Browse Topic: Electrical, Electronics, and Avionics

Items (56,738)

Speed Determination Using Audio of Dash Camera Video from Commercial Vehicle Tires Frequencies for Vehicle Accident Reconstruction

2026-01-0536To be published on 04/07/2026

Prior research has validated a reliable method for determining vehicle speed using audio recorded by cameras mounted in vehicles, specifically for rolling passenger vehicle tires. Passenger vehicle tires produce a frequency component directly correlated to vehicle speed when traveling on concrete roadways. However, prior research has not been conducted on audio for rolling commercial vehicle tires, which differ in construction from passenger vehicle tires and therefore produce audio signals on roadway surfaces that passenger vehicles tires did not when tested in the prior study. The current research concluded that commercial vehicle tires rolling on various roadway surfaces also generated a frequency that varied with vehicle speed. The purpose of this study was to outline, test, and confirm the source of the speed-dependent frequency and to develop a validated method for use in forensic applications. A modified version of the passenger vehicle tire equation from prior research was

Vega, Henry V., Cornetto, Anthony, Ngo, Long Justin, Hatab, Ziad, Hunter, Eric

Comparative Analysis of Rear Collision Prevention Systems

2026-01-0535To be published on 04/07/2026

Rear Collision Warning (RCW) and Rear Emergency Braking (REB) are key Advanced Driver Assistance Systems (ADAS) designed to prevent collisions with Vulnerable Road Users (VRUs) when a vehicle is reversing. These systems typically rely on a combination of radar, ultrasonic, and camera sensors. This study provides a comparative evaluation of Rear Collision Prevention systems across different vehicle manufacturers. Testing was performed on the 2022 Cadillac Escalade, 2017 Subaru Outback, and 2021 Ford F-150, using both static and moving pedestrian targets. Data collection was carried out with a VBOX data acquisition system, focusing on time-to-collision (TTC) as the primary performance metric. The analysis highlights variations in system effectiveness and provides a brief overview of OEMs' approach to pedestrian safety.

Harrington, Shawn, Takbhate, Shubham Ramraja, Nagarajan, Sundar Raman

A Cooperative AFS/DYC Control for FWDIEV Based on Dissipative Energy Method

2026-01-0619To be published on 04/07/2026

To enhance the lateral stability of four-wheel-drive intelligent electric vehicles (FWDIEV) under extreme operating conditions, this paper proposes a cooperative control strategy integrating active front steering (AFS) and direct yaw moment control (DYC) based on dissipative energy method. A nonlinear three-degree-of-freedom vehicle model is established to analyze the evolution of the vehicle state phase trajectory. A quantitative lateral stability index is constructed using dissipative energy to accurately evaluate the vehicle's lateral dynamics. Utilizing dissipative energy and its gradient information, a time-varying stability boundary is defined under dynamic constraints, and adaptive weighting coordination between the AFS and DYC systems is designed to achieve coordinated control of front steering angle and additional yaw moment. A feedforward–model predictive control (FF-MPC) framework is developed, in which a feedforward module generates compensation based on driver intent to

Zhao, Kun, Zhao, Zhiguo, Wang, Yutao, Xia, Xue, Chen, Xi, Hu, Yingjia

Multi-steering Mode and Path Following Control of Vehicle with Dual Modular Chassis

2026-01-0625To be published on 04/07/2026

The cooperative formation of four-wheel-steering and four-wheel-drive (4WS-4WD) modular chassis offers a flexible and scalable solution for oversized cargo transportation. To enable coordinated control between the dual chassis modules, a distributed model predictive control (DMPC) framework is introduced. This framework facilitates inter-module communication between the front and rear chassis controllers, thereby achieving consistent and cooperative behavior. Each chassis module is equipped with an independent model predictive control (MPC) path tracking controller, which is designed based on a single-module dynamic model. The MPC cost function jointly optimizes path tracking accuracy and roll stability. In terms of path planning, a hierarchical framework is proposed: the upper layer generates a global path, while the lower layer derives local reference trajectories for the front and rear modules based on their respective poses. Moreover, multiple steering modes are analyzed under

Liu, Zuyang, Shen, Yanhua, Wang, Kaidi, Lv, Xin

Multiple-Parameter Joint Estimation for Unmanned Mining Trucks Based on Real-Time Cornering Stiffness Identification

2026-01-0618To be published on 04/07/2026

High-precision estimation of key vehicle–road state parameters is essential for accurate and safe vehicle control, as well as for reliable trajectory tracking and path planning. However, the strong nonlinearity of tire forces makes real-time estimation of tire cornering stiffness challenging, and the uncertainty of this parameter significantly limits the performance of conventional estimation methods. To overcome this issue, this study proposes a novel joint estimation framework that integrates the Square-Root Cubature Kalman Filter (SCKF) with Moving Horizon Estimation (MHE). In the proposed approach, the SCKF provides high-accuracy estimation of the vehicle sideslip angle, while the MHE enables real-time identification and updating of tire cornering stiffness using vehicle state information over a short horizon. This establishes a closed-loop joint estimation mechanism between sideslip angle and cornering stiffness. Furthermore, the real-time updated cornering stiffness is

Xia, Xue, Shen, Peihong, Jiao, Leqi, Li, Tao, Chen, Huiyong, Zhao, Kun, Jiao, Leqi, Zhao, Zhiguo

Vault-Induced Pitchover Crash Testing of a Recreational Off-Highway Vehicle (ROV)

2026-01-0546To be published on 04/07/2026

VVehicle pitchover crashes can result in very severe accelerations and forces. Literature and test data available on pitchover crashes is sparse. This paper presents the results of a full-scale pitchover/rollover crash test using an instrumented vehicle in a controlled and documented off-road environment. The test vehicle was driven to the launch point by an off-board operator using remote steering and throttle controls. The test vehicle then experienced an airborne phase during which forward pitching occurred, followed by a front-to-ground impact which induced additional pitchover motion. Then, following the initial front and rear impacts, the vehicle transitioned from a pitchover to rollover motion before coming to rest. The resulting vehicle motion, vehicle damage markings, and ground markings were documented with various slow motion and real time camera views. The test vehicle was instrumented with accelerometers, rotation rate sensors, and other sensors, the results of which

