Motivated by the inclusion of active flow control provisions in the 2026 Formula One regulations, and building upon previous studies of Trapped Vortex Cavity (TVC) implementation in inverted front wings, this paper investigates the effectiveness of TVC as a flow control mechanism applied to vehicle diffusers. Both active and passive configurations were considered for three diffuser geometries: a base straight-line diffuser, an inverted airfoil-shaped diffuser, and a diffuser inspired by a Formula One car. The study employed numerical simulations to evaluate the aerodynamic performance and the potential benefits of integrating TVC systems. Across all types of diffusers, the implementation of a circular TVC cavity resulted in a significant improvement in the lift-to-drag ratio (CL/CD). In the active flow control configuration, a 10% improvement was observed in the straight diffuser under a limited mass-flow rate. With optimized cavity positioning and radius, the airfoil-shaped and

Ming Kin, NG, Teschner, Tom-Robin

Hierarchical Control Strategy Evaluation and Migration for a Four-Degree-of-Freedom Quarter Commercial Vehicle

02-19-03-0014

4/9/2026

In order to improve the comfort performance in commercial vehicles, this study proposes a hierarchical control strategy that integrates the evaluation and migration of control algorithms. First, a quarter-vehicle model with four-degree-of-freedom (4-DOF) is constructed, incorporating the dynamics of the wheel, frame, driver’s cab, and seat. The key modal characteristics of the model are then verified through amplitude–frequency analysis, confirming their consistency with the typical vibration patterns observed in actual commercial vehicles, which provides the foundation for subsequent control strategy evaluation and migration. Then, based on a standard two-degree-of-freedom (2-DOF) suspension model, a weighted comprehensive evaluation function is developed to account for comfort, structural safety, handling stability, and both time- and frequency-domain performance indicators. Using this evaluation function, various control algorithms—including Skyhook control (SH), acceleration-based

Pan, Ting, Pang, Jianzhong, Wu, Jinglai, Zhang, Jiuxiang, Kang, Gong, Zhang, Yunqing

Acoustic Improvements to a Passenger Service Unit Panel with Core Filler and Facesheet Patches using Topology Optimization

2026-01-0511

4/7/2026

Passenger comfort is becoming the forefront of luxury private jets where noise needs to be kept to a minimum. One source of structure-borne noise is the vibration of the Passenger Service Unit (PSU) panel. These vibrations originate from the outer skin, excited by turbulent boundary layer, and are transmitted through the fuselage frame to the PSU panel. This panel resides overhead of passenger seating, it is composed of a corrugated honeycomb core sandwiched between thin face-sheets. This paper presents a systematic approach to improve the vibro-acoustic performance of a honeycomb core sandwich structure by employing core filler and facesheet patches. Topology Optimization (TO) is used to determine the optimal layouts of these design modifications. The vibro-acoustic performance of the PSU panel with facesheet patches and core filler is evaluated using a frequency response analysis in the commercial finite element solver OptiStruct. The effectiveness of vibration reduction will be

Russo, Connor, Whetstone, Isobel, Patel, Anuj, Wotten, Erik, Kim, Il Yong

Integrated Welding Simulation and Experimental Characterization of Weld-Metal and Heat-Affected-Zone Strength in AA6063-T6

2026-01-0230

4/7/2026

In the context of automotive lightweighting and efficient manufacturing, welding is a key joining method for aluminum body structures due to its maturity, versatility, and cost effectiveness. This study investigates MIG butt welding of AA6063-T6 sheets using a sequential thermo-mechanical finite element model with a double-ellipsoid heat source. Thermocouple histories and macroscopic metallography of the weld-pool morphology are used to validate the predicted temperature field, and post-weld deformation measured by a coordinate measuring machine is compared with the simulation to confirm overall model reliability. Hardness mapping across the joint partitions the material into weld metal (WM), heat-affected zone (HAZ), and base metal (BM). Miniature tensile specimens extracted along the weld provide local mechanical properties, from which linear strength–hardness relations are established. Building on these results, a five-material equivalent strength model covering WM, HAZ-I, HAZ-II

