intensity ratio was referenced against the

Bolle, Bastien, Augoye, Kobi, Wong, Janet, Aleiferis, Pavlos, Hall, Jonathan, Bassett, Mike, Cracknell, Roger

System-Level Performance of Torque Vectoring Controllers for Electric Vehicles with In-Wheel Motors

10-10-04-0029

3/13/2026

Torque Vectoring (TV) is a critical control technology for enhancing the vehicle dynamics and stability of electric vehicles equipped with four-wheel-independent-drive (4WID) systems. A central challenge in TV design is managing the trade-off between maximizing handling performance and minimizing energy consumption, a crucial factor for EV range. While numerous advanced TV control strategies have been proposed, a comprehensive and comparative benchmark of foundational controllers evaluated on a platform that captures this trade-off is notably absent from the literature. Among the numerous TV control strategies proposed in literature, they are typically evaluated using simplified vehicle models that neglect the detailed dynamics and efficiency losses of the electric powertrain. This study addresses this gap by presenting a comprehensive comparison of six distinct TV control strategies—PID, LQR, two first-order Sliding Mode Controls (SMC), and two second-order SMCs. The controllers are

de Carvalho Pinheiro, Henrique, Carello, Massimiliana

Genetic Algorithm Based Optimization of Chaboche Kinematic Hardening Parameters along with the Validation of FE Model through Characterized Crack Initiation

2026-28-0086

2/12/2026

In the context of electro-mobility for commercial vehicles, the failure analysis of a connector panel in a DCDC converter is crucial, particularly regarding crack initiation at the interface of busbar and plastic component. This analysis requires a thorough understanding of thermo-mechanical behavior under thermal cyclic loads, necessitating kinematic hardening material modeling to account for the Bauschinger effect. As low cycle fatigue (LCF) test data is not available for glass fiber reinforced polyamide based thermoplastic composite (PA66GF), we have adopted a novel approach of determining non-linear Chaboche Non-Linear Kinematic Hardening (NLK) model parameters from monotonic uniaxial temperature dependent tensile test data of PA66GF. In this proposed work a detailed discussion has been presented on manual calibration and Genetic Algorithm (GA) based optimization of Chaboche parameters. Due to lack of fiber orientation dependent test data for PA66GF, here von Mises yield criteria

Basu, Parichay, Srinivasappa, Naveen

RAVEN Proprotor Performance and Aeroelastic Stability Predictions

SM-2026-VLADA-5167

1/27/2026

This paper details comprehensive analysis modeling and analysis supporting the development of the Research Aircraft for eVTOL Enabling techNologies (RAVEN). An isolated rotor model was developed in CAMRAD II, and predictions of rotor performance and rotor aeroelastic stability were generated. The rotor stability predictions are part of assessing airworthiness of the RAVEN vehicle. The performance predictions were used to calibrate the surrogate model for the NASA Design of Rotorcraft (NDARC).

Wright, Stephen, Silva, Christopher

Johnson-Cook Model Calibration for Brittle Materials under Impact Loads

2026-26-0367

1/16/2026

In the assessment of parts subjected to impact loading, the current process relies on static analysis, which overlooks the significant influence of high strain rate on material hardening and damage. The omission of these effects hinders accurate impact simulations, limiting the analysis to comparative studies of two components and potentially misidentifying critical hot spot locations. To address these limitations, this study emphasizes the importance of incorporating the effects of high strain rate in impact simulations. By utilizing the Johnson-Cook material calibration model, which includes both material hardening and damage models, a more comprehensive understanding of material behavior under dynamic loading conditions can be achieved. The Johnson-Cook material hardening model accounts for the strain rate sensitivity of the material, providing an accurate representation of its behavior under high strain rate conditions. This allows for improved prediction of material response

Pratap, Rajat, Apte, Sr., Amol, Babar, Ranjit, Dudhane, Karan, Poosarla, Shirdi Partha Sai, Tikhe, Omkar

Development of a Software Algorithm Based Semi-Automated Data Acquisition System for Gasoline Vehicle Calibration on Chassis Dynamometer

2026-26-0535

1/16/2026

Vehicle level EMS tuning is one of the crucial parts of calibration development. In this, vehicle level data is collected by using chassis dynamometer. Main objective of this data collection is to log the engine and vehicle level parameters at various speed and load conditions, covering the entire engine operational zone. This data acquisition process includes verification of base calibration, transient calibration and emissions-related calibration. Due to multiple number of similar acquisition steps this process becomes repetitive in nature and it covers 30-40% of the total calibration duration. All these measurements follow a standardized and repetitive sequence. However, these tasks are predominantly performed manually, leading to potential human error and fatigue. This paper presents a novel and comprehensive algorithm developed using INCA FLOW software; the first of its kind for this application. Here, a systematic development approach is used. First, the crucial vehicle data