Warner, Mark, Warner, Wyatt, Swensen, Grant, Perl, Mark

Quantifying the Accuracy of Detecting Lateral Movement Onset for a Selection of Bicycle GPS Computers

2026-01-0531To be published on 04/07/2026

Bicycle computers and apps record, at minimum, positional data over time, and this data is commonly used in accident reconstruction to inform the behavior of the bicycle and rider prior to an incident in question. This positional data is primarily calculated using the Global Positioning System (GPS) and thus specific accuracy and precision is not well quantified. To improve this understanding when it comes to relative accuracy of lateral movements, a series of controlled tests were performed and recorded with several commercially available bicycle GPS computers and apps and compared against a RaceLogic VBox 3i ADAS with Real-Time Kinematic corrections from a RaceLogic Base Station. The testing consisted of 5 sets of 15 runs: 15 runs riding down the center of a lane, 15 making a 6 ft lateral left movement, 15 making 12 ft lateral left movements, 15 making 6 ft lateral right movements, and 15 making 12 ft lateral right movements. Each test run was ended with hard braking to a stop to

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

Machine Learning-Based Gear Defect Detection in Electric Vehicle Drive Units Using End-of-Line Vibration Analysis

2026-01-0637To be published on 04/07/2026

The final assembly of electric vehicle (EV) drive units includes an essential End-of-Line (EOL) test to ensure both component integrity and Noise, Vibration, and Harshness (NVH) quality. This screening process, which uses dynamometers to measure vibration signals, is critical for identifying defects before a drive unit is installed in a vehicle. A significant source of failure during this test is gear defects, which can arise from manufacturing or handling issues. Traditional EOL testing methods rely on time-domain analysis and the impulsiveness of vibration signatures to detect these defects, a technique with inherent limitations in accuracy. This paper introduces and evaluates a novel approach using Machine Learning (ML) to analyze vibration signals for improved gear defect detection. We discuss the methodologies of both the traditional time-domain and the proposed ML-based techniques. Finally, we provide a comprehensive comparison of their respective efficiency and accuracy

Arvanitis, Anastasios, Michaloliakos, Anargyros

Modular Smart Corner Systems for Next-Generation Electric Vehicle Architecture

2026-01-0639To be published on 04/07/2026

The transition to software-defined vehicles (SDVs) necessitates a paradigm shift in both control strategies and vehicle architecture. The EU-funded SmartCorners project addresses this challenge by developing integrated smart corner systems (SCS) that combine in-wheel motor propulsion, brake-by-wire, active suspension, and steer-by-wire functionality in a single module. These SCS are embedded within a highly adaptable skateboard chassis architecture, enabling scalable deployment across diverse vehicle platforms. The central approach of this paper is the utilization of artificial intelligence and machine learning to implement multi-layer, data-driven control strategies, facilitating real-time actuation, fault mitigation, and a user-centric vehicle architecture. The SmartCorners project targets to demonstrate significant advancements, including improvement in real-world driving range, reduction of component and system costs, and development time drop of the EV systems enabled by digital

Ratz, Florian, Armengaud, Eric, Formento, Cecilia, Moscone, Giulia, Sorrentino, Gennaro, Bisciaio, Giorgio, Sorniotti, Aldo, Amati, Nicola, Braun, Daniel, Deibler, Bernd, Boxberger, Valerius, Sottile, Salvatore, Ivanov, Valentin, Fuse, Hiroyuki, Kompara, Tomaž

Research on MPC-based Control of Electric Active Anti-roll Bar

2026-01-0632To be published on 04/07/2026

To enhance vehicle lateral stability and anti-roll performance, a model predictive controller (MPC) is designed for an electrically driven active anti-roll bar. First, a vehicle roll dynamics model and an active anti-roll bar model based on motor drive are established. Then, model predictive controllers for the front and rear suspension anti-roll bars are designed to calculate the anti-roll moments for the front and rear suspensions. Finally, co-simulation using Carsim and Simulink is conducted to simulate typical vehicle driving scenarios. The simulation results of passive suspension, PID and MPC control are compared to validate the improvement in vehicle stability achieved by MPC control, and the sensitive parameters of the controller affecting anti-roll performance are analyzed. The research results show that, compared to passive suspension and traditional PID control, the maximum roll angle with model predictive control is reduced by 80% and 40%, respectively. The MPC controller

Shen, Leiming

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

2026-01-0010To be published on 04/07/2026

We propose a self-supervised Gaussian-based framework that enhances the robustness and efficiency of static-dynamic decomposition and high-fidelity reconstruction in autonomous driving scenarios. Existing approaches demonstrate promising performance but remain prone to noisy motion segmentation, overfitting on sparse LiDAR observations, and degraded accuracy in low-texture or distant regions, resulting in unstable geometry. To address these challenges, our method introduces a feature fusion module that jointly exploits image semantics and geometric cues, enabling more reliable motion prior extraction under noise and illumination variations. A cross-sequence geometric consistency constraint is further designed to regularize reconstruction across temporal and viewpoint changes, ensuring physically coherent scene representations. In addition, we formulate an efficient Gaussian parameter optimization strategy that constrains scale and normal updates, preventing unrealistic ellipsoid growth

Feng, Runlei, Wang, Ning, Zhang, Zhihao

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

2026-01-0016To be published on 04/07/2026

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

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

Efficient Embedded Control of Nonlinear Systems for Software-Defined Vehicles: A Case Study Using the Inverted Pendulum

2026-01-0075To be published on 04/07/2026

Software-defined vehicles (SDVs) are reshaping automotive control architectures by shifting intelligence to embedded systems, where computational efficiency is paramount. This paper presents the implementation of multiple control strategies such as PID, LQR, and MPC for the inverted pendulum on a cart problem, a canonical example of nonlinear and unstable system control. The objective is to demonstrate how sophisticated control algorithms can be optimized for execution on microcontrollers with limited processing power and memory, reflecting the constraints typical of embedded platforms in SDVs. Each control strategy is implemented with careful consideration of algorithmic complexity, real-time responsiveness, and resource utilization. Performance is evaluated across key metrics including stability, convergence time, and computational load, enabling a comparative analysis that highlights trade-offs between control fidelity and hardware efficiency. The results provide actionable insights

