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Announcements for SAE Mobilus

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SAE AI Chat — Update and What's Next
* Important Update - SAE AI chatbot Beta program * The SAE AI Chatbot Beta program - Phase 1 will end on March 31, 2026. Following this date, the AI chatbot capabilities will be temporarily unavailable as we begin development of a significantly enhanced version. We sincerely thank all Phase 1 Beta participants for their engagement and valuable feedback - your insights have played an important role in shaping the next phase of this experience. The upcoming version of the SAE AI Chatbot will expand content coverage beyond current standards, incorporating additional critical resources from our digital library. It will also be made available to a broader audience across Mobilus. Want to be part of what's next? Register your interest here to receive updates and be considered for early access. For additional information, visit the FAQs. — The Mobilus Team
SAE International Digitization and AI Policy
SAE International prohibits the entry of SAE Content into any form of Artificial Intelligence (AI) tools, such as ChatGPT, OpenAI, or Microsoft Copilot. Additionally, creating derivatives of SAE Content using AI is also prohibited without express written permission from SAE International. In the case of such use, SAE International will suspend access to SAE Content, and further legal action will be considered.
Welcome to the new SAE Mobilus®
  Welcome to the new era of SAE Mobilus®, redesigned with a modern, intuitive web interface featuring enhanced search capabilities to help you stay informed and easily discover content. Login today: To access content within your subscription and make the most of the platform, please log into your subscription and your personal My Library (sign up is free) located in the top right corner of the screen.   Key Features Intuitive User Interface: We've listened to our customers and tied this to the best practices in the industry to offer an improved, intuitive, and desirable website experience for our users.​ Improved In-app Discoverability: Enhanced search and expanded menu featuring search suggestions that allow for quick discovery of content.​ Announcements: Stay up to date on new features through a dedicated announcements page. Most recent announcements also get featured on the homepage.​ Enhanced Browse capabilities: Browse through over 200,000 publications that are now classified into categories commonly used across the industry. Deep-dive further into any topic or sub-topic of interest to make the most for your corporate projects or research.​ Smart Automation: "Meta Tags" on each result will help you find content related to your search.​ My Library: My dashboard is rebranded as My Library with powerful features to help you make the most out of the platform Mobile Friendly: Upgraded navigation on mobile devices​ Training Available SAE’s Customer Success team is dedicated to delivering the best platform experience possible. Through monthly trainings and short, on-demand video tutorials, find answers to your questions here: SAE Mobilus Spotlight training - Available monthly, hosted live by SAE’s Customer Success team Mobilus Minute – 2-minute videos, available on-demand   Need some troubleshooting help? Access our FAQs by visiting our Help section: https://saemobilus.sae.org/help/home Resources to help you navigate the new platform are available here, where you can find links to quick start guides and video tutorials on features like My Library, Announcements, Saved Search, Login and new features. Have feedback that you'd like to share with us? Please submit your feedback to the Mobilus team through the blue Knowledge bot in the bottom right hand corner of the new platform.

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Recent SAE Edge™ Research Reports

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Wing-in-Ground Effect Vehicles: From Modern Military and Commercial Development to Global Disaster ResponseEPR202501711/24/2025
The wing-in-ground effect (WIG) vehicle represents a significant advancement in aerodynamics and vehicle design, leveraging the ground effect phenomenon to enhance lift and reduce drag when flying close to the surface. This unique capability allows WIG vehicles to achieve higher payloads, longer range, and greater fuel efficiency compared to traditional aircraft, making them an attractive option for modern military and global disaster response applications. Wing-in-Ground Effect Vehicles: From Modern Military and Commercial Development to Global Disaster Response discusses future disaster response, logistics, and military applications for WIG vehicles, including the ongoing development of aerospace and transportation technology. Relavant advancements in materials and propulsion systems holds promise for further enhancing WIG performance and operational range. Additionally, cost-effective and powerful flight computers with various types of mission-enabling sensor suites from the
Sustainable Circular Future Mobility: Environmental Impact of Next-gen VehiclesEPR20250159/22/2025