Shao, Jiyong, Meng, Dejian, Xiang, Yao, Gao, Yunkai

Understanding Adversarial Transferability in Vision-Language Models for Autonomous Driving: A Cross-Architecture Analysis

2026-01-0170

4/7/2026

Vision-language models (VLMs) are increasingly used in autonomous driving because they combine visual perception with language-based reasoning, supporting more interpretable decision-making, yet their robustness to physical adversarial attacks, especially whether such attacks transfer across different VLM architectures, is not well understood and poses a practical risk when attackers do not know which model a vehicle uses. We address this gap with a systematic cross-architecture study of adversarial transferability in VLM-based driving, evaluating three representative architectures (Dolphins, OmniDrive, and LeapVAD) using physically realizable patches placed on roadside infrastructure in both crosswalk and highway scenarios. Our transfer-matrix evaluation shows high cross-architecture effectiveness, with transfer rates of 73–91% (mean TR = 0.815 for crosswalk and 0.833 for highway) and sustained frame-level manipulation over 64.7–79.4% of the critical decision window even when patches

Fernandez, David, MohajerAnsari, Pedram, Salarpour, Amir, Pese, Mert D.

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

2026-01-0010

4/7/2026

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

Feng, Runlei, Wang, Ning, Zhang, Zhihao

From Algorithm to Engineering Judgment: Navigating Topology Optimization’s Limits in Crash Applications

2026-01-0189

4/7/2026

The non-linear nature of crash scenarios has led to many designs being developed through extensive trial and error based on the intuitions of the design engineer. As such, effectively utilizing topology optimization for crash applications offers opportunities to provide major improvements in cost, weight, and passenger safety. Topology optimization is known for creating stiff, lightweight structures, however its application to crash scenarios must be handled carefully. Compliance minimization, the most common optimization objective, can yield misleading designs that prioritize undesirable qualities when developing structures for crash applications. In this paper, the design process of a passenger seat assembly subject to sequentially applied enforced displacement, and crash deceleration loads is discussed. Due to the conflicting nature of compliance minimization and enforced displacement, the design was split into two types of regions; sacrificial, which are regions manually designed

Orr, Mathew, Shi, Yifan, Lee, Jake, Gray, Savannah, Park, Taeil, Wotten, Erik, LeFrancois, Richard, Huang, Yuhao, Patel, Anuj, Kim, Hansu, Burns, Nicholas, Jalayer, Shayan, Grant, Robert, Kok, Leo, Hansen, Eric, Kim, Il Yong

Development and Validation of Torque Vectoring and Traction Control Strategies in a FSAE Vehicle Using a Dual-Axle Dynamometer HiL Platform

2026-01-0644

4/7/2026

This study presents the vehicle control optimization of a Formula SAE (FSAE) electric vehicle developed by National Taiwan University Racing Team (NTU Racing), utilizing a dual-axle dynamometer and a real-time Hardware-in-the-Loop platform from Chroma. The novelty of this work lies in the comprehensive system-level validation of independent torque control strategies, namely Torque Vectoring (TV) and Traction Control (TC), implemented directly within the vehicle control unit (VCU), and the high-fidelity simulation of dynamic driving scenarios based on the FSAE circuit. The vehicle features an independently controlled rear-axle, two-wheel drive (2WD) configuration, consisting of two in-wheel motors, self-developed inverters, and planetary gearboxes. During testing, a pre-built CarSim driver model provides throttle, brake, and steering inputs to the VCU via Controller Area Network (CAN) interface. The VCU, in turn, computes the independent torque commands according to the TV and TC

Hsiao, Tsung-Yu, Chen, Zhi-Ren, Jian, Rong-Wei, Chen, Tai-Hsiang, Wang, Tai-Jie, Hu, Wei-Zhe, Ho, Hui-Ting, Wu, Ting-Yu, Lin, Ting-He, Chiu, Joseph

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

2026-01-0650

4/7/2026

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

Liu, Xiyuan

Multi-Physics Topology Optimization and Neural Network–Based Design of Experiments for Lightweight Automotive Cast Components

