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

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

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

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

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

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

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

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

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

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

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

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

A Novel Wide Range Machine Learning Polynomial Regression Formulation for Enhanced Load Cell Calibration Accuracy

2026-26-0567

1/16/2026

Calibration of measuring instruments is of utmost importance in the field of metrology. It is a mandatory pre-requisite for establishing the fidelity of the measurements as well as to lend confidence. Even more critical is the requirement for the master equipment deployed to calibrate the devices in use. This entails that high accuracy needs to be guaranteed in the calibration process, and that the uncertainty be quantified precisely. The widely used conventional least squares polynomial regression formulation for load cell calibration is based on the non-normalized residual, which is the difference between the measured and master values. The nature of this formulation is such that it imparts more weightage on measured values at higher ranges resulting in good accuracy. However, there is a limitation of this same formulation that results in lesser accurate fit at lower values especially if the instrument is to be used in operation over a wide range including lower ranges of the

S Thipse, Yogesh

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

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

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

Development of an Artificial Calibrator for Shift Program Optimization to Enhance Fuel Efficiency

2025-32-0082

11/3/2025

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 Methods

2025-01-0402

10/7/2025

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 Comfort

2025-24-0103

9/7/2025

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 Reconstruction

10-09-04-0036

8/21/2025

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 Systems

2025-01-0283

7/2/2025

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 Control

2025-01-0252

6/16/2025

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

Development of Offline Simulation Software for Active Sound Design in Electric Vehicles

2025-01-0017

5/5/2025

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

Design of a Reactive-Style Muffler for a Formula SAE Race Car

2025-01-0024

5/5/2025

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

Novel Statistical Modelling-Based Approach for Exhaust Mass Flow Calculation in Motorcycles

2024-32-0072

4/18/2025

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

Virtual Calibration Approach to the Development of Control Systems and Strategies for Hybrid L-Category Vehicles

2024-32-0066

4/18/2025

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

CAATS – Automotive Wind Tunnel Calibrations

2025-01-8762

4/1/2025

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

Evaluation of Several Tesla Dashcam Angles for Model 3 and Y via Reverse Project Photogrammetry

2025-01-8700

4/1/2025

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

Design of Fault-Tolerant Control Strategy for Dual-Winding Permanent Magnet Synchronous Motor in Vehicles

2025-01-8805

4/1/2025

Motor drive control is crucial for achieving the performance, reliability, and comfort of electric vehicles. Multi-phase motors, represented by dual-winding permanent magnet synchronous motors (PMSMs), have significant research value in the electric vehicle field due to their high-power drive capabilities and strong fault tolerance. A simple and easily analyzable motor model is essential for achieving high precision in control. This paper employs VSD coordinate transformation (vector space decomposition) based on electromagnetic principles and the positional relationships between windings, treating the multi-phase motor as a whole and mapping various physical quantities to multiple subspaces for simplified analysis. Consequently, a mathematical model for the dual-winding PMSM is established. The vector control system based on VSD coordinate transformation adopts a dual closed-loop structure for speed and current. It focuses on a comparative analysis between traditional two-vector

Gao, ChaoFan, Zheng, Hongyu, Kaku, Chuyo

Real-time Terrain Analysis for Off-road Autonomous Vehicles

2025-01-8343

4/1/2025

One challenge for autonomous vehicle (AV) control is the variation in road roughness which can lead to deviations from the intended course or loss of road contact while steering. The aim of this work is to develop a real-time road roughness estimation system using a Bayesian-based calibration routine that takes in axle accelerations from the vehicle and predicts the current road roughness of the terrain. The Bayesian-based calibration method has the advantage of providing posterior distributions and thus giving a quantifiable estimate of the confidence in the prediction that can be used to adjust the control algorithm based on desired risk posture. Within the calibration routine, a Gaussian process model is first used as a surrogate for a simulated half-vehicle model which takes vehicle velocity and road surface roughness (GD) to output the axle acceleration. Then the calibration step takes in the observed axle acceleration and vehicle velocity and calibrates the Gaussian process model

Lewis, Edwina, Parameshwaran, Aditya, Redmond, Laura, Wang, Yue

Reduced Order Model for Viscosity and Mass Density Sensing by A U-Shaped Wire

2025-01-8472

4/1/2025

This paper focuses on the basic principle of measuring viscosity and density with U-shaped tungsten wire sensor, and develops a model for measuring liquid viscosity and density with the help of oscillating ball model. Firstly, the working mechanism of the wire resonator is deeply analyzed. Then, by reducing the order of the fluid dynamic function, a simplified model is established for measuring the viscosity and density of liquid with U-shaped tungsten resonator. The experimental results show that the maximum error of viscosity is 7.22% and the average error is 2.81% when the viscosity ranges from 4.526mPa.s to 62.01mPa.s. In the range of 0.8486g/cm3 to 0.8711g/cm3, the maximum density error is 7.00% and the average density error is 1.89%. In summary, the simplified model proposed in this paper can accurately measure the viscosity and density of liquids.