The next generation of mobility, driven by shared, driverless, connected, and electrified vehicles, holds strong potential to advance sustainability through lower emissions and improved resource efficiency. However, critical questions remain regarding their true environmental impact, including battery lifecycle management, material consumption, and circular manufacturing practices. Sustainable Circular Future Mobility: Environmental Impact of Next-gen Vehicles explores these unresolved issues, focusing on the shift from internal combustion to electric vehicles, supply chain challenges, regulatory gaps, and the operational realities of sustainable productization. It also critically examines the risks of greenwashing, the need for consistent standards, and the role of intersectoral collaboration—with energy, urban planning, information and communications technologies, and waste management sectors—in building resilient, scalable solutions. The report provides strategic recommendations and
Next-gen AI for Aerospace EngineeringEPR20250169/22/2025
Despite growing investments, the widespread adoption and scalable deployment of generative artificial intelligence (AI) remains a challenge due to data trustworthiness, regulatory uncertainty, interpretability, and ethical governance. The need to accelerate automation and maintain the human-in-the-loop demonstrates broader questions of responsibility and transparency. Next-gen AI for Aerospace Engineering investigates the transformative role of GenAI within aerospace engineering, examining its shift from conventional workflows toward more AI-driven solutions in design, manufacturing, and maintenance. It emphasizes GenAI’s emerging ability to automate repetitive mundane tasks, reduce design complexity, and optimize engineering pipelines. The report underscores the need for validation methods that must align AI-generated outputs with physics-informed models, integration with legacy engineering tools (e.g., computational fluid dynamics, finite element analysis, digital twins), and
Small, Mobile, and Autonomous Agricultural RobotsEPR20250128/13/2025
The automation of labor-intensive picking and planting operations is having an immediate impact in the agricultural indutry. In its simplest form, robotic automation can reduce the labor and soil disturbance while enabling organic soil cover and increasing species diversification through precision approaches to planting, weeding, and spraying. With this, pesticides and fertilizers can be applied in a more targeted way, and with machinery visiting fields more frequently, earlier and more targeted intervention can occur before pests become established. Small, Mobile, and Autonomous Agricultural Robots identifies issues that need to be resolved fo for this technology to thrive, including improving methods of acquiring and labeling training data to facilitate more accurate models for specific applications. It also discusses concepts such as general-purpose mechanical platforms for use as carriers of agricultural automation systems with high stability, positional accuracy, and variable
Safety Impacts of Active Safety and Driving Automation FeaturesEPR20250138/13/2025
Advanced technologies that assist the human driver or reduce (or even eliminate) the human driver’s role are becoming increasingly prevalent in new light-duty vehicles used by the general public. These technologies are divided between Active Safety features that monitor the human driver and vehicle motion and act intermittently to mitigate and avoid crashes, and Driving Automation features that assume some or all of the dynamic driving task from the human driver. Both types of technologies have the potential to reduce injuries and save lives by reducing the frequency and/or severity of crashes. Safety Impacts of Active Safety and Driving Automation Features addresses the current capabilities and future potential for Active Safety and Driving Automation features to reduce crash frequency and severity and provides an overview of the state of the industry for both types of features, including current deployments, trends, and anticipated rollouts. Gaps in knowledge, unsettled issues, and
Hybrid Data- and Physical Model-driven Safe and Intelligent Motion Planning and Control for Autonomous VehiclesEPR20250147/18/2025
Autonomous vehicle motion planning and control are vital components of next-generation intelligent transportation systems. Recent advances in both data- and physical model-driven methods have improved driving performance, yet current technologies still fall short of achieving human-level driving in complex, dynamic traffic scenarios. Key challenges include developing safe, efficient, and human-like motion planning strategies that can adapt to unpredictable environments. Data-driven approaches leverage deep neural networks to learn from extensive datasets, offering promising avenues for intelligent decision-making. However, these methods face issues such as covariate shift in imitation learning and difficulties in designing robust reward functions. In contrast, conventional physical model-driven techniques use rigorous mathematical formulations to generate optimal trajectories and handle dynamic constraints. Hybrid Data- and Physical Model-Driven Safe and Intelligent Motion Planning and
Product Assurance in the Age of Artificial IntelligenceEPR20250115/21/2025