2026-01-0510

4/7/2026

This paper presents a hybrid optimization framework that integrates Multi-Physics Topology Optimization (MPTO) with a Neural Network–surrogated Design of Experiments (NN-DOE) to enable lightweight structural design while satisfying crashworthiness, durability, and noise, vibration, and harshness (NVH) requirements under practical casting and packaging constraints. In the proposed MPTO formulation, crash and durability performances are incorporated through equivalent static compliance measures, while NVH performance is assessed using a frequency-domain dynamic stiffness metric, allowing consistent evaluation of trade-offs among competing design requirements. The framework is first demonstrated using a mass-produced passenger-car lower control arm (LCA) as a benchmark component. In this application, MPTO achieves weight reduction under multi-physics objectives by removing non-load-bearing material. Results show that single-discipline optimization produces unbalanced topologies, while

Kim, Hyosig, Senkowski, Andres, Gona, Kiran, Saroha, Lalit, Boraiah, Mahesh

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

2026-01-0648

4/7/2026

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

Altinbas, Koray, Soares, Renan F.

Safety-Constrained Control of Nonlinear Mobility Systems Using Reference Governor

2026-01-0033

4/7/2026

Ensuring safe operation and reliable control of mobility systems remains a significant challenge, particularly for nonlinear and high-dimensional applications subject to external disturbances with hard constraints and limited computational resources in real-time implementations. A reference governor (RG) can enforce constraints using an add-on scheme that preserves the pre-stabilizing controller while balancing the need to satisfy other requirements, including reference tracking and disturbance rejection. Thus, in this paper, we exploit RG-based strategies focusing on nonlinear mobility systems. While the method is generalizable to other applications, such as waypoint following for autonomous driving, the flight dynamics of a quadrotor system with twelve states are used as an example. We implement a disturbance rejection RG to satisfy safety constraints and track set points. To handle nonlinearity, we propose an optimal strategy to quantify the maximum deviation between the nonlinear

Dong, Yilong, Li, Huayi

Automotive Crash Dynamics Modeling Accelerated with Machine Learning

2026-01-0568

4/7/2026

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

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

Anisotropic Mechanical Property Simulation of Fiber Reinforced PEEK Using Composite Theory and Comparison to Experimental Results

2026-01-0256

4/7/2026

The mechanical properties of 3D printed composites have been shown to vary due to the manufacturing infill direction due to artifacts from the printing process. PEEK (Polyether Ether Ketone) and PEEK reinforced with carbon fiber were studied for these experiments because they are widely used for their high strength properties. 3D printed composites that behave with anisotropic characteristics have been evaluated under Laminate Composite Theory (LCT), which can be used to determine the mechanical properties of these 3D printed composites. By changing the orientation of the extruded strands in a 3D printed part, the structure can be optimized in a specific orientation for specific loading conditions, and LCT can be applied for simulating mechanical responses. Three point bending tests were performed on rectangular 3D printed samples and compared to a 3D simulation using LCT for a similar bending load. This allows for the use of LCT in combination with a finite element software such as

Bradley, Coilin, Garcia, Jordan, Sibley, Brian

Optimizing EV Battery Models for Full Vehicle Simulations

2026-01-0389

4/7/2026

The increasing adoption of electric vehicles (EVs) demands accurate yet computationally efficient battery models that can be integrated into full vehicle simulations. At the cell level, mechanical battery models often employ fine-scale elements to capture localized deformation and failure phenomena. While such detailed discretization enables high-fidelity predictions, it also imposes significant computational costs that become prohibitive when scaling up to pack-level or full-vehicle crash and durability simulations. This research addresses the challenge by systematically simplifying cell-level mechanical models to reduce computational burden while preserving predictive accuracy. We propose an approach in which larger elements and reduced complexity representations are introduced without compromising the model’s ability to replicate experimentally observed behaviors. The methodology emphasizes model validation against targeted loading conditions, ensuring that the essential mechanics

Sahraei, Elham, Parmar, Dhruv, Muralidharan, Umachandran

Application of Topology Optimization to Production-Ready Passenger Seat Components Design