Kavekar, Pratap Chandrashekhar, Tyagarajan, Sethuramalingam, Agarwal, Nishant Kumar, Shaikh, Wasim, Karadi, Subramanya

Accelerating EV Thermal Controller Development Using Rapid Control Prototyping on Speedgoat

2026-26-0685

1/16/2026

In the era of Software Defined Vehicles, the complexity and requirements of automotive systems have increased knowingly. EV Thermal management systems have become more complicated while having multiple functions and control strategies within software frameworks. This shift creates new challenges like increased development efforts and long lead time in creating an efficient thermal management system for Electric Vehicles (EV’s) due to battery charging and discharging cycles. For solving these challenges in the early stages of development makes it even more challenging due to the unavailability of key components such as fully developed ECU hardware, High voltage battery pack and the motor. To address this, a novel framework has been designed that combines virtual simulation with physical emulation at the same time, enabling the testing and validation of thermal control strategies without fully matured system and the ECU hardware. The framework uses the Speedgoat QNX machine as the

Chothave, Abhijeet, S, Bharathan, S, Ananth, Gangwar, Adarsh, Khan, Parvej, Gummadi, Gopakishore, Kumar, Dipesh

Multiphysics Analysis of Heat Sink Designs for Efficient Thermal Performance in Power Electronics Systems

2026-26-0446

1/16/2026

The thermal management capability of power electronic (PE) systems has a critical impact on the performance and efficiency of electric, fuel cell, or hybrid vehicles. Bus bars, high resistance sensor devices, semiconductor switches, power capacitors are the primary components, which make a major contribution in total heat generation in electrical drive unit. As PE packaging sizes are projected to become smaller, the challenge of managing increased heat dissipation becomes more critical. This paper numerically compares six different cooling strategies to determine the best possible thermal management scenario. A coupled physics co-simulation framework is used to analyze a 35W motor inverter integrated with water cooled heat sink. A multi-physics finite element model, integrating fluid, electrical, and thermal fields, is employed to analyze heat generation within the PE system and the associated cooling mechanisms. The power losses from the inverter system are dynamically computed in 1-D

Singh, Praveen Kumar, Natarajan, Nesamani, Murali, Sariki

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

Thermal Management and Engine Calibration Strategies for Enhanced Passive DPF Regeneration in CEV Stage V CI Engines

2026-26-0141

1/16/2026

Meeting the stringent emissions norms of CEV stage V for medium BMEP engines, CI engines present significant challenges. These stringent norms call for a highly efficient DPF. With the increasing demands for high-performance DPFs, the issue of soot accumulation and cleaning presents significant hurdles for DPF longevity. This paper explores the potential of passive DPF regeneration, which leverages naturally occurring exhaust gas conditions to oxidize accumulated soot, offering a promising approach to minimize fuel penalty and system complexity compared to active regeneration methods. The study investigates engine calibration techniques aimed at enhancing passive regeneration performance, emphasizing the optimization of thermal management strategies to sustain DPF temperatures within the passive regeneration range. Furthermore, the paper aims to expand the applicability of passive regeneration across diverse engine loads common in off-highway applications with effective passive

Saxena, Harshit, Gandhi, Naresh, Lokare, Prasad, Shinde, Prashant, Patil, Ajit, Raut, Ashish

A Novel Approach to Optimal Strain Gauge Placement for Structural Assessment: Integrating Looping Configurations with Calibration Techniques

2026-26-0551

1/16/2026

Addressing the challenge of optimal strain gauge placement on complex structural joints and pipes, this research introduces a novel methodology combining strategic gauge configurations with numerical optimization techniques. Traditional methods often struggle to accurately capture combined loading states and real-world complexities, leading to measurement errors and flawed structural assessments [9]. For intricate joints, a looping strain gauge configuration is proposed to comprehensively capture both bending and torsional effects, preventing the bypassing of applied loads. A calibration technique is used to create strain distribution matrices and access structural behavior under different loading conditions. Optimization algorithms are then applied to identify gauge placements that yield well-conditioned matrices, minimizing measurement errors and enhancing data reliability. This approach offers a cost-effective solution by reducing the number of gauges required for accurate stress

