Browse Topic: Vehicles, Equipment, and Performance

Items (68,836)

Disc Wheel and Hub or Drum Interface Dimensions - Truck and Bus

J694_202609 (Current)

9/28/2026

This SAE Recommended Practice contains dimensions and their tolerances concerning disc wheel to hub or drum interface areas for truck and bus applications. Disc wheels designed only for single wheel applications (not dual wheels) for light trucks and special or less common applications are not covered in this document.

Truck and Bus Wheel Committee

Principles and Process for Developing Icons for Electronic Displays

J3113_202609 (Current)

9/28/2026

SAE J3113 provides principles and a process for developing icons for use in electronic displays related to off-road work machines as stated defined in SAE J1116. Following the process ensures that icons are derived from ISO-registered graphical symbols or ISO-compliant non-registered graphical symbols.

HFTC2, Machine Displays and Symbols

Flight Deck Layout and Facilities

ARP4101A (Current)

9/22/2026

This document recommends criteria for the layout and for the design, installation, and operation of flight deck facilities for transport aircraft.

S-7 Flight Deck Handling Qualities Stds for Trans Aircraft

Carbon Fiber Fabric Repair Prepreg, 120 °C (250 °F) Vacuum Curing, Part 6 - Material Specification: Carbon Fiber Fabric Reinforced Epoxy Prepreg for Repair, Plain Weave Fabric, 193 g/m2, Adhesive Film for Repair and Non-Structural Glass Fiber Fabric Reinforced Epoxy Prepreg, 105 g/m2

AMS3970/6A (Current)

9/22/2026

AMS3970/6 Material Specification (MS) defines the requirements of carbon fiber plain weave fabric, 193 g/m2, reinforced epoxy structural prepreg for repair, curing under vacuum at 120 °C (250 °F), and a companion non-structural glass fiber fabric reinforced epoxy prepreg, 105 g/m2, used in repair of carbon fiber reinforced epoxy structures and qualified according to AMS3970/1 and AMS3970/2 for aerospace applications. The prepreg system may include an epoxy film adhesive to be applied in a co-curing process with the prepreg for joint and sandwich bonding. The need for a film adhesive shall be established during screening tests. If included, the requirements to be met by the adhesive are also defined in this document.

AMS CACRC Commercial Aircraft Composite Repair Committee

SAE TOMORROW TODAY - Engineering Mobility Standards in the Age of AI

135869/22/2026

Emerging technology is reshaping not only what engineers build, but how they build it. From software-defined and AI-powered vehicles to automation and electrification, the role of standards is changing as engineering workflows become increasingly digital, machine-readable, and AI-enabled. To learn how SAE International is navigating this next chapter, we sat down with Reuben Sarkar, CEO, to explore what's ahead for the 120-year-old organization as automotive, aerospace, commercial vehicles, and advanced air mobility increasingly converge. The conversation explores the importance of lifelong learning, the next generation of engineering talent, and how standards must evolve to keep pace in the age of AI. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.

Patterson, Lori

SAE TOMORROW TODAY - Lessons From a 7,000-Mile EV Road Trip

135859/22/2026

What does it take to drive an EV across America and back again? A 7,000-mile road trip is a great way to find out. Listen in as we sit down with science-comedian Sophie Shrand, Founder, Cool Jacket Productions and award-winning TV Host of Science with Sophie, to discuss The Great American ELECTRIC Road Trip, her exciting journey through 25 states in a Rivian R1S that puts EV infrastructure to the test and leads to unexpected discoveries along the way. From navigating EV charging networks to uncovering surprising economic opportunities, Shrand proves that curiosity and a sense of adventure can create meaningful connections with people from across the country. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.

Patterson, Lori

Stuck-Assist: A Large Multimodal Model-Based Stuck Recovery Assist for Driving on Off-Road Deformable Terrains

2026-01-7513

9/22/2026

Recent advancements in off-road autonomy have shown significant progress in perception, planning, and control frameworks, including end-to-end learning approaches. Comprehensive results have been demonstrated in both simulation and real-world experiments; however, there are significant challenges in critical cases that need further evaluation. One such challenge is the immobilization of autonomous ground vehicles (AGVs) in unstructured off-road environments, which can significantly impact agriculture, space exploration, military operations, and search and rescue missions. Addressing this problem requires recovery strategies that are context-sensitive, adaptable to terrain and vehicle conditions, and effective in integrating multimodal inputs. To this end, this paper investigates the use of a large multimodal model (LMM) providing higher-level planning assistance with human-in-the-loop evaluations for vehicle recovery after immobilization in unstructured off-road terrain. The experimental simulation platform developed was based on the Algoryx (AGX) Dynamics engine for high-fidelity terramechanics interaction and vehicle physics combined with Unreal Engine 5. This platform was further integrated with a driving simulator equipped with steering wheel and pedal interfaces for human-in-the-loop experiments. We evaluated ten representative unstuck scenarios across two deformable terrains (loose sand and compact sand) under two modes: an unskilled baseline, where participants attempted recovery unaided, and a co-intelligence mode, where participants used LMM advisory instructions. The results show that LMM assistance improved stuck recovery rates by 70% compared to unaided and unskilled human driving.

Bhosale, Mayuresh, Whitson, Jordan A., Vahidi, Ardalan, Jia, Yunyi

Evaluating the Robustness of Segmentation Models against Adversarial Patches in Off-Road Environments

2026-01-7520

9/22/2026

Semantic Segmentation (SS) is critical for autonomous vehicles to navigate off-road environments by identifying drivable terrain. Although models like ResNet34+UNet and EfficientViT have been proposed for these tasks, their susceptibility to localized adversarial patches in unstructured environments remains under-researched. This paper presents a comprehensive robustness evaluation of six real-time SS architectures, including the state-of-the-art YOLOv11 and YOLOv12 segmentation variants against five diverse adversarial patch schemes. Our experiments, conducted on a modified YCOR dataset, demonstrate that EfficientViT is the most resilient architecture, maintaining high accuracy with minimal performance degradation. In contrast, single-stage models like YOLOv11n-seg exhibit significant vulnerability, with pixel accuracy drops reaching 26.55%. We also show how decreases in overall segmentation accuracy impact the segmentation models’ ability to discern traversable terrain from non-traversable terrain.

Salas, Christopher, Pesé, Mert D., Li, Bing, Smereka, Jonathon, Cheng, Long

Terrain-Aware Probabilistic Prediction of Suspension Component Forces in Unmanned Ground Vehicles

2026-01-7509

9/22/2026

Unmanned ground vehicles (UGVs) operating in unstructured environments must account not only for terrain traversability but also for terrain-induced loads that affect component durability. Existing path planning approaches primarily consider obstacle avoidance and mobility, while neglecting cumulative structural degradation due to repeated loading. High-fidelity physics-based simulations can capture these effects but are computationally prohibitive for real-time applications. This study investigates machine learning-based surrogate models for predicting vehicle component reaction forces from terrain height sequences generated using a controlled parametric terrain formulation. Both feed-forward and recurrent neural network architectures are evaluated, and ensemble-based probabilistic techniques are incorporated to quantify predictive uncertainty. Results show that the ensemble long short-term memory (Ens-LSTM) model achieves the lowest prediction error (mean absolute error of 0.621 kN) while maintaining narrow 95% prediction intervals (3.22–4.28 kN). A simpler ensemble feed-forward network (Ens-NN) achieves comparable accuracy (0.626 kN) with reduced model complexity. These results demonstrate that data-driven surrogate models can provide accurate and uncertainty-aware force predictions, enabling the integration of structural reliability considerations into fatigue-aware path planning for UGVs.