2026-01-0498

4/7/2026

Lightweighting of components has become a key challenge in the development of modern transportation systems. In the automotive and aerospace industries, the overall mass of a vehicle has a significant impact on its fuel efficiency and manufacturing cost. Therefore, the lightweight design of vehicle components is crucial in the industrial field. Topology optimization (TO) is a computational design approach aimed at achieving lightweight designs. However, most existing studies focus on simplified academic models, with limited demonstration in real-world applications. This paper presents a revised TO workflow to obtain production-ready design and a practical implementation of TO in the design of three structural components in the aerospace industry: seatback frame, seat fuselage mount, and seat spreader. The revised TO workflow incorporates the practical demands of industry, including enhanced manufacturability and cost efficiency through TO design. The resulting designs are evaluated to

Lee, Hanbok Jake, Shi, Yifan, Gray, Savannah, Orr, Mathew, Park, Taeil, Wotten, Erik, LeFrancois, Richard, Huang, Yuhao, Patel, Anuj, Kim, Hansu, Jalayer, Shayan, Burns, Nicholas, Hansen, Eric, Grant, Robert, Kok, Leo, Kim, Il Yong

Design and Evaluation of a Deep Neural Network Model for Prediction of Diesel Equilibrium Combustion Products and Characteristics

2026-01-0293

4/7/2026

Accurate prediction of equilibrium combustion products and thermodynamic properties is essential for optimizing engine performance, enhancing combustion efficiency, and reducing emissions in diesel-powered systems. Traditional methods for combustion modeling often involve solving complex chemical equilibrium equations or thermodynamic relations, which could be computationally expensive and time-consuming. In this study, we present a data-driven approach using a deep neural network (DNN) model to predict the equilibrium combustion products and key thermodynamic characteristics of diesel under varying thermodynamic conditions. The proposed DNN model is trained on a comprehensive dataset generated from equilibrium calculations. The inputs include pressure, temperature, and equivalence ratio, covering a relatively wide range to encompass diesel equilibrium combustion under various conditions. Outputs are equilibrium combustion products and thermodynamic properties, including enthalpy

Ji, Huangchang, Wang, Kai, Guo, Zhefeng, Han, Yang, Lee, Timothy

Multi-Objective Reinforcement Learning Framework for Transmission Shift Schedule Optimization

2026-01-0162

4/7/2026

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

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

A System of System Design Framework for Multi-Domain Vehicles

2026-01-0263

4/7/2026

Traditionally, ground vehicle design is based on identifying engineering solutions that fulfil the requirements and specifications put forth by the stakeholders. Although a vehicle is a single entity, it is composed of many subsystems and thousands of parts that must operate together in unison to meet all design goals. A System of Systems (SoS) design approach enables the consideration of subsystem performance within a framework of overall system operation, which includes possible tradeoffs. This collaborative approach to subsystem and primary system design draws upon modelling, optimization, tradespace analysis and virtual studies. In this paper, a system of system design approach will be investigated for a collection of multi-domain vehicles assembled to undertake coordinated search and rescue operations on land and water. A host ground vehicle, an unmanned aerial drone, an unmanned marine drone and an unmanned tracked vehicle constitute the family of multi-domain vehicles which will

Somanchi, Anang, Abeynayake, Chandima, Deshmukh, Mrunal, Suresh, Johir, Ramnath, Satchit, Turner, Cameron, Schmid, Matthias, Castanier, Matthew P., Rapp, Stephen, Jaczkowski, Jeffrey J., Wagner, John

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

2026-01-0214

4/7/2026

The push for vehicle development through virtual prototyping and testing in motorsports highlights the critical challenge of tire model selection and calibration, especially when vehicle dynamics must be accurately captured. The calibration process for tire models such as the Pacejka Magic Formula (MF) relies on parameter identification and experimental data fitting. While optimization algorithms have been implemented to calibrate tire models, few studies explore the effects of parameter selection on overall vehicle performance, complicating prioritization for the vehicle’s modeling and simulation strategy. To bridge this gap, this paper leverages optimal control methods to quantify how the variability of MF tire model parameters propagates to the overall vehicle model and impacts lap time prediction accuracy. To achieve this, a subset of parameters critical to combined slip of the MF tire model are varied through a Design of Experiments (DOE). These variations are executed on a flat