Driven by the vast consumer marketplace, the electronics megatrend has reshaped nearly every sector of society. The advancements in semiconductors and software, originally built to serve consumer demand, are now delivering significant value to non-consumer industries. Today, electronics are making inroads into traditionally conservative, safety-critical sectors such as automotive and aerospace. In doing so, electronics—now further propelled by artificial intelligence—are disrupting the functional safety architectures of these cyber-physical systems. Electronics have created the world of cyber-physical systems, raising broader concerns about the broader category of product assurance. Product Assurance in the Age of Artificial Intelligence continues the work of previous SAE Edge Research Reports in examining open research challenges arising from this shift, particularly in automotive systems, as core electronic technologies (e.g., the combination of software and communications) have even
Edge Artificial Intelligence in Connected, Cooperative and Automated MobilityEPR20250095/20/2025
With many stakeholders involved, and major investments supporting it, the advancements in automated driving (AD) are undoubtedly there. Generally speaking, the motivation for advancing AD is driver convenience and road safety. Regarding the development of AD, original equipment manufacturers, technology start-ups, and AD systems developers have taken different approaches for automated vehicles (AVs). Some manufacturers are on the path toward stand-alone vehicles, mostly relying on onboard sensors and intelligence. On the other hand, the connected, cooperative, and automated mobility (CCAM) approach relies on additional communication and information exchange to ensure safe and secure operation. CCAM holds great potential to improve traffic management, road safety, equity, and convenience. In both approaches, there are increasingly large amounts of data generated and used for AD functions in perception, situational awareness, path prediction, and decision-making. The use of artificial
Leveraging Advanced Data Technologies for Smart Traffic ManagementEPR20250085/20/2025
The transportation industry is transforming with the integration of advanced data technologies, edge devices, and artificial intelligence (AI). Intelligent transportation systems (ITS) are pivotal in optimizing traffic flow and safety. Central to this are transportation management centers, which manage transportation systems, traffic flow, and incident responses. Leveraging Advanced Data Technologies for Smart Traffic Management explores emerging trends in transportation data, focusing on data collection, aggregation, and sharing. Effective data management, AI application, and secure data sharing are crucial for optimizing operations. Integrating edge devices with existing systems presents challenges impacting security, cost, and efficiency. Ultimately, AI in transportation offers significant opportunities to predict and manage traffic conditions. AI-driven tools analyze historical data and current conditions to forecast future events. The importance of multidisciplinary approaches and
Space-based Solar Power for Instantaneously Dispatchable Renewable Power on EarthEPR20250054/24/2025
Recent advances are reducing the cost of space launch, high specific power solar cells, and the production of satellite systems. Modular architectures with no moving parts and distributed power systems would minimize assembly and maintenance costs. Together, this may enable space-based solar power to provide decarbonized dispatchable power at a lower cost than equivalent technologies such as nuclear power stations. Space-based Solar Power for Instantaneously Dispatchable Renewable Power on Earth discusses the advances in emerging technologies, like thin film solar cells, reusable launch vehicles, and mass-produced modular satellite systems that would make economic space power feasible. Click here to access the full SAE EDGETM Research Report portfolio.

Latest Journal Issues

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Passenger Vehicle Systems
SAE International Journal of Passenger Vehicle Systems
Electrified Vehicles
SAE International Journal of Electrified Vehicles
Advances and Current Practices in Mobility
SAE International Journal of Advances and Current Practices in Mobility
Sustainable Transportation, Energy, Environment, & Policy
SAE International Journal of Sustainable Transportation, Energy, Environment, & Policy
Connected and Automated Vehicles
SAE International Journal of Connected and Automated Vehicles
Transportation Cybersecurity and Privacy
SAE International Journal of Transportation Cybersecurity and Privacy
Vehicle Dynamics, Stability, and NVH
SAE International Journal of Vehicle Dynamics, Stability, and NVH
Transportation Safety
SAE International Journal of Transportation Safety
Alternative Powertrains
SAE International Journal of Alternative Powertrains
Commercial Vehicles
SAE International Journal of Commercial Vehicles
Engines
SAE International Journal of Engines
Fuels and Lubricants
SAE International Journal of Fuels and Lubricants
Materials and Manufacturing
SAE International Journal of Materials and Manufacturing
Aerospace
SAE International Journal of Aerospace
Passenger Cars - Mechanical Systems
SAE International Journal of Passenger Cars - Mechanical Systems

Recent Books

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Aerospace Standards Index - 2026ASIN20262/25/2026
This valuable resource lists all Aerospace Standards (AS), Aerospace Recommended Practices (ARP), Aerospace Information Reports (AIR), and Aerospace Resource Documents (ARD) published by SAE. Each listing includes title, subject, document number, key words, new and revised documents, and DODISS-adopted documents. AMS Index - Now Available!