Vupparige, Varun, Pandya, Vidit

Loss Model and Finite Element Analysis Thermal Model of a Production Fast Charging Battery Module

2026-01-0123To be published on 04/07/2026

Accurate modeling of battery temperature rise during fast charging is challenging due to uncertainty around cell heat generation and the thermal characteristics of the materials and interfaces which make up the battery pack. High fidelity thermal models are critical to attaining the best battery pack design, since they enable a multitude of cooling and packaging approaches to be considered prior to building a prototype. In this study, a 3D finite element analysis (FEA) thermal model of a production fast charging battery module is created. A loss model is parameterized as input for the FEA model. A key part of the loss model is the entropic heating coefficient (EHC), which is the change of open circuit voltage with respect to temperature. The EHC is measured by waiting for the cell voltage to reach steady state at various temperatures in 5% and 10% state of charge intervals over its capacity. This is then corrected numerically by accounting for unwanted discharge or rebounds. The EHC is

Thornton, Jack, Kollmeyer, Phillip, Panchal, Satyam, Gross, Oliver

CAN-Based Model Interface Standardization and Model Integration Framework

2026-01-0081To be published on 04/07/2026

With the rapid increase in the complexity of automotive electronic control systems and advancements in software-defined vehicles, the efficient integration of diverse simulation models has become a critical requirement in vehicle development. Virtual testing (VT) and scenario-based validation are now considered essential for safety certification and regulatory compliance. However, current modeling practices are often hampered by inconsistencies in signal definitions, repetitive manual processes, and the inherent complexity of integrating various development environments and tools, which can lead to time-consuming workflows and an increased risk of errors. Engineers typically match controller and plant model signals by hand or rely on limited automation solutions that may lack scalability and robustness for large-scale, collaborative simulation environments. In this study, we propose a CAN-based integration framework that incorporates a standardized interface library and a Python-based

Lee, Bokyung, Lee, Jin Hwa, Kim, Donghwan, Han, Woojin, Cho, Yoonho, Kim, Wonho

Curvature-Based End-to-End Autonomous Vehicle Path Planning at Intersections

2026-01-0045To be published on 04/07/2026

Autonomous vehicle navigation requires accurate prediction of driving path curvature to ensure smooth and safe trajectory planning. This paper presents an end-to-end approach to curvature prediction using deep neural networks trained on real-world driving data. We develop a comprehensive neural network architecture that directly maps high-dimensional sensor inputs to curvature predictions, eliminating the need for intermediate feature engineering steps. The model processes multi-modal inputs including vision features, vehicle state parameters, and environmental context through a deep learning pipeline. Our end-to-end approach demonstrates the feasibility of learning complex driving behaviors directly from raw sensor data, providing a more robust and generalization solution compared to traditional rule-based methods. The neural network architecture incorporates dropout regularization and adaptive learning rate scheduling to ensure stable training and optimal performance. Results show

Hajnorouzali, Yasaman, Wang, Hanchen, Li, Taozhe, Burch, Collin, Lee, Victoria, Tan, Lin, Arjmandzadeh, Ziba, Xu, Bin

Optimization of Road Reference Profiles based on Highway Design for Connected and Automated Vehicles

2026-01-0096To be published on 04/07/2026

Advances in Connected and Automated Vehicles (CAVs) have developed a level in which high-definition maps can be used to improve road safety. Data compactness and robustness on road characterization is essential for the proper handling of vehicles under curves. In this paper, an optimization scheme that relates highway-design road curvature and optimal speed of travel is defined to safely navigate through a given road. The scheme is divided in two main steps. First a nonlinear optimization problem, in which curvature profiles are fitted from a model that based on street design standards as per the American Association of State Highway and Transportation Officials (AASHTO). Secondly, the optimized curvature profile is subject to a secondary optimization problem that uses vehicle dynamics for both constraints and objective function derivation. Guidance reference parameters such as curvature and velocity, at different levels of friction are analyzed. Results show that, even in sparse

Jacome, Ricardo Osmar, Stolle, Cody, Grispos, George

Research on Intelligent Vehicle Fleet Longitudinal Control Based on Fuzzy Model Predictive Control

2026-01-0040To be published on 04/07/2026

Addressing the challenge of balancing tracking efficiency and stability in longitudinal cooperative control of intelligent vehicle platoons, this paper proposes a hierarchical control strategy integrating fuzzy logic with Model Predictive Control (MPC). The upper-layer controller, based on the platoon's longitudinal dynamics model, formulates an optimization objective function that considers both tracking performance and ride comfort. A fuzzy logic system is introduced to adaptively adjust the weighting coefficients within the MPC objective function in real-time based on the prevailing tracking state, outputting an optimal desired acceleration. The lower-layer controller, utilizing an inverse longitudinal dynamics model incorporating the engine's universal characteristic map and a brake system model, accurately translates the desired acceleration into throttle opening or brake pressure via a Proportional Integral Derivative (PID) control algorithm. To validate the control strategy, a

Li, Ye

Monocular 3D Perception and Lane-Aware Bird’s-Eye-View Mapping for Autonomous Driving

2026-01-0012To be published on 04/07/2026

Accurate perception of the surrounding environment is fundamental and essential to safe and reliable autonomous driving. This work presents an integrated vision-based framework that combines object detection, 3D spatial localization, and lane segmentation to construct a unified bird’s-eye-view (BEV) representation of the driving scene. The pipeline provides geometric information on object position and orientation by employing Omni3D to infer 3D bounding boxes of objects from monocular camera frames. Detections are subsequently projected onto a 2D BEV canvas, where object instances are represented with respect to the ground plane for enhanced interpretability. To complement the object-level perception, we utilized YOLOPv2 to perform lane segmentation, producing both lane masks and lane line masks in the image domain for future coordinate transformation. By adopting a pinhole camera model, the coordinate transformation of these masks from the perspective image plane into the BEV canvas