Zarate Villazon, Angel M., Brown, Ian, Balchanos, Michael, Mavris, Dimitri

A Decision-Making Framework for Field Manufacturability of Vehicle Parts in Expeditionary Environments

2026-01-0143

4/7/2026

Expeditionary environments (such as remote exploration missions, forward military operations, and disaster response zones) demand adaptive manufacturing solutions to support vehicle sustainment in the absence of traditional supply chains. This work introduces a conceptual mathematical framework for modeling the constraints and tradeoffs inherent to expeditionary manufacturing, with a focus on vehicle repair and spare parts fabrication using low-energy and simple automated systems including desktop-scale 3D printers and CNC machines. The model integrates key variables such as energy availability, material transport cost, fabrication time, and environmental limitations to support rapid decision-making on part manufacturability and in-field feasibility. A case study involving the on-demand production of some common wear and failure parts on a vehicle, including suspension components and the water pump, is used to demonstrate how this framework can guide the selection of suitable

Mollan, Calahan, Pandey, Vijitashwa, Patterson, Albert E.

Lightweight Design of a Seat Module Assembly for a Robotic Arm Amusement Ride System Using Topology Optimization

2026-01-0481

4/7/2026

This paper presents a methodology for the design of a lightweight seat module assembly (SMA) for an indoor robotic arm amusement ride. Typical SMA designs begin with a welded metal frame, and the exterior shell serves only as a non-structural cover, resulting in stress concentrations and excess weight. The proposed methodology introduces a bottom-up process that integrates topology optimization at the outset, enabling the outer shell to function as a primary load path and subsequently identifies the ideal configuration for internal secondary framing by utilizing manufacturing constraints. This approach is further enhanced by adopting fiber-reinforced polymers as the structural material, leveraging their high stiffness-to-weight ratio to replace conventional metallic designs. Multiple manufacturing-specific interpretations of the optimized design were explored to evaluate feasibility, including extrusion and tubing-based approaches. Finite element analysis of the final design under high

Pooler, Claire, Hronowsky, Benjamin, Chai, Kevin, Shi, Yifan, Park, Taeil, Lo, David, Kim, Il Yong

High Order Control Lyapunov Function - Control Barrier Function - Quadratic Programming Based Autonomous Driving Controller for Bicyclist Safety

2026-01-0034

4/7/2026

Ensuring the safety of Vulnerable Road Users (VRUs) is a critical challenge in the development of advanced autonomous driving systems in smart cities. Among vulnerable road users, bicyclists present unique characteristics that make their safety both critical and also manageable. Vehicles often travel at significantly higher relative speeds when interacting with bicyclists as compared to their interactions with pedestrians which makes collision avoidance system design for bicyclist safety more challenging. Yet, bicyclist movements are generally more predictable and governed by clear traffic rules as compared to the sudden and sometimes erratic pedestrian motion, offering opportunities for model-based control strategies. To address bicyclist safety in complex traffic environments, this study proposes and develops a High-Order Control Lyapunov Function–High-Order Control Barrier Function–Quadratic Programming (HOCLF-HOCBF-QP) control framework. Through this framework, CLFs constraints

Chen, Haochong, Cao, Xincheng, Guvenc, Levent, Aksun Guvenc, Bilin

Optimization Method for Vehicle Shape to Drastically Reduce Aerodynamic Drag in Heavy-Duty Vehicles

2026-01-0603

4/7/2026

Since air drag is proportional to the square of the speed, it is expected that reducing air drag will significantly improve fuel efficiency for on-highway trucks and buses, which are often driven at high speeds. Therefore, the purpose of this study is to propose an optimization method for vehicle shape to drastically reduce aerodynamic drag in heavy-duty vehicles. Using NSGA-II, one of a genetic algorithm, the overall vehicle shape was optimized with drag coefficient (CD) and lift coefficient (CL) values as objective functions and design variables as parameters in a total of 13 locations. Among the Pareto solutions, an 86% reduction in CD was achieved compared to the base shape when the CD value was the lowest. Since the CL value remains low with this shape, it can be seen that driving stability does not deteriorate. Among the design variables in optimization, it was confirmed that the corner radius of the vehicle side was particularly effective in reducing the CD value. In addition