Business Reliability Growth for Automotive Engineering, Volume 4R-5522/17/2026
In a world where every business process is under pressure to perform faster, safer, and more reliably, this book delivers a powerful roadmap for sustained operational excellence. Centered on the proven methodology of Design of Experiments (DOE), it shows how organizations can move beyond reactive problem-solving to systematic reliability growth. From well-defined standard operating practices to management-level decision-making, the book connects strategy, data, and execution to create repeatable, measurable results across the enterprise. Readers are guided through practical, real-world applications of DOE, from selecting the right factors and levels to executing robust experiments, analyzing outcomes, validating solutions, and continuously monitoring performance. Each chapter translates complex statistical and engineering concepts into actionable business value, helping teams improve quality, reduce waste, and increase return on investment. Key capabilities explored in this book
January 2026 AMS IndexAMSJAN-20261/24/2026
Fundamentals of Sustainable Aviation FuelsR-5471/22/2026
As global energy demands rise and environmental challenges intensify, the aviation industry faces a critical need to redefine how it powers the skies. Fundamentals of Sustainable Aviation Fuels presents a comprehensive, research-driven exploration of the transition from fossil-based fuels to renewable alternatives that promise to reshape the future of air transportation. This book bridges the gap between academic knowledge and industrial application, offering a detailed overview of the science, technology, and policy driving the adoption of Sustainable Aviation Fuels (SAFs). From understanding feedstocks and conversion pathways to evaluating production technologies, emissions performance, and life-cycle impacts, the text equips readers with a holistic understanding of SAFs within today’s global energy landscape. Across five insightful chapters, readers will find a clear roadmap: the evolution and importance of SAFs; production methods and technological foundations; environmental
SAE International’s Dictionary of ADAS and Connected VehiclesR-5591/20/2026
The convergence of Advanced Driver Assistance Systems (ADAS) and connected vehicle technologies is ushering in a transformative era of automotive innovation—one that is fundamentally enhancing vehicle safety, efficiency, reliability, and the overall driving experience. SAE International’s Dictionary of ADAS and Connected Vehicles stands as the definitive reference for this rapidly evolving domain, meticulously compiled to clarify and standardize the language shaping modern mobility. Inside, readers will find clear, authoritative definitions encompassing the full spectrum of technologies that enable connected and automated driving—including vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X) communication systems. This comprehensive resource translates complex engineering terminology into accessible language, enabling engineers, researchers, policymakers, educators, and enthusiasts to share a common technical foundation. Reflecting the latest
Safety, ADAS, and Cybersecurity: Vol. 4EPRCOMPV01202512/29/2025
Infrastructure Enablers for Automation: Vol. 4EPRCOMPV02202512/29/2025
Automated Vehicles: Development, Testing, and Validation: Vol. 4EPRCOMPV03202512/29/2025
Commercial Vehicles: Vol. 2EPRCOMPV05202512/29/2025
Manufacturing and Industry 4.0 : Vol. 2EPRCOMPV06202512/29/2025

Recently Published

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Predicting the Impact of FIA 2026 Power Unit Regulations on the Performance of Formula 1 Car2026-01-02684/7/2026
This work investigates the impact of the forthcoming 2026 FIA Formula 1 power unit regulations on vehicle track performance. This new regulation introduces a rebalanced power distribution between the internal combustion engine and the Motor Generator Unit-Kinetic (MGU-K), with each unit contributing up to 350 kW. This transition nearly triples the previous 120 kW output of the MGU-K while constraining the internal combustion engine through newly imposed fuel energy limits. A full vehicle powertrain model was developed in GT-Suite following the 2026 FIA technical directives. Particular attention was given to Articles 5.4.7 to 5.4.10, which define key constraints on hybrid operation: a maximum variation of 4 MJ in battery state of charge, up to 9 MJ of recoverable energy per lap, and a peak MGU-K electrical power output of 350 kW. The model includes updated architecture specifications, active aerodynamic modules, energy deployment logic, and component-level constraints. Telemetry data
Giaquinta Aitala, Lucio, Samuel, Stephen
CrashML Analyzer: A Data-Driven Approach for Investigation and Fault Assignment in Autonomous Driving Collisions2026-01-01604/7/2026
