Browse Topic: Calibration

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Comparison of Detailed Chemistry and Flamelet-Based Models for Hydrogen Combustion in a Heavy-Duty PFI Engine2026-37-0014To be published on 06/09/2026
Addressing climate change requires substantial reductions in emissions from the transportation sector, where alternative fuels for internal combustion engines play a crucial role. Among these options, 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 such as heavy-duty transportation, 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
Scopelliti, Alex, Misul, Daniela Anna, Baratta, Mirko, Gallo, Alessandro, Rapetto, Nicola, Vargiu, Luca
Leveraging True Altitude Data to Improve Static Pressure Calibration from Dynamic Manoeuvres2026-26-0729To be published on 06/01/2026
Accurate calibration of aircraft air data systems is essential for reliable measurement of flow angles, altitude, and airspeed, particularly during dynamic flight manoeuvres where errors can become significant. Externally mounted vanes and probes are used to measure flow angles, local pressures and temperature, respectively. However, because these sensors are positioned close to the aircraft fuselage, they operate within disturbed local flow fields that differ notably from the free-stream conditions. As a result, their outputs require Position Error Corrections (PEC) to accurately estimate free-stream parameters such as pressure, temperature, and flow angles. The pre-flight PEC data or calibration tables for these sensors are generated based on the CFD predictions/wind tunnel experiments. All the flight-measured local variables from air data sensors need to pass through the air data computers, which contain these lookup tables, to obtain the free stream information. Despite the pre
TK, Khadeeja Nusrath, Patel, Dr. Ambalal V, J, Prabhavathi Bhai
Reducing Test Campaigns through Advanced Yield Surface Modeling for New Aircraft Metal Grades2026-26-0755To be published on 06/01/2026
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. Barlat2000), 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
Data-Driven Calibration of Vehicle Simulations for Accurate Control Response in Development and Testing2026-01-00584/7/2026
With the steady increase in autonomous driving (AD) and advanced driver-assistance systems (ADAS) aimed at improving road safety and navigation efficiency, simulation tools have become a critical part of the development process, allowing systems to be tested while mitigating the risk of physical injury or property damage upon failure. Physics-based simulators are central to virtual vehicle development, yet their control responses often differ from real vehicles, potentially limiting the transfer of controllers and algorithms developed in simulation. As these simulations play an important role in the vehicle design and validation process, a critical question is how well their predicted behavior translates to real-world physical systems. This paper presents a calibration framework for an autonomous vehicle platform that learns the motion characteristics of an experimental vehicle and uses that knowledge to correct the actuator response of a simulation model. The model is trained by
Soloiu, Valentin, Sutton, Timothy, Mehrzed, Shaen, Lange, Robin, Zimmerman, Charles, Peralta Lopez, Guillermo
Simulation Study on Rear-Wheel Steering Control of FSAE Vehicles Based on Temporal Convolutional Network2026-01-06504/7/2026
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
The Stellantis North America Aero-Acoustic Wind Tunnel Upgrade2026-01-06094/7/2026
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
Backward Fuzzy Driving Control for 6×4 Off-Road Vehicles2026-01-01484/7/2026
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 Optimization2026-01-01624/7/2026
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 Calibration2026-01-02144/7/2026
The push for vehicle development through virtual prototyping and testing in motorsports highlights the critical challenge of tire model selection and calibration, especially when vehicle dynamics must be accurately captured. The calibration process for tire models such as the Pacejka Magic Formula (MF) relies on parameter identification and experimental data fitting. While optimization algorithms have been implemented to calibrate tire models, few studies explore the effects of parameter selection on overall vehicle performance, complicating prioritization for the vehicle’s modeling and simulation strategy. To bridge this gap, this paper leverages optimal control methods to quantify how the variability of MF tire model parameters propagates to the overall vehicle model and impacts lap time prediction accuracy. To achieve this, a subset of parameters critical to combined slip of the MF tire model are varied through a Design of Experiments (DOE). These variations are executed on a flat
Zarate Villazon, Angel M., Brown, Ian, Balchanos, Michael, Mavris, Dimitri
A Real-Time Spatio-Temporal Configuration Framework for Shared Autonomous Electric Vehicle (SAEV) Fleets Integrating Deep Learning and Multi-Agent Simulation2026-01-00354/7/2026
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 Testing2026-01-02894/7/2026