Kawano, Daisuke

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

2026-01-0272

4/7/2026

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

de Carvalho Pinheiro, Henrique, Sartoretti, Enrico

Optimizing Weld Placement in Automotive Structures Using Topology and Multi-Disciplinary Approaches

2026-01-0514

4/7/2026

This work presents two approaches for weld optimization aimed at reducing manufacturing cost and process time, while meeting structural performance requirements in automotive structures. The first approach uses topology optimization to identify the most efficient weld layouts. A design space is generated along mating flanges, joints, and panel interfaces, where potential weld locations are defined. Welds are treated as discrete design variables, and the topology optimization systematically evaluates their contribution to global stiffness and load path integrity. Non-critical welds, those with minimal impact on stiffness, durability, or crashworthiness, are eliminated, resulting in a minimized weld pattern that maintains structural performance. The second approach applies Multi-Disciplinary Optimization (MDO) to balance weld reduction with performance targets across multiple domains, including linear and non-linear stiffness, crashworthiness, and fatigue. Using a preprocessing tool

Koppaka, Vinaya, Yoo, Dong Yeon, Chavare, Sudeep

Thermal Management of Gasoline Fuel Tanks

2026-01-0114

4/7/2026

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

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

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

2026-01-0035

4/7/2026

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

Shang, Kai, Wang, Ning

Optimal Control Analysis of Minimum-Time Maneuvers for Vehicles Recovering from Instability

2026-01-0218

4/7/2026

Vehicles may enter highly unstable dynamic states due to lateral collisions, sudden loss of grip, or extreme steering disturbances. When such instability arises in congested road sections where obstacle avoidance is required, the safety risk to both the ego vehicle and surrounding traffic escalates significantly. In such scenarios, the vehicle must not only regain stability but also navigate the roadway in the shortest feasible time to prevent secondary collisions. This paper investigates the minimum-time maneuver of a vehicle starting from an unstable dynamic condition and constrained to travel within prescribed road boundaries. A single-track vehicle model with combined-slip nonlinear tire model is employed to capture the vehicle dynamics under high slip conditions. Phase-plane analysis is conducted to reveal how control inputs reshape the system’s vector field and influence the possibility and speed of stability recovery. An optimal control problem is formulated to compute the

Leng, Jiatong, Yu, Liangyao, Wang, Yongxin, You, Weijie, Li, Ziang, Jin, Zhipeng

Multi-Material Topology Optimization Using Difference-Based Equivalent Static Load Method

2026-01-0173

4/7/2026

Topology optimization (TO) has become a powerful tool for generating lightweight structural designs. TO has been widely applied to linear static problems, where analytical sensitivities are easy to obtain. However, crashworthiness design requires nonlinear dynamic analysis, for which analytical sensitivities are generally not available. To extend TO into crash problems, approximation methods such as the Equivalent Static Load (ESL) method have been developed. ESL replaces the nonlinear problem with a series of linear static subproblems, ensuring that the displacement fields match at certain time steps. These subproblems can then be efficiently solved using standard TO techniques. A key limitation of ESL is that it relies on the initial mesh for all subproblems, which reduces accuracy for highly nonlinear crash responses. To address this, Triller proposed the difference-based ESL (DiESL) method, which updates the mesh in each subproblem to the deformed configuration, therefore improving

Huang, Yuhao, Kim, Il Yong

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

2026-01-0507

4/7/2026

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* path-finding algorithm and gradient based optimization to determine the optimal route layout between port points

LeFrancois, Richard, Kim, Il Yong

AI-Powered Requirements Engineering & Design Optimization of Electric Powertrains