This study investigates factors contributing to autonomous vehicle (AV) accidents and proposes an automated fault determination framework. A total of 563 accident reports from the State of California Department of Motor Vehicles spanning from 2019 to 2024 were analyzed by converting unstructured standardized reports into structured data using custom extraction tools. Analysis of these reports reveals that AVs were not at fault in 69.4% of cases and were fully at fault for 22.6% of cases. The proposed method uses these reports to provide an early indicator of fault likelihood and potentially replaces tedious manual review. Machine Learning (ML) and Natural Language Processing techniques were used to replicate the reported faults, achieving 96% average accuracy across three models: Gradient Boosting, Linear Regression, and Random Forest. Through feature engineering techniques in semantic feature extraction from narrative accident descriptions, quantifiable variables were obtained and
Rwejuna, Florida Perfect, Majid, Nishatul, Goutham, Mithun, Loukili, Alae
Application of Reliability Growth Methods in Conjunction with Design for Reliability Framework to Validate Heavy Truck Design2026-01-01374/7/2026
Why field campaigns in the automotive industry have been going up over the years despite the strong development of technical knowledge, computational design tools and techniques to secure higher reliability standards since early stages of development phases? Uncertainties created by product complexity have been a factor that affects the ability of the manufacturers to prevent design failures before the product launch. Another factor is the shorter product development time, less test time to validate the product means that the new design will not have enough exposure to the real truck application and so some failures may not be able to be detected during the project. To deal effectively with uncertainties this study shows an application of reliability growth techniques in conjunction with DfR- Design for Reliability framework to validate the truck design in the customer application. The Crow - AMSAA method is applied to measure the reliability growth of the complete vehicle in various
Coitinho, Marcos
Propulsion System Design Considerations for HEV Versus EV Applications2026-01-06414/7/2026
The design and development of EVs and HEVs has become a growing issue recently due to concerns about pollution and dependence on non-renewable fossil fuels. Accordingly, General Motors (GM) has an evolving vehicle electrification plan over the past several decades and into the future to deliver low-cost and efficient EVs and HEVs. Propulsion system requirements for the applications of EV and HEVs are quite different and therefore, the design principles and directions are also distinct between these cases. From micro-hybrid and full plug-in hybrid applications to full EV applications, design requirements, strategies and outcomes can widely vary. Continuous and peak duty are substantially different depending on the application of the vehicle. Motor operational duty is significantly higher for EV compared to the electric motor of a hybrid electric vehicle. Motor torque, power and efficiency requirements are also higher for EV motors, which greatly influences the choice of motor type and
Momen, Faizul, Jensen, William, Das, Shuvajit, Chowdhury, Mazharul, Alam, Khorshed, Anwar, Mohammad, Reinhart, Timothy
Safety-Centered Scenario Generation for Autonomous Vehicles2026-01-05294/7/2026
This paper presents a scenario generation framework that creates diverse, parametrized, and safety-critical driving situations to validate the safety features of autonomous vehicles in simulation [1]. By modeling factors such as road geometry, traffic participants, environmental conditions, and perception uncertainties, the framework enables repeatable and scalable testing of safety mechanisms, including emergency braking, evasive maneuvers, and vulnerable road user protection. The framework supports both regulatory and edge case scenarios, mapped to hazards and safety goals derived from Hazard Analysis and Risk Assessment (HARA), ensuring traceability to ISO 26262 functional safety requirements and performance limitations. The output from these simulations provides quantitative safety metrics such as time-to-collision, minimum distance, braking and steering performance, and residual collision severity. These metrics enable the systematic evaluation of evasive maneuvering as a safety
Chandra Shekar, Kiruthiga, Arab, Aliasghar
Development and Application of CAE Methods for Predicting Snow Accumulation on Floor Undercovers During Snowy Road Driving.2026-01-04824/7/2026