Shan, Baoquan, Shen, Yitao, Yang, Jianguo, Zhang, Zhaoying, Wu, Dehong, Zhao, Yingke

Tire Thermal Model with State Observer Integration for Enhanced Real-Time Temperature Prediction

2025-01-8756

4/1/2025

This paper focuses on the development of a tire thermal model for automotive applications, addressing the challenge of accurately predicting tire temperatures on different layers of the tire, under various driving conditions. The primary goal is to enhance the understanding of tire temperature behavior to improve safety, performance, and durability. The research utilizes a physics 1-D model for the tire, from which a system of differential equations, describing the interaction between different layers of the tire, is derived. Furthermore, a state observer is used to estimate tire temperatures, using Tire Pressure Monitoring System (TPMS) measurements to correct model predictions. In particular, the TPMS measurements are assumed to be sufficient to exclude the additional thermal contributions coming from the rims and disk brakes, which simplifies the model, making it more suitable for real-time applications. A calibration procedure is defined for deriving the model parameters, based on

Longobardi, Armando, Balaga, Sanjay, labella, Mario, Gorine, Mohamed El Amine

Supervisory Control System Development for a Toyota Mirai FCEV

2025-01-8586

4/1/2025

The performance of a second-generation Toyota Mirai fuel cell was characterized as part of the SwRI internal research program. This data was used to develop a supervisory controller scheme designed to balance the plant for the fuel cell system during steady-state and transient vehicle conditions. This was accomplished using a Supervisory Integrated Controller (SIC) implemented on a Real-time Power Electronics Control System (RPECS) with a Simulink-based control algorithm. The actuators of interest are the three hydrogen injectors at anode inlet, air compressor and three air side valves on at the cathode inlet. The FC power measurement and pressure sensor readings at the anode and cathode were utilized as real-time feedback for the controller operation. The aim of the controller was to achieve and maintain the power target set by the hybrid powertrain ECU present on the vehicle, which is responsible for balancing power on the fuel cell and battery over the high-voltage bus. These

Chundru, Venkata Rajesh, Kubesh, Matthew, Legala, Adithya

Parameter Virtual Calibration Method for Vehicle Path Tracking Controller Based on the Deep Reinforcement Learning

2025-01-8311

4/1/2025

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

Dynamic Evaluation Method for Public Transport Operation Indicators Based on Rank Transformation

2025-01-7219

2/21/2025

This study presents a method to evaluate the daily operation of traditional public transportation using multi-source data and rank transformation. In contrast with previous studies, we focuses on dynamic indicators generated during vehicle operation, while ignoring static indicators. This provides a better reference value for the daily operation management of public transport vehicles. Initially, we match on-board GPS data with network and stop coordinates to extract arrival and departure timetable. This helps us calculate dynamic operational metrics such as dwell time, arrival interval, and frequency of vehicle bunching and large interval. By integrating IC card data with arrival timetable, we can also estimate the number of people boarding at each stop and derive passenger arrival time, waiting time, and average waiting time. Finally, we developed a comprehensive dynamic evaluation method of public transportation performance, covering the three dimensions: bus stops, vehicles, and

Zhou, Yang, Shao, Yichang, Han, Zhongyi, Ye, Zhirui

Vehicle Positioning Technology Based on Stereo Vision

2025-01-7163

2/21/2025

Vehicle localization in enclosed environments, such as indoor parking lots, tunnels, and confined areas, presents significant challenges and has garnered considerable research interest. This paper proposes a localization technique based on an onboard binocular camera system, utilizing binocular ranging and spatial intersection algorithms to achieve active localization. The method involves pre-deploying reference points with known coordinates within the experimental space, using binocular ranging to measure the distance between the camera and the reference points, and applying the spatial intersection algorithm to calculate the camera’s center coordinates, thereby completing the localization process. Experimental results demonstrate that the proposed algorithm achieves sub-meter level localization accuracy. Localization accuracy is significantly influenced by the calibration precision of the binocular camera and the number of reference points. Higher calibration precision and a greater

Feifei, Li, Haoping, Qi, Yi, Wei

Modeling Internal Node Impedance in High-Speed Rail Passenger Hubs Based on Queuing Theory

2025-01-7172

2/21/2025

High-speed railway (HSR) hubs play a pivotal role in the integrated transport system, efficiently connecting various modes of transport and facilitating transport integration. Characterized by their large scale, complex functional spatial layouts, and diverse interchange types, these hubs see a growing proportion of passenger traffic annually. Thus, studying the interchange impedance in high-speed railway passenger transport hubs is crucial for enhancing interchange efficiency and service quality. However, current research lacks a quantitatively comparable impedance model for high-speed railway hubs, particularly under peak passenger flow conditions. This paper addresses this gap by examining the internal node impedance at Nanjing South Railway Station, focusing on the entry gate turnstile node and security check node. It begins by analyzing passenger passing behavior at these nodes and then constructs a integrated queuing model for inbound gates and security checks, considering the

Zhang, Zhenyu, Wang, Jian

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