Shingate, Uttam, Yadav, Dnyaneshwar, Deshpande, Onkar

Model Standardization for MBD throughout Entire Power Unit Development Process

2026-26-0428

1/16/2026

The distribution of mobility equipped with electrified power units is advancing towards carbon-neutral society. The electrified power units require an integration of numerous hardware components and large-scale software to optimize high-performance system. Additionally, a value-enhancement cycle of mobility needs to be accelerated more than ever. The challenge is to achieve high-quality performance and high-efficient development using Model-Based Development (MBD). The development process based on V-model has been applied to electrified power units in passenger vehicle. Traditionally, MBD has been primarily utilized in the left bank (performance design phase) of the V-model for power unit development. MBD in performance design phase has been widely implemented in research and development because it refines prototype performance and reduces the number of prototypes. However, applying the MBD to an entire power unit development process from performance design phase to performance

Ogata, Kenichiro, Katsuura, Akihiro, Tsuji, Minako, Matsumoto, Takumi, Iwase, Hiromu, Nakasako, Seiya, Takahata, Motoki

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

Accurate Component Load Prediction with Hybrid Test-FEA Approach

2026-26-0366

1/16/2026

Accurately determining the loads acting on a structure is critical for simulation tasks, especially in fatigue analysis. However, current methods for determining component loads using load cascade techniques and multi-body dynamics (MBD) simulation models have intrinsic accuracy constraints because of approximations and measurement uncertainties. Moreover, constructing precise MBD models is a time-consuming process, resulting in long turnaround times. Consequently, there is a pressing need for a more direct and precise approach to component load estimation that reduces efforts and time while enhancing accuracy. A novel solution has emerged to tackle these requirements by leveraging the structure itself as a load transducer [1]. Previous efforts in this direction faced challenges associated with cross-talk issues, but those obstacles have been overcome with the introduction of the "pseudo-inverse" concept. By combining the pseudo-inverse technique with the D-optimal algorithm

Pratap, Rajat, Apte, Sr., Amol, Babar, Ranjit

WITHDRAWAL NOTICE

2026-26-0567.01

1/16/2026

AI Based Models as Virtual Sensors to Replace Real Sensors

2026-26-0671

1/16/2026

Artificial Intelligence and Machine learning models have a large scope and application in Automotive embedded systems. These models are used in the automotive world for various applications like calibration, simulation, predictions, etc. These models are generally very accurate and play the role of a virtual sensor. However, the AI/ML models are resource intensive which makes them difficult to execute on largely optimized automotive embedded systems. The models also need to follow safety standards like ASIL-D. The current work involves creating a Global DoE with ETAS ASCMO to generate data from a 125cc single to create AI/ML model for the engine outputs like Torque, T3, Mid-cat temperatures etc. The created models were validated across the operating space of the engine and found to have good accuracies. With ETAS Embedded AI Coder, the torque and T3 prediction AI models were converted to embedded code which can be easily used as a virtual sensor in real time. Using these AI models

Chouhan, Vineet Singh, Bulandani, Saurabh, Kumar, Alok, Varsha, Anuroopa, P R, Renjith

Calibration Strategy to Address Abnormal Engine Noise in Cold Transient Driving Condition

2026-26-0585

1/16/2026

Passenger vehicle users often manoeuvre their cars in diverse and unpredictable driving patterns. The vast and varied terrain of the Indian subcontinent further complicates this scenario, introducing unique challenges due to differences in driving expertise, vehicle usage, and environmental conditions. A specific challenge addressed in this paper arises during different engine temperatures and transient driving conditions—a critical phase for engine calibration to ensure optimal drivability and emissions performance. With current calibration practices, a backfire like abnormal engine noise was observed during certain transient driving patterns. This paper presents a novel calibration methodology designed to eliminate such abnormal noise. The proposed approach involves a step-by-step transient calibration refinement, making the calibration process more robust and adaptable to any driving behaviour. The paper outlines the specific challenges encountered and details the multi-level

Suna, Bhagyashree, Tyagarajan, Sethuramalingam, Pise, Chetan, Aishwarya, Amritansh

Research and Implementation of a Neural Radiance Fields-Based 3D Reconstruction Method for Autonomous Driving Scenes