This study presents a simulation method for reproducing slush accumulation on underbody components, with a particular focus on the floor undercover, during vehicle operation on slush-covered roads. As electrified vehicles become increasingly important in the pursuit of carbon neutrality, the adoption of aerodynamic undercovers to improve driving range has accelerated. However, these components are exposed to various environmental stresses, including water, chipping, and especially snow and slush, which can lead to damage and performance degradation. While previous research has addressed water and chipping stresses through simulation, studies on slush-induced stress have been limited. To address this gap, the Moving Particle Semi-implicit (MPS) method was applied, incorporating a power-law model to represent the non-Newtonian flow characteristics of slush. Parameter identification was conducted through steel ball drop tests and tire scattering tests, ensuring both qualitative and
Matsuura, Tadashi, Annen, Teruyuki, Harada, Takeyuki, Ueno, Shigeki, Asai, Mikio, Watanabe, Haruyuki
Modeling Non-Uniform Temperature Distribution in Lithium-Ion Batteries2026-01-01204/7/2026
Non-uniform temperature distribution within lithium-ion battery cells is a critical challenge that accelerates degradation, compromises safety, and reduces pack-level performance in electric vehicles (EVs). This work focuses on modeling and minimizing these thermal gradients through the structured optimization of a liquid-based Battery Thermal Management System (BTMS). A one-dimensional transient thermal model is developed to capture the axial temperature differentials (ΔT) in a cylindrical cell under dynamic drive-cycle loading, incorporating detailed heat transfer from the cell interior through thermal interface materials (TIM) and an aluminum cooling plate to the coolant. Using a Design for Six Sigma (DFSS) approach with an L18 orthogonal array, key control factors—including coolant flow rate, inlet temperature, TIM properties, and plate geometry—are systematically analyzed to identify configurations that optimally balance low average temperature with minimal internal temperature
El-Sharkawy, Alaa, Asar, Mona, Serpento, Stan, Sheta, Mai
A Framework for Predicting Mechanical Performance of FFF Printed Parts Using Multimodal Deep Learning on Infrared Images2026-01-01404/7/2026
Fused filament fabrication (FFF) has gained popularity in recent years because it can produce prototypes and functional components with complex geometry. Because of inherent process variability, the components often exhibit defects such as warping, layer delamination, voids, and poor surface finish, as well as issues related to variable material strength and anisotropy. In-situ monitoring (ISM) of the FFF process is a promising technique to predict part performance, which in turn can support accept or reject decisions for printed parts. This paper proposes a framework for incorporating ISM-generated information, with a particular focus on infrared (IR) image analysis for this purpose. IR camera images, in conjunction with numerical features such as infill pattern and extruder nozzle temperature, serve as an input to a multimodal deep learning (MDL) model that predicts the mechanical performance of printed parts. In the framework, convolutional neural nets process image inputs, while a
Mollan, Calahan, Kulkarni, Saurabh, Malik, Ali Ahmad, Patterson, Albert E., Pandey, Vijitashwa
Design, Fabrication, and Assessment of a User-Friendly Suspension System for a 2025 BAJA Vehicle2026-01-05884/7/2026
A suspension system was designed, fabricated, and tested following a systems design approach by an SAE Off-road Team from a North Midwest university. Compared to previous suspensions, the new suspension system is more reparable and contains a minimal number of custom parts, while still maintaining sufficient strength to withstand dynamic loads experienced when operating the vehicle. Modifications were also made to fit the newly designed vehicle body frame. As an integral part of the team’s 2025 Baja vehicle, the redesigned suspension system contributed to the vehicle’s improved performance during the 2025 SAE (Society of Automotive Engineers) Baja Competition. This paper presents a detailed account of the design, development, and fabrication process of the suspension system. The final design was tested and evaluated via both computer simulations and physical tests, whose efficiency and reliability were finally demonstrated by the team’s improved ranking in the 2025 Baja SAE Competition
Liu, Yucheng, Anderson, Matthew, Larson, Cody, Rodgers, Joshua, Seberger, Aaron, Letcher, Todd
Multi-Physics Topology Optimization and Neural Network–Based Design of Experiments for Lightweight Automotive Cast Components2026-01-05104/7/2026
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
AI Powered Failure Mode and Effect Analysis in Safety Critical Industry2026-01-01064/7/2026