Tan, Lin, Wang, Hanchen, Li, Taozhe, Hajnorouzali, Yasaman, Burch, Collin, Lee, Victoria, Xu, Bin, Arjmanzdadeh, Ziba

Holistic Solution of Unified Framework for Integration of Safety Process into Automotive Software Development Process

2026-01-0068To be published on 04/07/2026

The rapid advancement of advanced driver assistance systems (ADAS) and automated driving has significantly increased the software content and complexity within modern vehicles. Consequently, ensuring both high process quality and compliance or qualification with functional safety standards becomes critically important. Automotive Software Process Improvement and Capability Determination (ASPICE 4.0) focuses on evaluating and improving software development processes, while ISO 26262-2018 standard ensures the establishment of functional safety in road vehicles. The efficient integration of the process and standard remains a key challenge due to differences in their objectives, terminologies, and assessment criteria. The misalignment between ASPICE 4.0 and ISO 26262-2018 standard often results in duplicated efforts, rework of work products, and delays in product release schedules. This paper proposes a unified framework to bridge ASPICE 4.0 process areas with ISO 26262-2018 safety

Ravi, Reshma, Easwaramoorthy, Prasad Vignesh, Promise, Dinu

THE IN-VEHICLE CONNECTIVITY FOUNDATION POWERING AUTONOMOUS DRIVING

2026-01-0009To be published on 04/07/2026

Achieving full vehicle autonomy is not just about adding sensors or compute-it requires a fundamental shift in how vehicles are architected. Autonomous systems rely on higher-resolution sensors, massive processing power, and the ability to fuse data from multiple sources in real time. Centralized in-vehicle architectures, which consolidate computing and enable sensor fusion, place unprecedented demands on connectivity. Precise time synchronization across all systems becomes critical, as does advanced control to ensure safe and reliable operation. Any delay or data loss can impact decision-making, making robust, resilient communication links essential. High-performance connectivity is the backbone of this evolution. It must deliver the highest bandwidth to handle massive streams of sensor data, support long-reach connections across the vehicle, and maintain error-free performance even in the most challenging electromagnetic environments. This combination of speed, reach, and reliability

Shwartzberg, Daniel

Is Implicit Knowledge Enough for LLMs? A RAG Approach for Tree-based Structures

2026-01-0111To be published on 04/07/2026

Large Language Models (LLMs) are effective at generating responses using contextual information, which is especially useful when working with structured data like code. Retrieval-Augmented Generation (RAG) enhances this capability by retrieving relevant documents to supplement the model’s context. However, the best way to represent retrieved knowledge—particularly for hierarchical structures such as trees—remains underexplored. In this work, we introduce a novel method to linearize tree-structured knowledge, making it suitable for storage in a knowledge base and direct use with RAG. We compare this approach to applying RAG on raw, unstructured code, evaluating both the accuracy and quality of the generated responses. Our results show that while response quality is comparable between the two methods, our linearized approach reduces the number of vector documents by almost 4x. Without any loss of generality, we applied and benchmarked our method to a specific automotive use case, that is

Gupte, Mihir, Giusto, Paolo, S, Ramesh

Backlash Evasion Strategy for Enhancing Vehicle Cornering Performance

2026-01-0640To be published on 04/07/2026

This study presents a torque distribution control strategy for EVs with e4WD powertrain to overcome the trade-off between ensuring vehicle acceleration and deceleration responsiveness and mitigating backlash shock in the driving system. The deterioration of the drivability which occurs from the intrinsic hardware characteristics of the drivetrain is prevented by designing a response-priority drive mode in which neither front or rear motor torque is allowed to change its sign. Instead, in such drive mode, the front motor torque is only allowed to perform regenerative braking while the rear motor torque is only allowed to produce positive acceleration torque. In order to avoid sacrificing the maximum acceleration by applying such strategy, the mode transition function is implemented as well. In addition, in order to prevent backlash impact due to drivetrain compliance, variable offset torque based on drivetrain compliance model is evaluated in real time and applied to each motor command

Oh, JIWON, Lee, Ho Wook

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

2026-01-0355To be published on 04/07/2026

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

Artificial Intelligence in Automotive Stamping –the latest industrial applications and future directions

2026-01-0179To be published on 04/07/2026

A review of the applications of Artificial Intelligence (AI) in automotive stamping is presented. The focus is on recent AI implementations within the automotive industry. Through this review, the authors aim to capture the current momentum of AI in automotive stamping. The article begins with an overview of the importance and challenges of stamping in the automotive sector, followed by a discussion of key AI technologies applied in this domain. Several industrial applications are then introduced, categorized by their specific use cases. Finally, strategic challenges and future directions are discussed.

Sheng, ZiQiang, Huang, Lu, Asimba, Brian, McCarty, Eric, whaley, Jason, cabral, Kleber, Osegueda, Mario, Huang, Xiaosong, Erol, Baris

A Real-time Capable, Three-dimensional, and Nonlinear Multibody Model of the Mercedes-Benz G-Class for an Efficient Assessment of Off-road Performance in Early Stages of Vehicle Development

2026-01-0217To be published on 04/07/2026

This article deals with the development of a real-time capable, three-dimensional model of the Mercedes-Benz G-Class with flexible ladder frame that considers nonlinear suspension kinematics and force elements. The shift to new drivetrain technologies often results in a significant increase in vehicle weight and requires corresponding design modifications - also applying to off-road vehicles. These modifications result in changed stiffness of elements such as the ladder frame or anti-roll bar, which significantly affect vehicle dynamics and off-road performance. Therefore, strategic, efficient assessments must be made in early development stages, where no detailed information about individual systems and components is available yet, to detect and avoid potential massive, costly changes in later stages. This requires a “handmade” vehicle simulation model specifically tailored to this particular application, since the use of commercial multi-purpose simulation packages is not effective