2025-01-7310

12/31/2025

This paper addresses the scarcity of training and testing data in autonomous driving scenarios. We propose a 3D reconstruction framework for autonomous driving scenes based on Neural Radiance Fields (NeRF). Compared to traditional multi-view geometry methods, NeRF offers superior scene representation and novel view synthesis capabilities but suffers from low training efficiency and limited generalization. To overcome these limitations, we integrate existing NeRF optimization techniques and introduce a scene-specific data reuse strategy tailored for autonomous driving, enabling continuous 3D reconstruction directly from 2D images without requiring elaborate calibration. This approach significantly improves reconstruction efficiency, achieving reliable reconstruction and real-time visualization in complex traffic environments. Furthermore, we develop an interactive scene editing plugin in Unreal Engine 5, supporting the addition, removal, and adjustment of static objects. This extension

Pan, Deng, Zou, Jie, Chen, Yuhan, Meng, Zhangjie, Li, Jie, Li, Guofa

Research on Early Warning Standard for Deformation of Surrounding Rock in Nearby New Four-Lane Tunnels

2025-99-0208

12/23/2025

Focusing on the deformation warning criteria for a new four-lane tunnel affected by an existing tunnel, this study employs numerical simulation to analyze the ultimate strain of the equivalent rock mass. The results reveal the ultimate shear strain and ultimate tensile strain of Class V surrounding rock, offering critical insights for deformation control and early warning systems. Relying on the Maaoling Tunnel Project, the tunnel planar analysis model is established based on the finite difference FLAC3D software to analyze the deformation and strain distribution pattern of the surrounding rock of the new tunnel under different distances and reduction factors between the new and the existing tunnel. Finally, the tunnel crown settlement as an indicator, the establishment of the Maaoling Tunnel V surrounding rock conditions of different distances construction safety warning standard for the construction of large-span tunnels and early warning provides the basis for the relevant

Zhang, Yufan, Tian, Wei, Liu, Dongxing, Kang, Xiaoyue, Chen, Liming, Zheng, Xiaoqing

PSF-Net: Uncertainty-Aware Fusion of TabPFN and SAINT for 5G Base-Station Electromagnetic Radiation Prediction

2025-99-0127

11/11/2025

With more 5G base stations coming into play, making an accurate assessment of RF-EMF exposure currently faces increasing demand to check if they meet regulatory requirements and ensure people’s safety. We present here PSF-Net, a novel deep learning network by uniting TabPFN’s meta-learned prior knowledge and SAINT’s dual attention structure; its use makes it particularly suitable to deal with applications like prediction of downlink power density and radiation level classification under different conditions within various kinds of 5G cell. A major component in the design of this approach is an uncertainty-aware gating block that determines the optimal weighting for each model output—TabPFN or SAINT—based on the estimated prediction variance as quantified via Monte Carlo sampling during training or the prediction variance calculated using inference-time dropout. In addition, a residual multi-layer perceptron (MLP) is also included to extract refined fused features and maintain a steady

Zhang, Yanjin, Yu, Zefeng

A Load Cell-Free Solution for Weighing

2025-28-0346

11/6/2025

Large farms cultivating forage crops for the dairy and livestock sectors require high-quality, dense bales with substantial nutritional value. The storage of hay becomes essential during the colder winter months when grass growth and field conditions are unsuitable for animal grazing. Bale weight serves as a critical parameter for assessing field yields, managing inventory, and facilitating fair trade within the industry. The agricultural sector increasingly demands innovative solutions to enhance efficiency and productivity while minimizing the overhead costs associated with advanced systems. Recent weighing system solutions rely heavily on load cells mounted inside baling machines, adding extra costs, complexity and weight to the equipment. This paper addresses the need to mitigate these issues by implementing an advanced model-based weighing system that operates without the use of load cells, specifically designed for round baler machines. The weighing solution utilizes mathematical

Kadam, Pankaj

Advanced Compliance Risk Assessment Using Big Data from Fleet Intelligence Data

2025-28-0343

11/6/2025

In-Use emission compliance regulations globally mandate that machines meet emission standards in the field, beyond dyno certification. For engine manufacturers, understanding emission compliance risks early is crucial for technology selection, calibration strategies, and validation routines. This study focuses on developing analytical and statistical methods for emission compliance risk assessment using Fleet Intelligence Data, which includes high-frequency telematics data from over 500K machines, reporting more than 1000 measures at 1Hz frequency. Traditional analytical methods are inadequate for handling such big data, necessitating advanced methods. We developed data pipelines to query measures from the Enterprise Data Lake (A Structured Data storage system), address big data challenges, and ensure data quality. Regulatory requirements were translated into software logic and applied to pre-processed data for emission compliance assessment. The resulting reports provide actionable

Arya, Satya Prakash, Shekarappa, Kiran

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