2026-01-0500

4/7/2026

The development of electric vehicle powertrains is driven by diverse and often conflicting requirements. In early development phases, these requirements are often vague, incomplete, continuously refined and subject to change as development progresses. Moreover, powertrain designs must be competitive regarding multiple key performance indicators (KPIs) such as performance, cost, energy efficiency, and package integration. This challenges engineers to concurrently develop the powertrain design alongside the requirements on which the design is based on. Managing this combination of uncertain requirements and multi-KPI design optimization represents a complex challenge in automotive engineering. The present work introduces a requirements engineering approach based on OPED (Optimization of Electric Drives). OPED digitalizes the transition from requirements to technical solutions by integrating parametric system models with an AI-based evolutionary optimization algorithm. This enables

Hofstetter, Martin, Lechleitner, Dominik

CFD-Guided DoE-ML Optimization Methods in a Heavy-Duty Hydrogen Engine

2026-01-0326

4/7/2026

This study introduces a CFD-guided design of experiments (DoE) and machine learning (ML) framework for the co-optimization of piston and pre-chamber geometries in a passive pre-chamber heavy-duty hydrogen engine operating at medium and low loads. Starting from a reference configuration, an omega-type piston and a methane-optimized pre-chamber, the design space was parameterized using seven geometric variables. A Sobol sequence was employed to generate 96 randomized design variants in the DoE, each evaluated through high-fidelity 3D-CFD simulations to capture key combustion and performance metrics. The resulting dataset served as the foundation for developing and evaluating several ML regression models. A rigorous ML workflow was adopted, featuring 5-fold cross-validation and hyperparameter tuning via Bayesian optimization to ensure generalization and robustness. Model selection was based on multi-metric performance criteria including prediction accuracy, error stability, and

Menaca, Rafael, Shakeel, Mohammad Raghib, Liu, Xinlei, Mohan, Balaji, AlRamadan, Abdullah, Cenker, Emre, Silva, Mickael, Zhang, Anqi, Pei, Yuanjiang, Im, Hong

From Propane to Pi: Affordable AI-Driven Solutions for Sustainable Bus Heating

2026-01-0164

4/7/2026

This paper proposes an intelligent, artificial intelligence (AI) enabled seat heating system for school buses that saves energy by only activating heating elements when a passenger is identified. A custom-trained YOLOv8 deep learning model identifies passengers in real time and opens/closes real-time control of the individual electric seat heaters via a Raspberry Pi 5. The detector achieves around 10 frames-per-second (FPS) of inference on the Raspberry Pi 5 and 80–90 FPS on a laptop with over 92% detection confidence across various illumination conditions. Energy modeling shows the anticipated demand for a 10-kW propane-based heater is approximately 75% lower by implementing a 2.52 kW electric seat-heating system. In a typical operation schedule of 540 hours a year, this results in 4,000–5,000 kWh of annual savings, $465–$579 of annual cost savings and mitigates 0.9–1.3 t CO₂ per bus, annually. When implemented at the fleet level, the energy and cost saving will be in proportion. This

Chikkala, Daney Bhargav, Zadeh, Mehrdad, Tan, Teik-Khoon, Ponnam, Jitin, Batte, Jai Rathan

Ensemble of Experts for Generating Digital Parking Maps from Remote Sensing Images

2026-01-0107

4/7/2026

A digital parking map with precise parking spot geospatial information is crucial for tasks such as automatic valet parking, parking spot recommendations, and parking route optimization. This paper presents a parking map generation scheme that extracts high-definition parking spot geometry from remote sensing images. These images often suffer from occlusion, inconsistent resolution, and varying luminosity conditions. The proposed scheme utilizes a model ensemble paradigm, integrating multiple machine learning models to enhance the accuracy and quality of the generation of parking maps. The experiments demonstrate that the proposed scheme achieves an 80.5% parking spot detection precision and a center-to-center geometric representation error of 0.93 meters.