This paper presents the first systematic examination of Large Language Model (LLM) capabilities for automating the development of Failure Mode and Effects Analysis (FMEA) utilizing architectural diagrams as input. Although prior research has examined LLMs for FMEA tasks, our methodology incorporates innovative aspects, such as the direct analysis of architectural diagrams for component extraction, prediction of failure modes, causes, estimation of risk and a human-in-the-loop (Hu-IL) validation framework. We examine the capability of general-purpose LLMs to accurately automate the creation of FMEA by formulating a methodology that extracts components and signals from architectural diagrams, conducts automated component classification, and produces a comprehensive FMEA form sheet encompassing Severity, Occurrence, and Detectability (S/O/D) scoring. Our methodology is grounded in structured prompt engineering theory, utilizing scope bounding techniques to reduce hallucination while
Diwakaruni, Sundara Sasi Koushik, Krishnamurthy, Anunay
Evaluating the Effects of Improper Positioning and Unbuckling of a Restraint System on a 10-Year-Old Child ATD in a Far Side Side-Impact Environment2026-01-05624/7/2026
Five sled tests were performed with a Hybrid III (H-III) 10-year-old child sized Anthropomorphic Test Device (ATD) positioned in the 2nd row left seat of a three row 2006 Sport Utility Vehicle (SUV). A HYGE Sled buck was positioned to represent/replicate a side impact collision to the passenger (right) side of the SUV, with a Principal Direction of Force (PDOF) of 60 degrees, resulting in a far side side-impact for the ATD. Of the 5 tests performed, three of the five tests were performed with a delta-V of 17 mph, and two of the tests at a delta-V of 24 mph. Of the 17 mph tests, one test was performed with a properly restrained ATD, and two tests performed with improper restraint positioning. Both of the 24 mph tests were performed with improper restraint positioning, effectively identical to the two 17 mph delta-V tests. The two improper restraint use tests (at both 17 and 24 mph delta-V) included two different improper restraint scenarios. The first scenario of improper restraint
Luepke, Peter, Hewett, Natalie, Betts, Kevin, Van Arsdell, William, Weber, Paul, Stankewich, Charles, Miller, Gregory, Watson, Richard, Sochor, Mark
Aerodynamic Proximity Effects between the DrivAer and AeroSUV Standard Models, Part 2 – Adjacent-Lane and Lateral-Offset Interactions2026-01-06064/7/2026
Aerodynamic interactions between two 30%-scale passenger vehicles in close proximity were examined experimentally in a large wind tunnel, with a focus on longitudinal separations up to two vehicle lengths, lateral separations up to one lane width, and combinations thereof. Part 1 of this paper described the longitudinal following (platooning) configurations of these results, while this paper concentrates on adjacent-lane influences and lateral-offset effects when platooning at a single separation distance. Test models were based on the DrivAer and Aero-SUV open-access geometries, each with slant-back (Notchback or Fastback) and square-back (Estateback) variants. This provided four distinct model pairings, not all of which were tested in each positional arrangement. Adjacent-lane results matched the trends from a smaller-scale study in a different wind tunnel using the same geometry pair, with small-but-distinct differences attributed to different blockage ratios in the two wind-tunnel
McAuliffe, Brian, Ghorbanishohrat, Faegheh
Backlash Evasion Strategy for Enhancing Vehicle Cornering Performance2026-01-06404/7/2026
This study presents a torque distribution control strategy for EVs with e4WD powertrain to overcome the trade-off between ensuring vehicle acceleration and deceleration responsiveness and mitigating backlash shock in the driving system. The deterioration of the drivability which occurs from the intrinsic hardware characteristics of the drivetrain is prevented by designing a response-priority drive mode in which neither front or rear motor torque is allowed to change its sign. Instead, in such drive mode, the front motor torque is only allowed to perform regenerative braking while the rear motor torque is only allowed to produce positive acceleration torque. In order to avoid sacrificing the maximum acceleration by applying such strategy, the mode transition function is implemented as well. In addition, in order to prevent backlash impact due to drivetrain compliance, variable offset torque based on drivetrain compliance model is evaluated in real time and applied to each motor command
Oh, JIWON, Lee, Ho Wook
Simultaneous Reduction of NOx and Fuel Consumption for Off-Road Powertrains2026-01-02964/7/2026