Chua, Yang Kang, Mundiwala, Mohammad, Wang, Xudong, Castanier, Matthew, Hu, Zhen, Hu, Chao

Deep Learning-Based Rotor Temperature Estimation for Rare-Earth-Free Motors

2026-01-7506

9/22/2026

This paper presents a deep learning-based approach for online rotor temperature estimation in electrically excited synchronous motors (EESMs). Accurate rotor temperature estimation is critical for ensuring safe operation, improving performance, and enabling reliable thermal management of electric traction motors. Recurrent neural network (RNN) architectures, including gated recurrent unit (GRU) and long short-term memory (LSTM) networks, are investigated to develop a data-driven thermal virtual sensor capable of capturing the temporal dynamics of motor operation. Experimental data collected from a 190 kW EESM prototype are used to train and evaluate the proposed models. A systematic training, testing, and 10-fold cross-validation framework is employed to assess prediction accuracy and generalization capability. The results demonstrate that the GRU-based model achieves higher prediction accuracy than the LSTM model while maintaining comparable inference latency. The proposed approach provides an efficient and lightweight solution for real-time rotor temperature estimation suitable for embedded motor control applications.

Tatari, Farzaneh, Aligoudarzi, Mohsen Mirza

Integrating MBSE with High-Fidelity Crash Simulation

2026-01-7542

9/22/2026

Modern electrified ground vehicles introduce complex, multi-domain safety requirements, such as post-crash thermal runaway prevention, that expose the traceability limitations of Document-Based Systems Engineering (DBSE). This paper proposes a four-layer, bidirectional digital thread architecture that integrates Model-Based Systems Engineering (MBSE) with high-fidelity, non-linear Computer-Aided Engineering (CAE) crash simulations. Leveraging SysML, System-Theoretic Process Analysis (STPA), and Python-based orchestration middleware, the framework automates the translation of descriptive safety requirements into explicit finite element boundary conditions. The architecture programmatically extracts key performance indicators from massive binary solver outputs and injects them back into the SysML environment for automated compliance verification. Demonstrated through a simplified electric vehicle side-pole impact case study utilizing LS-DYNA and a 1D thermal model, the framework successfully eliminates manual data handoffs, accelerates multidisciplinary design optimization, and ensures robust, risk-driven requirement traceability across the engineering lifecycle.

Rye, Patrick J.

An Agentic AI Vision for Combat Vehicle Architecture Model

2026-01-7527

9/22/2026

This position paper presents

Dattathreya, Macam

Leveraging Standards-Based MOSA for SWaP-Constrained Deployable Systems with VNX+

2026-01-7536

9/22/2026

Littlefield, Mark

Balancing Durability and Deployability: Enhanced Situational Awareness (ESA) for Non-Combat Tactical Vehicles

2026-01-7540

9/22/2026

The persistent rate of accidents and fatalities involving legacy tactical military vehicles underscores a critical need for Enhanced Situational Awareness (ESA) technologies. However, the prohibitive cost and lengthy development cycles associated with full MIL-STD ruggedization often prevent these safety systems from reaching the in-service non-combat vehicles with limited driver visibility. This paper suggests a strategic shift in procurement policy: The adoption of relaxed ruggedization standards for vehicles operating in non-combat, administrative, and training roles. By deriving requirements from high-stress commercial sectors— such as heavy mining, steel production, and NASCAR racing—the military can utilize electronics designed for "extreme industrial" rather than "battlefield" environments. The principal objectives of this relaxation is cost reduction, lowering the barrier to entry and increasing the likelihood of ESA deployment across the legacy fleet. Furthermore, this approach aligns with Modular Open Systems Approach (MOSA) principles by enabling the integration of non-proprietary commercial devices. Utilizing these accessible technologies on legacy platforms creates a real-world testbed to evaluate technological advances rapidly. These insights can then inform and accelerate the development of future MIL-STD systems for combat vehicles, effectively shortening the traditional development life cycle while prioritizing the immediate enhanced protection of service member lives.

Pilgrim, Robert A., Brown, Roy C.

Pitfalls in High Stakes Environmental Chamber Testing for Transparent Armor

2026-01-7543

9/22/2026

Transparent armor, also known as ballistic glass, is a primary sustainment cost for tactical wheeled vehicle programs, accounting for 10’s of millions of dollars annually. The “Accelerated Life Test” is required in first article testing, and is an effective predictor of part life. However, the test takes 4-6 months and is expensive. Passing the test is a major hurdle, contributing to backlogs for critical Marine Corps and Army tactical vehicles. The test is also a very ‘high stakes’ test because it is only required once per design and is a primary factor in determining selection of the winning company bid. While the test requirements appear simple, multiple seemingly minor test errors have had dramatic effects on the test results. It is critical for both manufacturers and government program offices to understand how to properly run the test, analyze results, and report the results in a way that can be confidently accepted as a technically sound measure of part quality. This work highlights key pitfalls using a thermal model to show their potential effect. Recommendations are provided for correctly running and reporting the test.

Busch, Brendon S., Key, Christopher T., Magner, Matthew J., Brown, Kevin A., Merrill, Marriner H.

Leveraging Domestic Automotive Software Development Best Practices for MOSA and GCIA/Victory Implementation

2026-01-7538

9/22/2026

ISO 26262ISO/SAE 21434ISTQB/ASPICEMOSAGCIAVICTORY

Priemer, Douglas, Sime, Karl

Human Digital Twin Architecture for Ground Vehicle Scenario Integration

2026-01-7505

9/22/2026

Ground vehicle commanders operate in scenarios which bare high cognitive load. They must be reactive to time-critical events where attention is divided between a variety of sensors, crew members, the physical world, and digital displays, which can result in missed situational cues. This paper presents a Human Digital Twin (HDT) architecture which provides real-time, embodied AI assistance to commanders in a military ground vehicle simulation scenario. The system integrates a data pipeline for combining a MetaHuman avatar in Unreal Engine with multi-modal data ingestion and a large language model (LLM). In addition, a retrieval-augmented generation approach grounds the LLM with mission-specific context, and a Big Five personality framework for prompt design constructs a consistent agent persona throughout the scenario. The architecture is demonstrated with a prisoner of war camp scouting mission, in which the HDT selectively intervenes when needed to alert the commander to critical events when missed. A system latency evaluation is provided to demonstrate viability for real-time integration. Results show the potential of integrated HDT systems to improve situational awareness and decision support in high stakes ground vehicle operations.