Riebler, Sandro, Pernsteiner, Samuel, Granitz, Christina, Schabauer, Martin

Energy-Aware Traffic Signal Optimization for Sustainable Urban Networks Using Valid Action Policies

2026-01-0462To be published on 04/07/2026

Optimizing signal control across urban networks is crucial for reducing energy consumption and improving traffic efficiency. Advances in Vehicle-to-Everything (V2X) technologies have enabled deep reinforcement learning (DRL) to demonstrate significant promise in traffic signal control. However, many existing studies focus primarily on agent performance while neglecting practical operational constraints, which can lead to fairness issues in traffic control. Moreover, prior work often treats energy optimization as a by-product of efficiency. To address these gaps, this study proposes a multi-agent DRL framework to mitigate peak-hour congestion and reduce excessive emissions in urban networks. Building on this framework, a variant of Independent Proximal Policy Optimization (IPPO), termed Global State Independent PPO (GS-IPPO), is developed to enhance IPPO's coordination capability. To further improve practical applicability and fairness, this study incorporates constraints imposed by the

Qian, Wangrui, Jiang, Shuyan, Qi, Hao, Ou, Shiqi(Shawn)

Comparative Analysis of Flux Linkage Characterization Methods for Automotive Permanent Magnet Traction Motors: A Critical Review and Performance Evaluation Framework

2026-01-0441To be published on 04/07/2026

Accurate flux linkage characterization is essential for the design, control, performance and efficiency optimization of permanent magnet (PM) traction motors in automotive applications. Precise knowledge of flux linkage across varying load, speed, and temperature conditions directly impacts torque production, field-weakening capability, overall drive system efficiency and torque security. This paper presents a critical review and classification of flux-linkage characterization methods, encompassing offline laboratory mapping, standstill signal injection, self-commissioning inverter-only routines, and online real-time estimation. Each method exhibits distinct trade-offs in terms of accuracy, robustness to inverter nonlinearities, temperature adaptability, cost, and scalability for production and in-vehicle use. With the increasing complexity of automotive electrified traction systems, understanding these trade-offs is crucial for optimal motor design and control. To enable systematic

Khan, Ahmad Arshan, Haddad, Reemon, Kim, Jay, Hermann, Justin, Mohamadian, Mustafa

Peak SCR Efficiency for Ultra-Low NOx Using Electrically Heated Mixer for EU-VII and China-VII

2026-01-0363To be published on 04/07/2026

Achieving ultra-low NOx emissions remains a major challenge in diesel emission control worldwide, especially as increasingly stringent regulations are introduced globally. Selective Catalytic Reduction (SCR), the leading NOx reduction technology in diesel systems, performs best when both heat and ammonia are available. At the same time, any proposed solution must also be cost-effective and economically viable. In this work, we present a low-cost Electrically Heated Mixer (EHM). EHM not only significantly reduces urea deposit formation, thereby lowering warranty costs and mitigating failure modes, but also enhances SCR efficiency, especially in challenging low-load conditions having low exhaust temperature. EHM is also ideal for rapid heat-up and urea injection during engine cold-start conditions ( <100 °C) enabling swift NH₃ formation and storage in the SCR catalyst. Additionally, it enables early urea injection (~ 130 - 150 °C), improving SCR efficiency in low temperatures below 200

Masoudi, Mansour, Poliakov, Nick

Dual Battery Pack System for Multi-Voltage Electric Vehicle Operation

2026-01-0392To be published on 04/07/2026

Electric Vehicles (EV) have become a major focus in the automotive industry. This paper introduces a propulsion system design, which supports the Wide Torque Band (WTB) concept to boost the power density of PM (permanent magnet) motors in EV Trucks resulting in performance, efficiency, and cost benefits. The WTB drive requires an 800V battery for vehicle operation. The chosen solution combines two 400V battery packs to create a selectable 400V/800V DC bus. The electrical hardware and controls to manage the dual battery packs in either series (800V) or parallel (400V) mode are presented in this paper. A prototype battery management system (BMS) along with existing production Voltage, Current, Temperature Monitoring (VITM) hardware are applied to perform mode switching, safety, and cell balancing. The success of this dual pack hardware enables high voltage dynamometer testing of a new 800V DU (Drive Unit) and inverters for EVs. The flexibility of switching between two voltage levels (400

Zhu, Yongjie, Lee, Chunhao, Gopalakrishnan, Suresh, Namuduri, Chandra

Multi-stage Packaging and Routing Optimization for Automotive Component Layout, Wire Harness and Duct Route Design

2026-01-0507To be published on 04/07/2026

Modern vehicle design involves complex considerations and tradeoffs between system integration and layout which have a direct impact on performance, efficiency, and cost. The placement of equipment including control boards, motors, and fans as well as the routing of ducts and wire harnesses poses a time-consuming and intricate problem for design engineers. This paper presents an automated methodology to determine the optimal component packaging configuration, duct routing, and wire harnessing layout to maximize component packing density and minimize the total routing length. A two-stage optimization framework has been developed where the first stage packages the components within the design space with considerations for space utilization, component overlap, proximity relationships, point-to-point accessibility, and component mounting. The second stage implements a custom A-star path-finding algorithm and gradient based optimization to determine the optimal route layout between port

LeFrancois, Richard, Kim, Il Yong

A Frequency-Aware Reinforcement Learning Framework for Suspension Control with Integrated Temporal Features and Expert Guidance

2026-01-0209To be published on 04/07/2026

Active suspension systems play a crucial role in improving vehicle ride comfort and handling stability. However, most existing studies focus on the low-frequency range below 20 Hz, leaving the suppression of high-frequency vibrations within 50–500 Hz largely unexplored, even though these vibrations strongly affect in-cabin noise and ride quality. To address this gap, this study introduces a quarter-car suspension model incorporating both bushing dynamics and a rigid-ring tire within a reinforcement learning (RL) framework. A major challenge for RL-based suspension control is its degradation in high-frequency performance. To overcome this issue, we design an innovative training framework that integrates multiple synergistic strategies. First, frequency-domain rewards are incorporated as auxiliary signals to explicitly guide policy optimization in the high-frequency band. Second, long short-term memory (LSTM) networks are embedded in both the Actor and Critic to capture the sequential

zhu, Zhehui, Zhang, Lijun, Meng, Dejian, Hu, Xingyu

An Image-Derived Multimodal Framework for Automotive Tire Exterior Noise Prediction