With the increasing market penetration of automated vehicles, there is a critical need for credible and repeatable methods to quantify their energy impacts. This paper presents a Model-Based Systems Engineering (MBSE)-driven Anything-in-the-Loop (XIL) methodology for quantifying the powertrain energy consumption and potential savings from various controls for automated vehicles in realistic road scenarios while preserving high-fidelity powertrain behavior. The novelty of this approach lies in its use of a unified MBSE backbone (AMBER: Argonne National Laboratory’s [Argonne’s] MBSE-centric platform for transportation energy analysis) to automate the seamless and traceable progression from pure simulation to Vehicle-in-the-Loop (VIL) testing. This work utilizes Argonne's multi-vehicle simulation tool, RoadRunner, which automatically constructs closed-loop road scenarios (road geometry, vehicle sensors, other vehicles, and traffic controls) and connects them to Argonne’s validated, high
Jeong, Jongryeol, Sharer, Phillip, Di Russo, Miriam, Das, Debashis, Zhang, Yaozhong, Karbowski, Dominik
Mathematical Modeling of Trust Calibration for Human–Automation Safety2026-01-05304/7/2026
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
Integrated Design and Validation of a Robust Lane Centering Controller for Automated Driving2026-01-00374/7/2026
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 static conditions, revealing both Gaussian and non-Gaussian characteristics. No filtering was applied, allowing direct evaluation of the controller’s inherent robustness to raw-signal variability. The LCC maintained a mean lateral offset within ±0.35
Bijinepalli, Ravi Teja, Tambolkar, Pooja, Midlam-Mohler, Shawn
Defect Depth Characterization via Thermally and Pneumatically Induced Shearography for Component Inspection in the Automotive Sector2026-01-02054/7/2026
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 Algorithm2026-01-02854/7/2026
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 Engine2026-01-03554/7/2026
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 Motors10-10-04-00293/13/2026
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 Initiation2026-28-00862/12/2026
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
Accelerating EV Thermal Controller Development Using Rapid Control Prototyping on Speedgoat2026-26-06851/16/2026
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
Model Standardization for MBD throughout Entire Power Unit Development Process2026-26-04281/16/2026
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 Learning2026-26-02291/16/2026
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
Thermal Management and Engine Calibration Strategies for Enhanced Passive DPF Regeneration in CEV Stage V CI Engines2026-26-01411/16/2026
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 Techniques2026-26-05511/16/2026
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
Accurate Component Load Prediction with Hybrid Test-FEA Approach2026-26-03661/16/2026
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
Development of a Software Algorithm Based Semi-Automated Data Acquisition System for Gasoline Vehicle Calibration on Chassis Dynamometer2026-26-05351/16/2026
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
Multiphysics Analysis of Heat Sink Designs for Efficient Thermal Performance in Power Electronics Systems2026-26-04461/16/2026
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
WITHDRAWAL NOTICE2026-26-0567.011/16/2026
AI Based Models as Virtual Sensors to Replace Real Sensors2026-26-06711/16/2026
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
Development of an Optimized XCP Software Stack for Real-Time Validation of Vehicle Control Units Using Next-Generation Communication Protocols2026-26-01901/16/2026
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
Calibration Strategy to Address Abnormal Engine Noise in Cold Transient Driving Condition2026-26-05851/16/2026
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
Johnson-Cook Model Calibration for Brittle Materials under Impact Loads2026-26-03671/16/2026
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
Research and Implementation of a Neural Radiance Fields-Based 3D Reconstruction Method for Autonomous Driving Scenes2025-01-731012/31/2025
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 Tunnels2025-99-020812/23/2025
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 Prediction2025-99-012711/11/2025
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 Weighing2025-28-034611/6/2025
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 Data2025-28-034311/6/2025
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
Development of an Artificial Calibrator for Shift Program Optimization to Enhance Fuel Efficiency2025-32-008211/3/2025
The calibration of automotive electronic control units is a critical and resource-intensive task in modern powertrain development. Optimizing parameters such as transmission shift schedules for minimum fuel consumption traditionally requires extensive prototype testing by expert calibrators. This process is costly, time-consuming, and subject to variability in environmental conditions and human judgment. In this paper, an artificial calibrator is introduced – a software agent that autonomously tunes transmission shift maps using reinforcement learning (RL) in a Software-in-the-Loop (SiL) simulation environment. The RL-based calibrator explores shift schedule parameters and learns from fuel consumption feedback, thereby achieving objective and reproducible optimizations within the controlled SiL environment. Applied to a 7-speed dual-clutch transmission (DCT) model of a Mild Hybrid Electric Vehicle (MHEV), the approach yielded significant fuel efficiency improvements. In a case study on
Kengne Dzegou, Thierry Junior, Schober, Florian, Rebesberger, Ron, Henze, Roman
Automated Calibration of Heavy-Duty Low NO x Aftertreatment System Controls using Deep Learning and Global Optimization Methods2025-01-040210/7/2025