McCarthy, Martin, Mohammed, Abdul Mannan, Gallagher, Reese, Neumann, Carsten, Bruder, Gerd, Reiners, Dirk, Cruz-Neira, Carolina, Paul, Victor

View Translation: Geometry-Guided Latent Diffusion for Cross-View Image Synthesis on Ground Vehicles

2026-01-7501

9/22/2026

Synthesizing novel camera views is important for autonomous ground vehicles, with applications in surround-view monitoring, occlusion recovery, and training data augmentation. We present View Translation, a geometry-guided latent diffusion framework that generates a target camera view from a source image, relative camera pose, and an available target-view depth prior. The method combines three components: a Vector Quantized Variational Autoencoder for compact latent encoding, a depth-based warping module that projects the source image into the target view to provide geometric guidance, and a ControlNet-augmented denoising UNet conditioned on source appearance, relative pose, and an auxiliary Image-Depth fusion network. Evaluated on KITTI and a simulated off-road dataset, our method achieves competitive FID while improving LPIPS and PSNR over baseline approaches, supporting cross-view synthesis for ground vehicle perception.

Mayekar, Omkar, Aiyetigbo, Mary, Salvi, Ameya, Samak, Tanmay, Samak, Chinmay, Desjardins, Brendan, Smereka, Jonathon, Brudnak, Mark, Krovi, Venkat, Luo, Feng, Li, Nianyi

The Impact of Observability Constraints on the Planning for Employment of Robotic and Autonomous Systems

2026-01-7514

9/22/2026

Employment of Robotic and Autonomous Systems requires a different paradigm of mission planning. The GTRI Missioneer for Organic Collaborative Kill-Chains (MOCKS) effort was developed to mature the concepts of mission planning in light of autonomy, where the decision making is distributed across the battlespace and each individual entity has limited awareness of global state. The paper presents some initial characterizations of the impact of constraints on periods out of communication, or no-comms windows, on the number of resources required for missions of a certain operational area. This metric, along with the foundational metric of remaining effective range margin are foundational to robust a priori planning.

Spratley, Michael

Perspectives on Augmented Cognition Implementation in Military Ground Vehicle Crew Stations

2026-01-7515

9/22/2026

Military ground vehicles are transitioning from manually operated platforms to highly automated systems, which fundamentally alters the cognitive demands placed on crew members. Rather than reducing workload, automation frequently redistributes it, introducing challenges associated with supervisory monitoring, autonomy mode calibration, and situational awareness. Augmented cognition (AugCog) offers a closed-loop framework in which real-time physiological and behavioral measures of operator cognitive state can be used to dynamically adapt system interfaces and autonomy levels. This paper reviews six nonintrusive sensor modalities suitable for military ground vehicle crew stations: remote photoplethysmography (rPPG), facial thermography, eye tracking and pupillometry, dry-electrode electroencephalography (EEG), Facial Action Coding System (FACS) action unit (AU) analysis, and elastic electrodermal activity (EDA) sensors. A conceptual multimodal crew station integration framework is proposed, and future research directions are outlined.

Mikulski, Christopher, Riegner, Kayla

Scene-Level Multi-View Instance Consistency Using Multi-Feature Vision Fusion for Synthetic Ground Vehicle Environments

2026-01-7502

9/22/2026

Maintaining consistent object identities across multiple camera viewpoints is a critical challenge in synthetic perception environments used for autonomous ground vehicle evaluation. This paper presents a scene-level multi-view instance consistency framework that integrates OpenUSD scene composition, Omniverse Replicator synthetic-data generation, and a multi-feature vision fusion pipeline. The proposed approach combines semantic embeddings from CLIP, patch-level descriptors from DINOv2, geometric correspondences from LoFTR, mask-derived shape invariants using Hu moments, and relative-position priors to associate object instances across views, including visually identical objects. A compact composite scoring function fuses these complementary cues to achieve robust cross-view identity assignment while preserving OpenUSD asset modularity through grouped-prim support. Synthetic experiments across 120 multi-camera scenes demonstrate improved Top-1 Match Accuracy and Identity Consistency Rate, with reduced ID-switch occurrences compared to single-cue baselines. The framework supports scalable, repeatable, and traceable digital engineering workflows for defense-oriented perception evaluation.

Bhattacharya, Sambit, Nakamoto, Kyle

SOSA Enabled Automotive Technologies Disrupt the Battlefield

2026-01-7535

9/22/2026

Pilaud, William J.

Physics-Constrained Procedural Generation of Synthetic Environments for the Robust Validation of Autonomous Maneuver and Logistics

2026-01-7549

9/22/2026

The validation of Autonomous Ground Vehicles (AGVs) and intelligent logistics planners is frequently compromised by the ”Sim-to-Real” gap, where simulation environments fail to replicate the physical friction of operational deployment. Ideally, valid test cases must enforce strict mobility constraints and impose realistic sustainment penalties; however, many current generation tools rely on idealized terrain interactions and infinite-resource assumptions. We present a real-time procedural framework designed to generate high-friction validation environments that stress-test the robustness of the System Under Test (SUT). The architecture integrates gradient-based terrain analysis with a stochastic contested logistics model. It ingests synthetic heightmaps to precompute mobility corridors, ensuring that every generated evaluation episode adheres to vehicle-specific traversability limits. Simultaneously, a logistics kernel enforces fuel consumption scaled by terrain gradients and models supply chain interdiction as a parameterized Bernoulli process. We validate this framework through a ”Digital Twin” methodology, demonstrating that terrain-aware generation eliminates invalid initialization states (0% mobility violations) while the logistics model induces operationally relevant failure modes in the SUT. This unclassified, open-architecture approach supports DoD Verification, Validation, and Accreditation (VV&A) requirements by providing deterministic, reproducible edge cases for autonomous system evaluation.

Soykan, Bulent, Rabadi, Ghaith, Bochenek, Grace, Paul, Victor J.

Multimodal Digital-Twin Simulations for Off-Road Autonomous Vehicles

2026-01-7550

9/22/2026

Research in autonomous driving has largely focused on structured, on-road environments in densely populated urban areas, leaving off-road autonomy comparatively underexplored despite its importance for applications such as search and rescue, agriculture, and defense. Progress in developing solutions for off-road autonomy is heavily constrained by the high cost, safety risks, and limited coverage of real-world off-road data collection, particularly for rare terrain-induced failure cases. To address these challenges, we introduce a large-scale simulation platform for off-road autonomous driving generated in the Unity3D engine. Our simulator provides indefinite data from multiple RGB cameras, LiDAR, GPS, and IMU sensors to support research in long-duration applications. Our platform also facilitates research in risk-aware planning through dynamic weather, lighting, and annotations for sequence-level failures like collisions and weather-induced sensor blockages. By combining long-duration temporal coverage, multimodal sensing, and procedurally generated terrain diversity, our simulator facilitates progress for systematic evaluation of learning-based autonomous driving models in unstructured, safety-critical environments.