2026-01-0220To be published on 04/07/2026

This study presents an image-derived multimodal AI framework for early-stage tire noise evaluation. The proposed model requires only multi-angle photographs captured by a standard smartphone and basic tire specifications. From these images, scaled three-dimensional (3D) meshes and fixed-view depth maps are reconstructed and combined with numerical parameters within a neural network architecture. Three input branches—a point-cloud–gradient branch, a depth-map convolutional neural network (CNN) branch, and a specification multi-layer perceptron (MLP) branch—are jointly trained using a composite loss that integrates frequency-weighted mean squared error (MSE), spectral cosine similarity, FFT-domain consistency, and A-weighted sound-level terms. A dataset of 28 tires, spanning passenger, SUV, and pickup applications for both battery electric vehicles (BEVs) and internal combustion engine (ICE) vehicles, was evaluated using leave-one-out (LOO) cross-validation. The model achieved a mean

Shao, Guangxin, Shopoff, Scott, Franklin, Nicholas

In-situ Decoupling and Stiffness Optimization of Resiliently Coupled Assemblies Using FRF-Based Substructuring

2026-01-0227To be published on 04/07/2026

Road shake remains one of the most critical NVH challenges in light- and heavy-duty body-on-frame trucks, directly shaping ride comfort and perceived vehicle quality. At the heart of this issue are the body mounts, which govern the transfer of vibration between frame and body. Optimizing these mounts for improved road shake performance is therefore a key objective in NVH engineering. Traditionally, such optimization has relied on finite element analysis (FEA). While highly accurate, FEA—especially when performed with commercial solvers such as NASTRAN—demands enormous computational resources, with complete simulations often requiring hundreds of hours. Each design iteration multiplies this cost, restricting the pace and efficiency of NVH development. Recent methods such as response surface modeling (RSM) and machine learning (ML) have aimed to reduce the burden, yet they remain dependent on large libraries of pre-computed FEA data, inheriting much of the same expense and inflexibility

Haider, Syed, Abbas, Ahmad, Jahangir, Yawar, Maddali, Ramakanth

Techno-economic assessment of battery recycling and cascade use for legal and illegal recyclers in China

2026-01-0456To be published on 04/07/2026

With the rapid popularization of electric vehicles (EVs), the battery recycling industry is undergoing rapid growth. While the Chinese government has implemented a white-list mechanism under which only approved recyclers are allowed to process retired batteries, the presence of small-scale illegal battery recycling vendors has posed challenges to this mechanism. This study compares the techno-economic performance of battery recycling between legal and illegal recyclers in China, and discusses strategies to eliminate illegal operations. Our research covers two battery chemistries: lithium nickel-manganese-cobalt oxide (NMC) and lithium iron phosphate (LFP), as well as two technological pathways: resource recycling and cascade utilization. Under the base case, the costs of illegal vendors are 35-46% lower than that of legal companies. Although legal companies achieve high resource utilization, their overall economic performance lags behind due to their high costs associated with

Du, Shilong, Li, Haoyang, Dou, Hao, Hao, Han

Analysis of In-Cylinder Flow for Hybrid Gasoline Engine using High-Speed Particle Image Velocimetry

2026-01-0298To be published on 04/07/2026

To curb global warming, reduction of CO2 emissions in automotive powertrains is required. Hybridization and further improvement of thermal efficiency has been promoted in internal combustion engines. Therefore, the 2.0-liter inline 4-cylinder direct-injection engine for 2 motor-hybrid powertrain have been developed to realize rapid combustion by in-cylinder flow enhancement. In this paper, in-cylinder flow analysis using high-speed PIV on the optical single-cylinder engine was performed, and evaluated the combustion promotion concept by analyzing the bulk flow and the turbulence. To achieve rapid combustion, the intake port shape for tumble flow enhancement and bowl-in type piston which was smoothly converted the tumble flow to upward flow were examined. As a method for turbulent component decomposition, the time filter method proposed by the authors were applied. The characteristics of the time filter method is that turbulent component does not include the cycle-by-cycle fluctuations

Okura, Yasuhiro, Urata, Yasuhiro

Low-voltage electric vehicle with increased electrical safety and distributed charging system

2026-01-0396To be published on 04/07/2026

Abstract - Reducing the high-voltage BEV to a household level of 120-240 volts is considered in the article as an effective means of solving the problems of electrical safety, maintenance and minor repairs of an electric vehicle in household conditions, distributed power supply of BEV with walking distance for the driver. The analysis of the low-voltage electric drive is performed under the assumption that the battery has a nominal voltage of 200 volts. The issues of transforming a high-voltage machine (400 volts) into a low-voltage one (200 volts) by switching the stator phase sections from serial to parallel connection without changing the overall and energy characteristics are considered. It is shown that a two-motor unit with induction machines with a capacity of 50 kilowatts can provide 100 kilowatts in long-term and up to 200 kilowatts in peak modes. The article considers the issues of implementing a low-voltage inverter and modern trends in distributed power supply for BEVs

Smolin PhD, Victor, Sobolevskiy, Anatoliy, Volovich, Georgy

Development of the Honda S+ Shift Utilizing e:HEV System

2026-01-0418To be published on 04/07/2026

Honda is promoting mobility electrification to realize a carbon-neutral society by 2050. Hybrid vehicles will remain advantageous over electric vehicles in terms of manufacturing cost and driving range until renewable energy usage increases, charging infrastructure is sufficiently developed, and battery costs are reduced. In response to this situation, Honda has developed a new control system, “Honda S+ Shift”, which further enhances the “emotional value of driving pleasure” inherent to the e:HEV system and creates new value for hybrid vehicles. Honda S+ Shift synchronizes the engine and vehicle speed and selects a virtual gear position according to the driver's operation such as acceleration, cornering, and deceleration. Subsequently, the system achieves the required system output in cooperation with a dedicated energy management system. It also works with each vehicle system, such as drive force control, sound control, and meter cluster, to stimulate all five senses of the driver