Heavy duty diesel engines provide a robust power plant for transportation applications for both on highway and off road applications. Control of criteria pollutants such as particulate matter and NOx at tailpipe for these applications based on standards set by regulatory bodies such CARB and EPA is critical. SwRI has demonstrated capability to achieve 0.02 g/bhp-hr. tailpipe NOx standard through the application of a model based controls in EPA and CARB funded projects. This control mechanism enables precise urea dosing for both steady state and transient conditions by leveraging estimated ammonia storage state in a dual dosing system using a set of chemical kinetics-based SCR observer models. This controller is highly nonlinear, with a significant amount of controller tuning with up to 55 calibratable parameters. In order to improve the accuracy and reduce the time required for calibration of this controller, this work proposes the deployment of a Deep Learning-based SCR plant model in
Chundru, Venkata Rajesh, Rajakumar Deshpande, Shreshta, Sharp, Christopher, Gankov, Stanislav
Multi-Objective Optimization of the Energy Management Strategy for Hybrid Electric Vehicles - Examination of Sensitivities between Fuel Consumption, Electric Driving and Driving Comfort2025-24-01039/7/2025
The combination of the electric drive and the internal combustion engine (ICE) in hybrid electric vehicles (HEV) requires the implementation of an Energy Management Strategy (EMS). The task of the EMS is to split the driving demand between the two energy converters. The design of the EMS in charge-sustaining operation is commonly targeted at the minimization of fuel consumption. For in-vehicle implementation of the EMS, supplementary objectives, such as the electric driving (ED) experience or the driving comfort, influenced by the frequency of state shifts, are considered. Therefore, this work extends the framework for EMS optimization from the fuel-optimal design to multi-objective target spaces. First, the general multi-objective optimal control problem (MOOCP) is formulated. In a next step the central target space for EMS calibration consisting of fuel consumption, ED time and number of ICE starts is considered and the resulting MOOCP is solved using Dynamic Programming (DP). The
Ehrenberg, Bastian, Engbroks, Lukas, Schmiedler, Stefan, Geringer, Bernhard, Hofmann, Peter
Design of a Fault-Tolerant Control Algorithm for Four-Wheel Steering Vehicle Steering Locking Based on Dynamic Model Reconstruction10-09-04-00368/21/2025
The motion control system, as the core executive component of the automatic hierarchical framework, directly determines whether autonomous vehicles can reliably and stably follow planned trajectories, making it crucial for driving safety. This article focuses on steering lock faults and proposes a cross-system fault-tolerant control (C-FTC) algorithm based on dynamic model reconstruction. The algorithm uses a classic hierarchical collaborative architecture: the upper-level controller employs an MPC algorithm to solve lateral velocity and yaw rate reference values in real-time, while the lower-level controller, designed based on the reconstructed dynamic model, uses an MPC algorithm to adaptively adjust actuator control quantities. In cases where four-wheel steering vehicles lose steering ability due to locked steering axles, the locked axle’s steering angle is treated as a state variable, and healthy actuator outputs are used as control variables to dynamically reconstruct the vehicle
Hu, Hongyu, Tang, Minghong, Chen, Guoying, Gao, Zhenhai, Wang, Xinyu, Gao, Fei
Reducing Traffic Congestion with Reinforcement Learning-Driven Signal Control Systems2025-01-02837/2/2025
The road network is a critical component of modern urban mobility systems, with signalized traffic intersections playing a pivotal role. Traditionally, traffic light phase timings and durations at intersections are designed by transportation engineers using historical traffic data. Some modern intersections employ trigger-based mechanisms to improve traffic flow; however, these systems often lack global awareness of traffic conditions across multiple intersections within a network. With the increasing availability of traffic data and advancements in machine learning, traffic light systems can be enhanced by modeling them as agents operating in an environment. This paper proposes a Reinforcement Learning (RL) based approach for multi-agent traffic light systems within a simulation environment. The simulation is calibrated using real-world traffic data, enabling RL agents to learn effective control strategies based on realistic scenarios. A key advantage of using a calibrated simulation
Kalra, Vikhyat, Tulpule, Punit, Giuliani, Pio Michele
NVH Improvement of Transmission Electric Oil Pump with Uneven Pitch Control2025-01-02526/16/2025
This paper proposes an uneven pitch control for electric oil pumps. For the noise reduction of vane pumps, mechanical arrangements of uneven pitch vain angle are widely used. However, the tooth angle of gear-type pumps should be even mechanically. The proposed uneven pitch control provides similar effects of the mechanical uneven pitch arrangement by instantaneous motor torque controls of the electric oil pump which cannot have uneven pitch mechanically. The magnitude of motor torque for each pump tooth is determined by an uneven pitch formula which is widely used for mechanical vane pumps in previous study and patents. A formula for the shape of motor torque is proposed by analyzing pressure fluctuations of pump as a combination of trigonometric and exponential functions. The calibration factors for the magnitude and shape are adjusted by characteristics of pumps. The experimental results showed that noise reduction and dispersion effects of the proposed method.