Ross, Timothy, Boone, Julia, Afghah, Fatemeh

PR-2930™-LVLC and Coraseal™ Ambient U1800: High Performance Adhesives for Military Application

2026-01-7555

9/22/2026

This paper investigates two high-performance structural adhesives, PR-2930™-LVLC and CORASEAL™ Ambient U1800, classified as Group I under the MIL-PRF-32662 specification, emphasizing their potential role in structural and armor applications. The study highlights the adhesives’ unique properties, including PR-2930-LVLC’s superior adhesion and CORASEAL Ambient U1800’s remarkable combination of strength and flexibility. Rigorous testing, including hot-wet conditioning and elevated temperature assessments, demonstrates their durability under extreme conditions. The paper also details the adhesives’ performance in Mode I and II strength and toughness evaluations, revealing PR-2930-LVLC’s high strength and CORASEAL Ambient U1800’s superior toughness. These findings underscore the potential of these adhesives to enhance performance during high impact-rate events and to support lightweight vehicle design, addressing a significant gap in adhesive technology for military applications.

Hellerman, Edward, Toolis, Amy, Pollum, Marvin

Crew Optimization and Augmentation Technologies (COAT) Soldier Touch Point 5 (STP5) Field Experiment

2026-01-7545

9/22/2026

Ground Vehicle Systems Center (GVSC) conducted a Soldier Touch Point (STP) in the field comparing the use of Vitreous User Interface (UI) on a Helmet Mounted Displays (HMD) to a Soldier Machine Interface (SMI) on Vehicle Mounted Displays (VMD). Soldiers drove a Stryker Vehicle equipped with 360-degree indirect vision through a series of mobility obstacles at Camp Grayling. No significant differences were found in Soldier performance between the UI conditions, however, a significant performed better maneuvering through obstacles on the right-hand side of the vehicle in comparison to the left. This is a continuation of simulation only work previously presented at GVSETs 2023.

Anderson, Rachel, Hoelscher, Andrew, Schultz, Jeffrey, Paul, Victor, Wood, Ryan, Reid, Alexander, Ratka, Steven, Roose, Kaitlyn, Grant, Lauren, Shrestha, Sumit

Initial Thermal Evaluation of 422 Martensitic Stainless Steel Piston in a High-Output Diesel Engine

2026-01-7564

9/22/2026

A piston manufactured with a crown comprised of grade 422 martensitic stainless steel and skirt manufactured from 4140 steel was instrumented with fifteen thermocouples and a wireless telemetry system. Piston temperature data were collected at five engine operating conditions and compared to two additional instrumented pistons with crown and skirt both made of 4140 martensitic steel, which is traditionally used for heavy-duty diesel applications. Thermal finite element modeling was used to predict the increase in operating temperature of the 422 piston relative to the 4140 piston and help understand instrumentation uncertainty. Previous research of candidate high-temperature alloys indicated that 12Cr martensitic steel alloys, such as alloy 422, offer several potential benefits when used in a diesel piston application, including increased high-temperature oxidation resistance and strength. The potential benefits of alloy 422 may however be partially negated by the expected increased piston operating temperature due to the alloy’s lower thermal conductivity. In this work 422 alloy resulted in no statistically significant change in piston temperatures relative to the baseline 4140 steel during engine testing. The 422 alloy is poised to offer a dual durability advantage because the initial results show it can achieve superior oxidation resistance without operating at the higher temperatures that would accelerate such degradation. Maximum piston temperature capability is expected to be a critical design limit in next generation diesel engines with greater power density, lower heat rejection, and improved fuel economy. Citation: E. Gingrich, et. al., “Initial Thermal Evaluation of 422 Martensitic Stainless Steel Piston in a High-output Diesel Engine,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13, 2026.

Gingrich, Eric, Tess, Michael, Grunin, Arkady, Korivi, Vamshi, Sebeck, Katherine, Pierce, Dean, Wang, Yiyu, Muralidharan, Govindarajan, Pillai, Rishi, Haynes, James A., Will, Kurt

Mobilizing Innovation: Venture Capital Alignment for Defense with an Autonomous Construction Case Study

2026-01-7560

9/22/2026

Defense acquisition often struggles to match the pace of private investment, slowing the transition of mature commercial technologies into military use. This paper examines how aligning government acquisition with venture-oriented business models can increase industry participation, accelerate fielding, and reduce government program office risk. Using autonomous construction as a case study, it highlights how commercial investment has advanced autonomy while traditional procurement limits adoption. The paper outlines approaches such as non-traditional partnerships, phased acquisition, and performance-linked revenue structures to improve flexibility, leverage private capital, and expand the Defense Industrial Base while speeding operational capability delivery. Citation: Mazzara, M., San Nicolas, A., Gadea, J., Himmel, M., Kruger, J., Gill, C., & Simon, A., Soylemezoglu, A., Netchaev, A., Nottage, D., Klein, J. “Mobilizing Innovation: Venture Capital Alignment for Defense with Autonomous Construction Case Study” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA Michigan Chapter, Novi, MI, August 11–13, 2026.

Mazzara, Mark, Nicolas, Austen San, Gadea, James, Himmel, Max, Kruger, John, Gill, Charles “Spuck”, Simon, Andrea, Soylemezoglu, Ahmet, Netchaev, Anton, Nottage, Dustin, Klein, Jordan

Analyzing Open-Source Data to Inform Military Vehicle Design

2026-01-7559

9/22/2026

The modern battlefield is increasingly transparent, generating large volumes of open-source data on the use, damage, and loss of military vehicles. This paper presents a structured methodology to exploit such data for deriving operational requirements for future vehicles. It uses a mixed-method framework combining qualitative reporting with quantitatively verified loss data. Daily battlefield reports are analyzed with large language models to extract operational context, employment patterns, and tactical conditions. These insights are cross-referenced with loss data to assess how operational factors affect vehicle survivability, with the findings being used to prioritize requirements that improve vehicle performance. The approach is demonstrated through a case study of Leopard tanks in the Russia-Ukraine war, using Institute for the Study of War reports and Oryxspioenkop loss data. Results show how open-source intelligence can systematically inform survivability, mobility, and combat effectiveness in modern vehicle design.

Lynch, Benjamin, Mittal, Vikram

Adopting Cots Technology for UGV Wheel Drive System

2026-01-7561

9/22/2026

As the defense industry prioritizes speed of play to allow our warfighters to maintain a decisive edge over our adversaries, creativity is needed to leverage COTS effectively. This paper presents a case study of a fast-paced workflow leveraging modeling and simulation, targeted risk testing, thermal characterization, and accelerated life testing. Citation: K. May, J. Costa, J. Boyd, “Adopting COTS Technology for UGV Wheel Drive System,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13, 2026.

May, Ken, Costa, Joao, Boyd, Jake

Modeling the Operational Performance Impacts of a Vehicle Integrated Power Kit (VIPK) on a Modern Medium Tactical Wheeled Vehicle

2026-01-7567

9/22/2026

Michigan Technological University (MTU) was awarded a competitive prototype project to develop a Vehicle Integrated Power Kit (VIPK) for multiple variants of the Family of Medium Tactical Vehicles A2 (FMTV A2). The VIPK provides high voltage power export, expeditionary power for silent watch capability in low load cases, and interoperability with tactical microgrids. To support VIPK development, MTU created a vehicle model to quantify the impacts of VIPK integration, accelerate design decisions, and predict vehicle performance. This model was calibrated using experimental data, and the calibrated model was then used to compare vehicle performance with and without VIPK installed. Power flow diagrams were utilized to understand the energy pathways during vehicle operations. This paper details how the VIPK system affects performance and analyzes its power flow under select operational conditions. Citation: B. Goodenough, H. Schmidt, J. Naber, P. Dice, D. Subert, K. Meyers, “Modeling the Operational Performance Impacts of a Vehicle Integrated Power Kit (VIPK) on a Modern Medium Tactical Wheeled Vehicle,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13, 2026.