Shukla, Ajitesh, Cao, Xiaofei, Liu, Yongkang, Takeuchi, Yusuke, Sisbot, Akin

Effect of Housing Design on oil Splash Lubrication in an E-Drive Unit through Computational Fluid Dynamics Simulation

2026-01-0116

4/7/2026

Effective lubrication of gears and bearings is essential for optimal performance of electric vehicle (EV) e-drive units, particularly under high-speed and high-torque conditions. Rather than relying on costly and time-consuming physical prototypes with transparent casings to study lubrication, we employed Simerics-MP+ software to create a virtual testing environment. Computational Fluid Dynamics (CFD) modeling serves as a valuable alternative by enabling rapid design assessments and shortening product development cycles. This research utilizes CFD to evaluate a housing design aimed at improving lubrication in the rear and front drive units (RDU/FDU) of EVs. Multiphase flow simulations were performed using the volume of fluid (VOF) method within Simerics-MP+, which utilizes an unstructured Cartesian mesh and handles complex mesh dynamics via volume remeshing techniques. Results demonstrated that an oil collection feature enhanced lubrication by guiding oil splash towards the bearings

Kumar, P. Madhan, Motin, Abdul, Pasunurthi, Shyam Sundar, Ganamet, Alain, Maiti, Dipak, Taghizadeh, Salar, Mohapatra, Chinmoy K.

Ultra-Low Latency Video Pipeline Optimization for Perception-Centric Autonomous Systems

2026-01-0022

4/7/2026

Autonomous platforms such as self-driving vehicles, advanced driver-assistance systems (ADAS), and intelligent aerial drones demand real-time video perception systems capable of delivering actionable visual information at ultra-low latency. High-resolution vision pipelines are often hindered by delays introduced at multiple stages—sensor acquisition, video encoding, data transmission, decoding, and display—undermining the responsiveness required for safety-critical decision making. This study introduces a holistic system-level optimization framework that systematically reduces end-to-end video latency while maintaining image fidelity and perception accuracy. The proposed approach integrates hardware-accelerated encoding, zero-copy direct memory access (DMA), lightweight UDP-based RTP transport, and GPU-accelerated decoding into a unified pipeline. By minimizing redundant memory copies and software bottlenecks, the system achieves seamless data flow across hardware and software

Indrakanti, Rama Kiran Kumar

Target Cascading Kinematics and Compliance Tables to Suspension Design

2026-01-0584

4/7/2026

During the initial design phase, automotive Original Equipment Manufacturers (OEMs) require the adaptability to examine various suspension system architectures while maintaining focus on the specific performance objectives. Those requirements are expressed by Kinematics and Compliance (K&C) look-up tables and represent the footprint of what the suspension should look like in real-world applications. However, translating those requirements into the full geometric hardpoint layout is not straightforward. This process often relies on trial-and-error approaches, making it time consuming and requiring significant expertise. This challenge, known as ”target cascading,” remains a major hurdle for many engineers. The main objective of this paper is to cascade the suspension requirements from K&C look-up tables to hardpoint locations by adopting an automatic workflow and ensuring respect for constructive and feasibility constraints. Design space exploration was conducted using a robust

Brigida, Piero, Di Carlo, Paolo, Di Gioia, Niccolò, Geluk, Theo, Tong, Son, Alirand, Marc, Gorgoretti, Davide, Occhineri, Marco, Tassini, Nicola, Berzi, Lorenzo

Lightweight Wheel Design Through Shape Optimization with Structural Constraints

2026-01-0504

4/7/2026

The wheel rim is an annular, thin-walled structure featuring complex geometry and is subjected to multiple load cases, including radial, rotary, and impact scenarios. Achieving an optimal balance between mass reduction and structural performance remains a significant challenge in modern vehicle wheel design. Aero-efficient vehicles demand lightweight backbone wheels capable of accommodating aerodynamic covers without compromising handling, steering precision, or overall performance. In this study, shape optimization is applied to an 8-spoke truck wheel with the goal of minimizing mass while enhancing lateral stiffness and ensuring that stress constraints are satisfied under all critical load cases. A three-dimensional finite element model is developed and evaluated under realistic radial, rotary, and impact loading conditions representative of industry validation tests. The optimization process fine-tuned the spoke geometry using symmetric shape domains and carefully defined

Yoo, Dong Yeon, Adduri, Phani, Chakravarty, Rajan

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