Choi, Chinchul, Kim, Jongbeom
Static System Error Compensation in Air Data Calibration for Tiltrotor System CertificationF-0081-2025-01285/20/2025
ABSTRACT Air data measurement and calibration are fundamental components in the pursuit of accurate and reliable aerodynamic assessments. The systematic collection of essential data regarding air properties are important for evaluating aircraft performance under various conditions and configurations. The scope is to achieve a comprehensive understanding of airflow characteristics, which is fundamental for design improvements and operational strategies, contributing to safer and more efficient flight operations in a several range of scenarios. This type of data measurement is even more challenging for the AW609 Tiltrotor which combines vertical take-off technology capabilities with the fixed-wing flight efficiency. The activity starts from known pitot-static system calibration methodologies for conventional applications and shows what were the difficulties encountered in a non-conventional Tiltrotor approach. The paper goes through the presentation of the original Pitot-Static and Air
Evangelista, Marco, Mori, Massimiliano
Design of a Reactive-Style Muffler for a Formula SAE Race Car2025-01-00245/5/2025
The noise generated by high-performance vehicles like Formula SAE (FSAE) race cars, presents a significant challenge in adhering to strict competition noise regulations. In this study two muffler designs were created: muffler design 1 and 2. Each design utilized two chambers to generate destructive interference, targeting two dominant exhaust frequencies of the Honda CBR600RR engine to maximize transmission loss and reduce sound pressure levels (SPL) below the FSAE-mandated range of 103 dBC at idle and 110 dBC at all other operating conditions. For each design, the exhaust noise and muffler performance were simulated using GT-Suite, allowing for an evaluation of noise attenuation across engine speeds. Experimental testing was conducted to validate the GT-Suite model and assess the effectiveness of muffler design 1. This testing involved measuring the SPL with a calibrated microphone, both with and without the designed muffler. Muffler design 1 was based on the dominant exhaust
Labao, Kai, Middleton, Nicholas, Nuszkowski, John
Development of Offline Simulation Software for Active Sound Design in Electric Vehicles2025-01-00175/5/2025
With the increasing adoption of electric vehicles (EVs), Active Sound Design (ASD) has become a crucial method for enhancing both sound quality and the overall driving experience, addressing the absence of the distinctive engine sounds found in internal combustion vehicles. This paper presents an ASD offline simulation software developed on the MATLAB platform. The software integrates a vehicle dynamics model with three key sound synthesis algorithms—order synthesis, pitch shifting, and granular synthesis—enabling comprehensive control strategy development, real-time sound playback, and rapid adjustments. It comprises multiple functional modules, including configuration, order generation, pitch shifting, and granular synthesis interfaces, offering a user-friendly environment for flexible sound parameter tuning under various simulated driving conditions. Users can easily configure vehicle dynamics, adjust gain values, and visually manipulate sound parameters to create a customized ASD
Qian, Yushu, Xie, Liping, Xiong, Chenggang, Liu, Zhien
Virtual Calibration Approach to the Development of Control Systems and Strategies for Hybrid L-Category Vehicles2024-32-00664/18/2025
Hybrid powertrain for motorcycles has not been widely adopted to date but has recently shown significant increased interest and it is believed to have great potential for fuel economy containment in real driving conditions. Moreover, this technology is suitable for the expected new legislations, reduced emissions and enables riding in Zero Emissions Zones, so towards a more carbon neutral society while still guaranteeing “motorcycle passion” for the product [1, 2]. Several simulation tools and methods are available for the concept phase of the hybrid system design, allowing definition of the hybrid components and the basic hybrid strategies, but they are not able to properly represent the real on-road behaviour of the hybrid vehicle and its specific control system, making the fine tuning and validation work very difficult. Motorcycle riders are used to expect instant significant torque delivery on their demand, that is not properly represented in legislative cycles (e.g. WMTC); rider
Antoniutti, Christian, Sweet, David, Hounsham, Sandra
Novel Statistical Modelling-Based Approach for Exhaust Mass Flow Calculation in Motorcycles2024-32-00724/18/2025