Goodenough, Bryant, Schmidt, Henry, Naber, Jeffrey D., Dice, Paul, Subert, Dave, Meyers, Kevin

Methods for Designing Rotating Components for Compliance to ISO 1940 Balance Requirements using Generic 3D CAD Software

2026-01-7565

9/22/2026

The benefits of specifying balance requirements in terms of an ISO 1940 balance quality grade instead of traditional mass-distance based requirements are discussed along with methods to convert ISO 1940 balance quality grades into permissible imbalance limits at the bearing supports. Methods are developed to determine the expected imbalance values at bearing supports using mass property data from generic 3D CAD software packages without the need for Finite Element Analysis. Practical exercises are presented using these methods to assess a part’s compliance to ISO 1940 while still in the conceptual 3D CAD design stage. These practical exercises cover the selection of appropriate geometric tolerances to ensure part balance without the need for post fabrication balancing as well as the design of nonsymmetrical components for ISO 1940 balance compliance. Citation: J. Srodawa, “Methods for Designing Rotating Components for Compliance to ISO 1940 Balance Requirements Using Generic 3D CAD Software,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13, 2026.

Srodawa, John

AI-Driven Predictive Sonification for Human-on-the-Loop Ground Vehicle Autonomy

2026-01-7503

9/22/2026

Ground vehicle autonomy increasingly depends on human-on-the-loop (HOTL) supervision, yet supervisors are often overloaded by visual interfaces that can obscure emerging risks. This paper presents an AI-driven predictive sonification architecture that converts short-horizon forecasts of platoon behavior into structured auditory cues for supervisory monitoring. A forecasting engine predicts future vehicle interaction states and evaluates predicted and active violations to generate a composite risk indicator. When risk exceeds defined thresholds, a sonification module conveys risk magnitude and trajectory through changes in pitch, loudness, modulation, and spatial panning. The paper describes the system architecture, sonification design, operational use cases, and a planned human-subject evaluation. The proposed framework is intended to improve early awareness of emerging instability and support more timely supervisory intervention.

Plotzke, Zachary R., Mohammadi, Alireza, Cheung, Calvin M.

Shrike Nano: On-Body AI-Enabled Digital Fires from Unmanned Aircraft Feeds

2026-01-7504

9/22/2026

Shrike Nano provides forward observers and small unmanned aerial system (sUAS) operators with an integrated solution to enhance target prosecution using sUAS video feeds and indirect fire systems. Operable within the Android Tactical Assault Kit (ATAK) ecosystem, Shrike Nano functions as a software plugin that interacts seamlessly with existing tools, including UAS Tool, Robot Picker, and Network Monitor. By utilizing either aided threat recognition (AiTR) or manual targeting workflows, along with passive single-camera geolocation, operators can nominate targets and correct shot placement via digital messaging to enterprise fires terminals such as the Advanced Field Artillery Tactical Data System (AFATDS). The system offers key advantages, including operator standoff capabilities, accurate geolocation, and streamlined fires messaging workflows, all while leveraging low-observable platforms. Shrike Nano seeks to bridge gaps in traditional targeting processes by providing a cohesive and efficient sensor-to-shooter workflow that reduces cognitive load and enables faster, more reliable fire missions at the tactical edge.

Baharanyi, Ali I., Tozzi, Gregory M.

Beyond Detection: Zero-Shot UAS Payload Identification, Passive Ranging, and Pose Estimation

2026-01-7507

9/22/2026

Unmanned Aerial Systems (UAS) pose a growing threat on the modern battlefield, demanding rapid detection and characterization capabilities for the warfighter. Existing single-model solutions are inadequate for Counter-UAS (C-UAS), as they struggle across varying ranges and cannot provide detailed contextual information beyond bounding boxes. We present ZEUS (Zero-shot Explainable Universal Segmentation), a multi-model detection and recognition system that integrates several machine learning approaches. ZEUS employs a high-performance UAS detector trained on synthetic, internally collected, and open-source datasets, with real-time capability demonstrated on edge hardware across both electro-optical and infrared modalities. For classification, ZEUS uses a zero-shot approach: detected UAS are segmented and compared against a library of 3D reference models rendered at various poses, enabling identification of new UAS types without retraining. This methodology additionally provides UAS pose and range estimates critical for threat assessment and engagement decisions.

Matousek, Gregory, Varberg, Nathan, Torrione, Pete, Brandon, Namdi, Inkawhich, Matt, Camilo, Joe

Development of a UAV Platform for Remote Inspection and Visualization

2026-01-7511

9/22/2026

This paper details the development of an intelligence and inspection platform consisting of an attritable sub-250g UAV, a ground control station, and a visualization interface for users. The UAV architecture combines onboard obstacle detection and avoidance along with simultaneous localization and mapping to have full autonomous navigation inside of complicated GPS-denied environments. The ROS 2-to-Unreal Engine data pipeline allows for sensor fusion, data cleansing, and initial analysis as well as creation of a high-fidelity real-time 3D digital twin. The visualization interface allows users to easily identify critical features and turn data into intelligence to support decision making by soldiers and first responders.

Lee, Yeen K., Bainard, Sean, Shaughnessy, Michael, Bolger, Matt, Koepp, R. Tucker, Salehzadeh, Roya, Mallory, Stephen, Mynderse, James A., Guillen, Pedro, Hernandez, Margarita

Clemson Off-Road Dataset (CORD): A Multimodal Benchmark for Autonomy beyond the Flat-World

2026-01-7518

9/22/2026

While autonomous perception has matured within the structured confines of urban roadways, it remains brittle when confronting the chaotic, non-rigid terrain of the natural world. This paper introduces the Clemson Off-Road Dataset, a high-fidelity, multimodal dataset engineered to bridge this gap by challenging standard “flat-world” assumptions. Featuring 2.90 TB of sensor data, the dataset captures a diverse spectrum of unstructured environments, ranging from the transitional trails of CU-ICAR and the day/night lighting dynamics of TN3 to the unstructured wilderness of Camp Daniels and the novel coastal scenery of Edisto Island. Distinguishing itself from existing forest-centric benchmarks, the Clemson Dataset provides a first-of-its-kind focus on coastal data, featuring unique adversarial conditions such as extreme solar glare, loose sand, and shifting tide lines. The data is collected aboard a Polaris RZR Pro R 4, a high-performance platform integrated with a sensor suite designed to perceive physics beyond geometry. Alongside 360° HD camera coverage, 3D LiDAR, and Radar, we integrate Cubert Ultris Hyperspectral imaging and Prophesee EVK4 Event-based vision to enable material classification and high-dynamic-range motion tracking. To overcome the bottleneck in ground truth generation, we used our “AI LabelMate,” a context-aware semi-automated annotation agent that fuses Vision-Language Models (Florence-2) with SAM2 to generate 6331 pixel-perfect annotated frames using a specialized off-road ontology and a human-in-the-loop pipeline. We establish performance baselines using Oneformer for semantic segmentation and used SalsaNext for lidar point clouds labelling. Available in both raw ROS2 bag and extracted standard formats, this Dataset serves as a pivotal testing ground for the next generation of robust autonomous systems.The dataset of this paper is available upon request to the Virtual Prototyping of Autonomy-Enabled Ground Systems (VIPR-GS) Center.