Murata, Naoya, Narimoto, Ryosuke, Saito, Masatoshi, Ishida, Daichi, GUNJI, HIROKI, UKAI, YOHEI, Kurachi, Shinobu, SHIKI, KAZUKI, Nagakura, Akari, Maeda, Sadaharu, Mitogawa, Terumasa

Advancing Safe Battery Design through Experimental Cell Characterization of Thermal, Electrical and Mechanical Properties

2026-01-0408To be published on 04/07/2026

Lithium-ion batteries are critical to electric vehicles and grid-scale energy storage. Safe design of battery systems relies on accurate simulation of battery thermal runaway under electrical, thermal, and mechanical abuse. A predictive battery simulation requires the characterization of electrical, thermal, and mechanical properties of the battery at the full cell and cell-component levels. In this study, a commercial cell from an electric vehicle was disassembled, and testing was conducted to support both homogenized and detailed computational models. At the cell level, electrical properties were characterized using Hybrid Pulse Power Characterization (HPPC) testing to assess the cell power capability. Cell punch tests were conducted to characterize mechanical behavior under deformation. These tests were used to develop a multi-physics homogenized cell model. On the other hand, detailed cell modeling that includes different component layers could help users understand localized cell

Challa, Vidyu, Rostami-Angas, Masoud, kong, Kevin, Wang, Leyu, Reichert, Rudolf, Kan, Cing-Dao

On-Engine Measurement of Automotive Turbocharger Turbine Blade Vibration

2026-01-0201To be published on 04/07/2026

Automotive turbochargers are designed to avoid resonance of the turbine blades and backwall, which can result in High Cycle Fatigue failures. Blade Tip Timing is a method of turbomachinery testing which utilizes fiber optic probes to measure turbine blade displacements in real time on turbochargers spinning at upwards of 150,000 RPM. Historically, Blade Tip Timing measurements of automotive turbochargers have been made under steady-state conditions using a Hot Gas Stand. General Motors conducted testing of a turbocharger on a running engine to capture realistic exhaust pressure dynamics. A reference turbocharger was measured on an engine testbed running a production calibration; the same turbocharger was then tested on a Hot Gas Stand to observe how the blade behavior differed. Blade displacements were found to be lower on engine, because the dynamics of engine pulsation reduced the in-phase work available to drive the turbine blades, resulting in lower blade stresses and an

SCHWARZ, JORDAN, Goodheart, Rachel, Tappert, Peter, DePaoli, Dominic, Longacre, Christian

An Efficient Approach to Key Negotiation in an Automotive Environment

2026-01-0087To be published on 04/07/2026

Negotiating Keys for applications such as message authentication within a vehicle presents many problems as, in designing the algorithm; the algorithm must be able to be utilized by small, fixed-point processors. In addition, if there is a desire to do this algorithm in the manufacturing environment, there are severe time constraints placed on how long this algorithm can take, as there are strict station time requirements, which are expensive to change, and any time utilized in the plant can negatively affect vehicle throughput. Additionally, negotiating these keys between many ECUs can greatly increase the time required to negotiate a common key using standard multi-party Diffie-Hellman. Timing would also be an issue in the case of using pair-wise Diffie-Hellman for encryption and distribution of keys utilizing a key master. To solve these problems in multi-party key negotiation, we have utilized the Elliptic Curve variation of the Burmester-Desmedt (ECBD) algorithm. ECBD is

Van Dam, Theo, Mazzara, Bill

Time-of- Flight (ToF) estimation using the Discrete Logarithmic Frequency (DLF) method

2026-01-0019To be published on 04/07/2026

When incorporated into the small, curved, and enclosed spaces of contemporary automotive lighting systems, conventional measurement tools like photodetectors, optical sensors, and laser-based systems frequently encounter difficulties. Physical space restrictions, light attenuation along curved surfaces, and the high energy consumption of conventional sensors—particularly in intelligent headlight modules and advanced driver-assistance systems (ADAS)—are some of these limitations. To address these issues, this work presents a novel method for Time-of-Flight (ToF) estimation based on the Discrete Logarithmic Frequency (DLF) approach. The suggested method improves the accuracy and dependability of ToF-based measurements by examining the time-frequency correlations of modulated light signals sent through optical channels in automotive systems. Applications like adaptive beam shaping, vehicle-to-vehicle (V2V) optical communication, and in-lens diagnostics benefit greatly from this. The

Chinni, Venkata Sai Sandeep

A Shared Autonomy Architecture: Utilizing an Advanced Personalized Control System (APeCS) and Shared Autonomy Arbiter (SAB)

2026-01-0147To be published on 04/07/2026

The shared autonomy framework has become an option with great potential in the field of autonomous vehicles. Human and machine control decisions typically demonstrate strengths in different scenarios. As a result, the robustness of systems can be enhanced by the collaboration between humans and autonomy. A shared autonomy architecture that takes into account both human and environmental factors was proposed in this work. The authority distribution between the human operator and the autonomy algorithm was determined by the Shared Autonomy Arbiter (SAB). Designed with a two-tier structure, the SAB incorporated a policy-level decision module, as well as a numerical-level arbitration tuning module. A fuzzy inference system (FIS) was incorporated to enhance the noise tolerance of the policy selection module. Furthermore, the human factor was taken into account by applying a projection to the users’ control input. The human operator’s control decision was projected by the Adaptive

Sang, I-Chen, Norris, William, Patterson, Albert, Sreenivas, Ramavarapu S., Soylemezoglu PhD, Ahmet, Nottage, Dustin S.