The exhaust mass flow measurement for motorcycles poses a unique challenge due to presence of pulsations arising from an unfavorable combination of the engine displacement-to-exhaust system volume ratio and the long or even unequal ignition intervals. This pulsation phenomenon significantly impacts the accuracy of the differential pressure-based measurement method commonly employed in on-board measurement systems for passenger cars. This paper introduces an alternative approach calculating exhaust mass flow in motorcycles, focusing on statistical modelling based on engine parameters. The problem at hand is rooted in the adverse effects of pulsations on the differential pressure-based measurement method used in the EFM. The unfavorable combination of engine characteristics specific to motorcycles necessitates a novel approach. Our proposed alternative involves utilizing readily available OBD parameters, namely engine speed and calculated engine load as there is mostly no data for intake
Schurl, Sebastian, Sturm, Stefan, Schmidt, Stephan, Kirchberger, Roland
Evaluation of Several Tesla Dashcam Angles for Model 3 and Y via Reverse Project Photogrammetry2025-01-87004/1/2025
Tesla Model 3 and Model Y vehicles come equipped with a standard dashcam feature with the ability to record video in multiple directions. Front, side, and rear views were readily available via direct USB download. Additional types of front and side views were indirectly available via privacy requests with Tesla. Prior research neither fully explored the four most readily available camera views across multiple vehicles nor field camera calibration techniques particularly useful for future software and hardware changes. Moving GPS instrumented vehicles were captured traveling approximately 7.2 kph to 20.4 kph across the front, side, and rear views available via direct USB download. Reverse project photogrammetry projects and video timing data successfully measured vehicle speeds with an average error of 2.45% across 25 tests. Previously researched front and rear camera calibration parameters were reaffirmed despite software changes, and additional parameters for the side cameras
Jorgensen, Michael, Swinford, Scott, Imada, Kevin, Farhat, Ali
CAATS – Automotive Wind Tunnel Calibrations2025-01-87624/1/2025
Wind tunnel calibration is necessary for repeatable and reproducible data for all industries interested in their output. Quantities such as wind speed, pressure gradients, static operating conditions, ground effects, force and moment measurements, as well as flow uniformity and angularity are all integral in an automotive wind tunnel’s data quality and can be controlled through appropriate calibration, maintenance, and statistical process control programs. The purpose of this technical paper is to (1) provide a basis of commonality for automotive wind tunnel calibration, (2) help customers and operators to determine the calibration standards best suited for their unique automotive wind tunnel and, (3) complement the American Institute of Aeronautics and Astronautics recommended practice R-093-2003(2018) Calibration of Subsonic and Transonic Wind Tunnels as specifically applied to the automotive industry. This document compiles information from various automotive wind tunnel customers
Bringhurst, Katlynn, Best, Scott, Nasr Esfahani, Vahid, Senft, Victor, Stevenson, Stuart, Wittmeier, Felix
Parameter Virtual Calibration Method for Vehicle Path Tracking Controller Based on the Deep Reinforcement Learning2025-01-83114/1/2025
Path tracking control, which is one of the most important foundations of autonomous driving, could help the vehicle to precisely and smoothly follow the preset path by actively adjusting the front wheel steering angle. Although there are a number of advanced control methods with simple structure and reliable robustness that could assist vehicles achieving path tracking, these controllers have many parameters to be calibrated, and there is a lack of guidance documents to help non-professional test site engineers quickly master calibration methods. Therefore, this paper proposes a parameter virtual calibration method based on the deep reinforcement learning, which provides an effective solution for parameter calibration of vehicle path tracking controller. Firstly, the vehicle trajectory tracking model is established through the kinematic relationship between the vehicle and the target path, combined with the Taylor series expansion linearization method. Next, a vehicle path tracking
Zhao, Jian, Guo, Chenghao, Zhao, HuiChao, Zhao, Yongqiang, Yu, Zhen, Zhu, Bing, Chen, Zhicheng
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