Patil, Ashish, Gupta, Prakhar, Bhosale, Mayuresh, Mukwaya, Arthur, Jegede, Akinbobola, Mikulski, Dariusz, Mwakalonge, Judith, Jia, Yunyi

Hybridnav: A Hybrid Approach to Zero-Shot Object Detection with Adaptive Exploration for Autonomous Reconnaissance

2026-01-7517

9/22/2026

Autonomous reconnaissance in unknown or contested environments demands robust perception systems capable of identifying diverse objects without prior training data. This paper presents HybridNAV, a hybrid framework that combines multiple foundation models with an adaptive navigation system for zero-shot object detection and autonomous exploration. Unlike monolithic detection models, HybridNAV’s multi-model fusion achieves balanced precision (0.60) and recall (0.58) with a macro F-score of 0.59, representing a 24% improvement over single-model baselines. The adaptive navigation system reduces scan time by 25% and path length by 30% compared to static waypoint approaches, while operating in real-time at 3.2 Hz with sub-100 msec latency on resource-constrained hardware. All processing is performed locally on the robotic platform, eliminating reliance on external communication infrastructure—a critical requirement for operations in communication-denied environments. We evaluate HybridNAV in both simulated indoor scenes and physical robot trials, demonstrating its effectiveness for intelligence gathering in unknown environments.

Indurthi, Hemanth, Martinson, Eric

Bullfrog: AI Powered Counter Unmanned Aerial System

2026-01-7519

9/22/2026

The proliferation of small unmanned aircraft systems (sUAS) presents an asymmetric threat to ground maneuver forces operating in contested and gray-zone environments. The Bullfrog Autonomous Weapon Station (AWS) addresses this operational gap through a passive, AI-powered counter-UAS system employing computer vision and machine learning for autonomous detection, tracking, classification, and engagement. Field testing at Technology Readiness Experimentation (T-REX) 26-1 demonstrated 100% probability of defeat against Group 1 UAS targets with a mean engagement time of 6 seconds and 10 rounds per kill at ranges exceeding 160 meters. Operating in both autonomous and human-in-the-loop modes, Bullfrog achieved 99.45% operational availability while leveraging service-common M240B weapons and Modular Open Systems Architecture for rapid integration with Joint All-Domain Command and Control (JADC2) networks. At $300,000 per unit with $10 cost-per-engagement, Bullfrog demonstrates operational relevance, speed-to-field, and alignment with Army and Marine Corps autonomy priorities.

Cunningham, Jason, Clark, Alex

Motion Approach for Turret System Physical Simulation

2026-01-7553

9/22/2026

Physical simulation permits government and contractor engineers to characterize, validate and test a complex weapon system’s many components and subsystems. This is particularly important when new sub-systems and components are in the prototype development stage and integrated into a full weapon system for the first time. This paper presents a comprehensive methodology in creating a motion environment to adequately test a functional turret system in a lab environment using the GVSC’s Crew Station Turret Motion Base Simulator. The simulator is a high-capacity, 6-degrees-of-freedom test device that utilizes computer-controlled hydraulic actuators and a platform to reproduce dynamic conditions encountered by a combat vehicle turret system traversing off-road terrain. The paper presents an iterative technique using the simulator’s measured frequency response functions to achieve a targeted response. Platform error and repeatability are presented which is key for “baseline vs. modified” studies.

Paul, Victor, Hoelscher, Andrew, Tiguert, Ahmed, Zywiol, Harry

Intelligent Optimization Simulation Framework for Test and Evaluation of Autonomous Systems

2026-01-7551

9/22/2026

This paper describes ongoing research and development of an efficient optimization/search–based modeling and simulation framework for rapidly identifying low-performance scenarios in advanced autonomous systems. Ensuring predictable, safe behavior across complex, integrated systems remains a core operational test-and-evaluation challenge. Our goal is to balance rigorous validation with timely deployment. We are developing TEAAS (Test & Evaluation of Advanced Autonomous Systems), a scalable, faster-than-real-time framework designed to uncover critical failure scenarios efficiently. Key features include GPU-accelerated parallel simulation and learning, computational intelligence–based search of optimal parameters, uncertainty quantification for reproducibility, and real-time physics-accurate sensor models. We conducted simulation experiments to evaluate and demonstrate the framework performance for two black-box ground-vehicle autonomous systems. Key results were that adequate uncertainty quantification can be achieved with as few as 10 repeated runs per simulation scenario, sensor realism has a significant effect on failure rate, distinct differences between the two autonomies failure modes were identified, and our efficient optimization/search methods identify critical performance regions in a small fraction of the number of simulations required by a naïve Monte Carlo search.

Snarski, S., Menozzi, A., Persons, B., Lazar, D., Khan, N.

Electrical and Power Quality Performance Evaluation of a SiC Based 500kW High Temperature and High Power-Density Inverter

2026-01-7568

9/22/2026

In pursuit of future high-power capabilities for U.S. military ground vehicles, the transition towards vehicle electrification has been heavily adopted. High power-density and high temperature inverters play a key role in progressing vehicle electrification adoption across the U.S. military. This paper presents experimental results to evaluate the power quality performance of the developed high power-density and high temperature inverter, Enercycle™ DC-1000 Inverter based on silicon carbide (SiC). The DC-1000 inverter is a bi-directional inverter with a power density of 11.4 kW/L, which is capable of operating at 600 Vdc and delivering 500kW continuous output power and transient output power up to 640 kW, enable ground vehicle electrification. The experimental results to evaluate the power quality aspects such as distortion factor, ac voltage ripple, and voltage transient due to step load are presented in this paper. Moreover, challenges and next steps for further improvement of design have been discussed. Citation: A. Sadigh, Iris Shiroma “Electrical and Power Quality Performance Evaluation of a SiC Based 500kW High Temperature and High Power-Density Inverter,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13, 2026.