A System-Level Calibration Framework for Embedded Vision: Integration of Sensor, Firmware, and Software Enhancements

2026-01-0013To be published on 04/07/2026

Embedded vision systems are essential for contemporary applications, including robotics, advanced driver assistance systems (ADAS), and intelligent surveillance; yet they frequently experience diminished image quality due to resource constraints, environmental variability, and inconsistent illumination conditions. Such degradations impact multiple visual attributes-sharpness, contrast, color accuracy, noise levels, and structural similarity-that are critical for reliable perception in safety- and performance-driven domains. This study introduces a comprehensive system-level calibration architecture that integrates three coordinated layers: sensor-level adjustment, firmware optimization, and adaptive software enhancements. At the sensor level, exposure control, gain tuning, and white balance adjustments mitigate luminance imbalance and color shifts under changing light conditions. Firmware optimization leverages image signal processor (ISP) parameters to reduce temporal and spatial

Indrakanti, Rama Kiran Kumar, Vishnoi, Nitin, KAMADI, VENKATA

Modeling Non-Uniform Temperature Distribution in Lithium-Ion Batteries

2026-01-0120To be published on 04/07/2026

Effective thermal management is critical to the performance, lifespan, and safety of lithium-ion batteries in electric vehicles (EV). Transient and non-uniform temperature distributions within cells accelerate degradation, cause pack imbalance, and reduce efficiency. Localized hotspots, in particular, promote lithium plating, solid electrolyte interface (SEI) growth, and other failure mechanisms. Persistent non-uniformities increase the likelihood of thermal runaway under high-stress operation. To address these challenges, this work develops a one-dimensional thermal model that captures heat generation and dissipation under dynamic operating conditions, incorporating both conduction within the cell and active convective cooling. Using a DFSS-based approach, we evaluate design and cooling strategies that minimize temperature differentials while maintaining acceptable operating ranges. The findings provide practical guidance for battery thermal management system design, emphasizing

El-Sharkawy, Alaa, Asar, Mona, Serpento, Stan, Sheta, Mai

Low-Cost Packaging of SiC Power Modules for EV Traction Inverters

2026-01-0119To be published on 04/07/2026

The increasing demand for efficient and cost-effective power electronics in electric vehicle (EV) traction inverters necessitates innovative power module designs. This paper presents a novel SiC power module packaging focused on low-cost fabrication and assembly.. The proposed design features a simplified sandwich structure consisting of a lead frame, the innovative epoxy composite deposited on a high-performance cold plate. The novel epoxy composite eliminates the critical solder connection typically found between the DBC/AMB and the cold plate. This significantly streamlines the thermal path by reducing the number of material layers and interfaces between the SiC chip and the coolant. As a result, the integrated stack-up achieves significantly low junction-to-coolant thermal resistance (Rth) while delivering substantial cost savings related to power module module packaging. This work provides the details on the power module prototype fabrication steps and functionality including the

Chen, Yu, Mena-Garcia, Javier, Chen, Hao, Xiao, Kewei, Gupta, Man Prakash, Degner, Michael

Characterizing Regenerative Braking of Tesla Model 3 Using Dynamometer Testing

2026-01-0453To be published on 04/07/2026

Regenerative braking has a strong influence on the energy efficiency and drivability of battery-electric vehicles. This study establishes an empirical baseline analysis under controlled conditions of the regenerative braking behavior of the 2020 Tesla Model 3 to support the interpretation of on-road performance and serve as a reference for subsequent testing and analysis. The tests were performed on a four-wheel-drive chassis dynamometer at Argonne National Laboratory, combining Multi Cycle Testing (MCT) to simulate real world driving patterns (city, highway) with coast-down tests to isolate periods where the motor is operating in regen mode and compare the behavior across different parameters. Vehicle data was collected from the vehicle using taps in the Controller Area Network (CAN) bus as well as a high-resolution power analyzer. The vehicle displayed the highest efficiency during simulated city driving conditions (3.62 miles/kWh followed by highway (3.40 miles/kWh) and aggressive

Pierce, Benjamin Branch, Di Russo, Miriam, Das, DEBASHIS, Zhan, Lu

From LLM to Deep Learning: Efficient Simulation of Last-Mile Energy Behavior in Campus Communities

2026-01-0461To be published on 04/07/2026

Communities are critical nodes in the urban energy network, integrating energy, transportation, and building systems to enhance overall energy efficiency. Accurate management of these systems depends on characterizing individual energy-demand behaviors. However, traditional last-mile behavioral models often fail to capture the complex and non-linear nature of human decision-making, creating a need for advanced simulation techniques. Although agent-based simulations powered by Large Language Models (LLMs) have demonstrated considerable efficacy across diverse domains, their substantial computational demands—specifically in terms of token consumption—pose pronounced constraints in large-scale simulations involving tens of thousands of agents, considerably limiting their practical applicability. To address this challenge, a deep learning-based approach is proposed for single-step prediction of last-mile behavior. A hybrid architecture, consisting of an MLP encoder and a Transformer

Yang, Zhifeng, Chen, Yongjian, Ou, Shiqi(Shawn)

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

2026-01-0205To be published on 04/07/2026

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

Ignition and Flame Characteristics of the Ultra-high Pressure Hydrogen Jets

2026-01-0334To be published on 04/07/2026

This study investigates the ignition and flame characteristics of ultra-high-pressure hydrogen jets under elevated pressure and temperature conditions, with relevance to next-generation zero-carbon propulsion systems. Experiments were conducted in a high-pressure, high-temperature optical chamber at ambient temperatures ranging from 900 to 1200 K, oxygen concentrations between 12% and 18%, and injection pressures from 200 to 600 bar. High-speed optical diagnostics were employed to resolve the multi-scale flame development process. Planar OH* chemiluminescence imaging was used to track high-temperature reaction zones, schlieren imaging provided visualization of global jet penetration and flame propagation, and natural flame luminosity recorded with a color camera revealed the visible flame evolution. The combined measurements enabled a comprehensive assessment of auto-ignition behavior, ignition delay, and subsequent flame stabilization under extreme injection conditions. Results

Cheng, Qiang, Grahn, Viljam, Ahmad, Zeeshan, Hyvonen, Jari, Vuorinen, Ville, Kaario, Ossi

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