Sadigh, Arash, Shiroma, Iris

Experimental Comparison Between Real and Simulated Battery Pack Efficiency in Light Quadricycles Using a Custom Inertia Dynamometer

2026-24-0008

9/21/2026

This study compares the energy efficiency of a real battery pack and a simulated battery pack using a hardware-in-the-loop battery emulator, through experimental testing on a dedicated inertia dynamometer for light quadricycles. The investigated LiFePo4 battery pack has a nominal voltage of 48 V and a nominal capacity of 100 Ah. Initial characterization identified a reduced State of Health based on capacity (SOHC), with a measured usable capacity of 48 Ah (from the nominal 100 Ah) and a corresponding reduction in charge acceptance capability. The emulator was configured to replicate the degraded battery characteristics, including the open-circuit voltage (OCV)-SOC relationship, internal resistance, and current limitations, enabling a direct comparison between simulated and experimental dynamic behavior. The experimental setup was designed to overcome the limitations of conventional chassis dynamometers for low-mass, independent four-wheel-drive quadricycles operating without mechanical friction braking systems. Multiple driving cycles were reproduced using a PLC-based closed-loop control system, with data acquisition performed via CAN communication. The comparative analysis highlights significant differences during regenerative braking events. While the emulator accurately reproduces baseline electrical behavior under mild operating conditions, the aged battery exhibits strong limitations during both high-power acceleration and severe deceleration phases. In particular, increased internal resistance and transient electrochemical polarization lead to premature saturation of charge acceptance, resulting in rejection of high-frequency current transients. Consequently, the experimentally observed energy recovery is significantly lower than the theoretical values predicted by the emulator. In addition, due to the absence of mechanical braking, the reduced regenerative capability directly leads to speed tracking deviations, as the required braking torque cannot be fully achieved. These results identify battery degradation as a key physical constraint affecting both energy efficiency and dynamic braking performance, highlighting the importance of improved electro-thermal and aging-aware model calibration for realistic system-level simulations.

Sementa, Paolo, Vaglieco, Bianca Maria, Altieri, Nunzio

Thermal Management of Pre-Chamber Spark Plugs for the Mitigation of Abnormal Ignition in Hydrogen-Fueled Engines – An Experimental and Numerical Study

2026-24-0007

9/21/2026

The internal combustion engine will continue to contribute to global mobility, particularly when operated with carbon dioxide low-carbon fuels. Pre-chamber ignition systems are increasingly investigated to improve efficiency, emissions, and combustion stability. In combination with hydrogen as a carbon-free fuel, they extend the lean operating limit while ensuring reliable ignition under demanding conditions. A key challenge is the thermal management of pre-chamber spark plugs. While the thermal behaviour of conventional spark plugs is well understood, limited knowledge exists for pre-chamber systems. Chamber geometry, material selection, manufacturing, and installation strongly influence thermal loading, where elevated local temperatures may contribute to knock, pre-ignition, and material degradation. The objective of this study is to establish a system-level understanding of pre-chamber thermal behaviour. Experiments are conducted on a single-cylinder research engine using hydrogen and research octane number 95 (RON 95) as a reference fuel. Dedicated temperature measurements identify thermal hotspots and assess parameter sensitivities. For the investigated configuration (14:1 compression ratio (CR), 1500 revolutions per minute (rpm), 12 bar indicated mean effective pressure (IMEP)), measurements and conjugate heat transfer (CHT) simulations suggest wall temperatures are not the primary contributor to pre-ignition. Reduced pre-ignition is observed with increasing scavenging bore diameter, indicating a strong influence of mixture preparation and residual gas effects. A coupled CHT model is integrated into a computational fluid dynamics (CFD) simulation with moving boundaries. The model includes realistic wall thicknesses, temperature-dependent material properties, and calibrated boundary conditions, enabling cycle-resolved analysis of heat fluxes and temperature fields for pre-chamber optimization.

Nenzel, Markus, Alkezbari, Ahmad Anas, Rottenkolber, Gregor

Characterisation of Brake Wear PM2.5 and PN10 Emissions Across Pad/Disc Technologies, Light-Duty Vehicle Types, and Braking Behaviours Under Real World Conditions

2026-24-0022

9/21/2026

Targeted brake emissions investigations undertaken within the Department for Transport’s Non-Exhaust Emissions programme are described in this paper. The non-exhaust emissions study aimed to improve understanding of particulate mass and particle number emissions from friction braking, and to quantify the influence of component selection, vehicle technology, operating conditions, and emissions control measures. A brake enclosure and sampling methodology, developed in an earlier project phase, was refined to improve airflow control, reduce leakage, and minimise artefacts. The updated system incorporated MPEC (hot and cold), APC10, DMS500, and eFilter instruments, enabling simultaneous measurement of volatile and non-volatile PN10, plus PM2.5. Nine brake pad formulations and two disc types were evaluated using a common C-segment platform during chassis dynamometer and on-road drive cycles, and under specific controlled braking events. Speed, deceleration and temperature effects on PM2.5 and PN10 emissions were investigated. The common platform testing included ICE, PHEV, and EV variants to capture test mass and regenerative braking influences, together with assessments of aged components. Results showed clear, repeatable differences between pad formulations, with low dust/ceramic pads yielding the lowest PM2.5 and PN10 emissions. Disc type effects were minimal, while component ageing/conditioning reduced emissions and improved repeatability. Brake temperature and energy input dominated emissions behaviour: dynamic braking produced the highest emissions, these increasing with road speed and disc temperature. Regenerative braking reduced EV and PHEV PM2.5 versus ICE, but PN10 remained comparable due to the dominance of non-volatile PN emissions during friction braking events. Increased vehicle mass led to proportionally higher emissions.

Andersson, Jon

EKF-Based Machine Learning Approach for Battery SOH Prediction in Commercial Vehicles Fleet

2026-24-0002

9/21/2026

Estimating battery state of health (SOH) from field data is essential to ensure successful operation and increase the uptime of battery electric vehicles (BEVs). Most studies in the literature propose methods relying on datasets acquired under controlled laboratory conditions. However, SOH estimation becomes significantly more challenging when dealing with real-world data due to the increased variability and complexity of operating conditions. In this work, CAN telematics data, sampled at 1 Hz, were collected over approximately 20 months of operation from 10 electric commercial vehicles. During this period, a maximum battery degradation of 4% is observed within the fleet. Firstly, a model-based framework was introduced, in which a second-order battery equivalent circuit model (ECM) was coupled with an extended Kalman filter (EKF) to estimate the battery SOH. Results confirmed that the EKF is able to accurately capture the battery's physical behavior and degradation trend, yielding a maximum root mean square error (RMSE) of 1.23% when compared with the SOH signal provided by the onboard BMS. However, a Kalman filter requires accurate model parameter identification and high-frequency measurement data, leading to increased computational costs. To bridge these gaps, this paper utilizes the SOH estimates obtained from the EKF to train and validate a feedforward neural network (FNN) model, specifically designed to operate on aggregated metrics. The FNN model can provide accurate SOH estimates, with a RMSE as low as 0.26% during the testing phase. The approach proposed in this work combines the interpretability of model-based methods with the scalability and reduced data dimensionality of machine learning (ML) ones, making it more suitable for monitoring battery SOH in large fleets of BEVs.

D'Agostino, Valerio, Pulvirenti, Luca, Shanker, Anirudh, Cardone, Massimo, Rizzoni, Giorgio, Vitale, Francesco

Multi-Level CFD and System Simulation Approach for Optimization of Cooling Circuits in High Power Density Powertrains

2026-24-0009

9/21/2026

This paper presents a CFD-based optimization workflow for the simulation and development of automotive cooling circuits, integrating three-dimensional steady-state analyses with one-dimensional transient modeling. The objective of the activity is to establish a robust methodology that links detailed component-level thermal characterization to system-level dynamic simulations, enabling the assessment of cooling performance under both driving and charging operating conditions. The thermal behavior of the cooling circuit components was first investigated using three-dimensional steady-state simulations performed with Ansys Fluent. For each relevant operating point, the fluid flow and heat transfer were resolved in full 3D, and temperatures were monitored at multiple locations within the components and along the circuit. The steady-state analyses provided spatially resolved temperature fields and heat transfer characteristics for a range of boundary conditions representative of real operating scenarios. From these results, temperature and performance maps were generated, describing the relationship between operating conditions, heat loads, and thermal responses of the components. These maps were then used for the calibration of one-dimensional models implemented in GT-Suite. The calibrated 1D models reproduce the thermal behavior observed in the 3D CFD simulations while allowing efficient simulation of the entire cooling system under transient conditions. This multi-level approach enables the combination of detailed local physics from CFD with the computational efficiency required for system-level dynamic analyses. Transient simulations were carried out in GT-Suite to evaluate the thermal response of the cooling circuit during both driving and charging phases. The driving phase accounts for variable thermal loads and flow conditions associated with vehicle operation, while the charging phase represents operating conditions specific to battery recharging scenarios. The calibrated 1D models were used to simulate the evolution of temperatures throughout the system over time, considering the interactions between components and the overall thermal inertia of the circuit. The results show that the designed cooling system is capable of maintaining component temperatures within the targeted limits across the analyzed operating conditions. The thermal containment is achieved for all components included in the cooling circuit under both dynamic driving and charging scenarios. The electric motor is oil-cooled and therefore is not part of the water-based cooling circuit addressed in this study. The proposed CFD-to-1D workflow provides a consistent and transferable methodology for the thermal development of cooling systems of high power density electrified powertrains. The novel contribution lies in (i) the application of the multi-level framework to a heavy-duty platform with SiC-based power modules and dedicated on-board charger developed within the Horizon Europe POWERDRIVE project, (ii) a DOE-based map generation strategy that preserves the conjugate heat transfer interactions between actively cooled components (power modules, OBC) and passively cooled neighbors (busbar, capacitors) within the reduced-order representation, and (iii) the integration of the reduced-order maps within a single transient system-level model covering both vehicle-at-rest charging and dynamic driving operating modes. This activity is carried out within the framework of the Horizon Europe project POWERDRIVE.

Chiappini, Daniele, Tribioli, Laura, Rodionov, Artem

Computational Electro-Thermal and Degradation Modeling Framework for Lithium-Ion Batteries in Electric Vehicles

2026-24-0011

9/21/2026

This paper presents an integrated computational framework that couples electro-thermal and degradation dynamics for lithium-ion batteries used in electric vehicles (EVs). The model is implemented using Python. At the cell level, the model describes charge and discharge behavior, state of charge (SOC), terminal voltage, internal resistance losses, and heat generation. An energy balance equation is used to estimate temperature variation during operation. Temperature-dependent resistance and capacity are included to represent nonlinear battery behavior under different load conditions. At the system level, feedback relationships between SOC, temperature, state of health (SOH), and degradation rate are modeled using system dynamics. Battery aging is represented through mathematical functions that relate capacity loss to temperature and usage cycles. This allows simulation of long-term performance under different driving scenarios. The model enables parametric and sensitivity analyses to evaluate the effects of discharge rate, ambient temperature, and degradation parameters. Results show the strong interaction between thermal behavior and battery aging. Model validation against experimental data is not performed in this study and remains an important direction for future research. The current work focuses on the derivation and parametric analysis of the modelling framework. The proposed framework provides a clear, low-cost, and scalable computational approach for EV battery analysis, design studies, and engineering education.

Gutierrez, Marcos, Taco, Diana

Structural Robustness and Regime-Dependent Uncertainty in a Reduced-Order Physics Model for Vehicle Fuel Consumption Using Monte Carlo Analysis

2026-24-0015

9/21/2026

Accurate prediction of vehicle fuel consumption typically relies either on simplified empirical correlations or on high-fidelity simulations that are computationally expensive. However, the structural robustness of reduced-order physics-based models under parametric uncertainty has not been systematically quantified. In particular, the interaction between model simplifications and uncertainty in vehicle and fuel properties across different operating regimes remains insufficiently investigated. This study presents a reduced-order physics-based framework derived from fundamental force and energy balances to estimate fuel consumption in L/100 km. The model includes aerodynamic drag, rolling resistance, inertial effects, drivetrain efficiency, and fuel lower heating value. Unlike purely empirical formulations, the proposed structure preserves physical interpretability while remaining computationally efficient. Monte Carlo simulations are employed to propagate simultaneous uncertainties in vehicle mass, drag coefficient, rolling resistance, engine efficiency, and fuel energy content. Thousands of randomized realizations are executed to quantify output variability, compute confidence intervals, and evaluate robustness indices. In addition, regime-dependent dominance transitions are analyzed by comparing urban and highway operating conditions. Results show that parameter influence is strongly dependent on speed regime: mass and rolling resistance dominate in low-speed conditions, while aerodynamic parameters become dominant at high speeds. Fuel energy content and efficiency exhibit nearly linear inverse relationships with consumption. The reduced-order structure demonstrates stable variance behavior under realistic uncertainty ranges, supporting its suitability for parametric studies and alternative fuel assessment. The proposed framework contributes a systematic evaluation of structural robustness in simplified physics-based fuel consumption models and provides a scalable methodology for uncertainty-aware automotive performance analysis.

Gutierrez, Marcos, Taco, Diana

Data-Driven System Dynamics and Chauvenet-Based Outlier Detection for Assessing Global CO₂ Emissions under Fossil Energy and Electric Vehicle Transition Scenarios

2026-24-0020

9/21/2026

Understanding the structural drivers of global CO₂ emissions requires integrated analysis of fossil fuel production, total energy consumption, and electric vehicle (EV) deployment trends. This study presents a data-driven modeling framework combining system-dynamics formulation with statistical outlier detection implemented in Python to evaluate emission trajectories over a ten-year historical period. The methodology incorporates historical datasets of global CO₂ emissions, primary energy consumption, fossil fuel production, and EV manufacturing volumes. A computational routine developed in Python applies the criterion proposed by William Chauvenet to identify statistically inconsistent observations within the dataset, ensuring robustness prior to regression and correlation analyses. Carbon intensity (CO₂ per unit of energy) is calculated to assess decoupling behavior, while correlation matrices and elasticity indicators quantify the relative influence of fossil production and EV penetration on emissions. The dynamic structure expresses CO₂ emissions as a function of fossil energy share, total energy demand growth, and electrification rate. Sensitivity analysis evaluates the responsiveness of emissions to variations in these parameters. Results indicate that emission reductions are strongly dependent on carbon intensity evolution rather than EV growth alone. Outlier detection enhances model reliability by preventing anomalous years from biasing trend interpretation. The proposed framework provides a transparent and computationally efficient tool for emission diagnostics, transition scenario evaluation, and policy-oriented forecasting within the context of sustainable mobility and global energy transformation.

Gutierrez, Marcos, Taco, Diana

Items per page:

1 – 50 of 68836