Simultaneously reducing criteria pollutants and fuel consumption is important for clean air and improving vehicle total cost of ownership. The goal of this effort was focused on a 90% NOx reduction and 10% fuel savings for an off-road 407 kW diesel engine. The baseline was a production Fiat Powertrain 13L engine and aftertreatment system meeting 0.4 g/kW-hr NOx. The baseline system was quantified over the NRTC, RMC, new low load cycle and five field cycles. A next generation engine was built incorporating several fuel-efficient design features, including a higher compression ratio, increased fuel-rail pressure, low-friction piston rings, and a high-efficiency variable-geometry turbocharger. Cylinder deactivation and EGR pump technologies were added to this engine as well. The combination was optimized prior to adding advanced aftertreatment systems, showing the trade-off of engine out NOx and exhaust temperature. Two next-generation catalyst technologies were employed into a LO-SCR
McCarthy, Jr.,, James, Wine, Jonathan, Bradley, Ryan, Hasseman, Andy, Prikhodko, Vitaly, Howell, Thomas
Real-time Parameter Optimization for Eco-driving Control in Connected and Automated Vehicles Using Reinforcement Learning2026-01-00414/7/2026
This paper introduces a novel methodology to enhance the energy efficiency of eco-driving controllers in Connected and Automated Vehicles (CAVs) by leveraging reinforcement learning (RL) techniques for real-time parameter optimization. Traditional eco-driving strategies rely on fixed control parameters, which limit adaptability across diverse traffic and road conditions. To address this, we apply continuous action space RL algorithms, specifically Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO), to dynamically tune four key parameters within a model predictive control framework that is grounded in Pontryagin’s Maximum Principle (PMP). These parameters influence acceleration, braking, cruising, and intersection-approach behaviors, making them critical for achieving optimal eco-driving performance. Our study employs Argonne National Laboratory’s RoadRunner simulator, a Simulink-based environment designed for high-fidelity CAV analysis, incorporating
Zhang, Yaozhong, Ammourah, Rami, Han, Jihun, Moawad, Ayman, Shen, Daliang, Karbowski, Dominik
Methodology for Generating Off-Road Tracks for Powertrain Simulation of Off-Road Passenger Vehicles2026-01-02754/7/2026
For off-road driving, particularly on steep grades and over barriers, the engine torque is a key design criterion of off-road vehicles. In conventional powertrains with combustion engines, mechanical all-wheel-drive systems combined with differential locks are used to distribute the torque demand between the front and the rear axle based on wheel-specific traction. With the growing market share of electric powertrains, off-road applications are becoming increasingly relevant for electric passenger cars. In comparison to conventional powertrains, electric all-wheel-drive configurations do not have a mechanical torque transfer between the two axles. If one axle experiences low traction, the second axle can rely on its own torque capability only. Transfer of unused torque of the slipping axle to the other one is not possible. The challenge, therefore, is to specify the right torque requirements for each axle for off-road driving while avoiding over-dimensioning and high powertrain costs
Martin, Michael, Winkelheide, Jonas, Hartmann, Lukas, Sturm, Axel, Henze, Roman
Effects of Gasoline Composition and Operating Parameters on the Response of End-Gas Autoignition to Nitric Oxide in a Lean-Burn DI-SI Engine2026-01-03134/7/2026
Lean operation of spark-ignition engines can lead to engine thermal efficiency gains and lower NOx emissions due to reduced combustion temperatures. Yet, lean operation could still face challenges in end-gas autoignition and knock generation due to higher intake pressures and trapped NO in the residual gas. This study evaluates the impact of NO on end-gas autoignition for two gasoline fuels with similar octane rating but different composition: high cycloalkane fuel (HCA) and high olefin fuel (HO). Experiments were performed at stoichiometric and lean (λ = 2) conditions and at two engine speeds of 1400 rpm and 2000 rpm. Accompanying chemical kinetics simulations in CHEMKIN revealed that the mechanisms controlling the effect of NO on autoignition are similar λ = 2 and λ = 1, with NO + HO2 = NO2 + OH being the main pathway for enhancing reactivity by promoting low-temperature heat release (LTHR). The compositionally different fuels reacted differently to NO seeding and engine speed, and
Kim, Namho, Abboud, Rami, Sjöberg, Magnus, Lopez Pintor, Dario, Saggese, Chiara, Matsubara, Naoyoshi, Kitano, Koji, Yamada, Ryota, Sugata, Kenji
Achieving Euro 7 WHSC NOx Emission Targets from a Spark Ignited Engine Using Methanol-Hydrogen Co-Fuelling2026-01-03214/7/2026
E-methanol is increasingly seen as a promising clean fuel because its chemical makeup is close to fossil fuels, making it easier to use in existing engines. It offers a carbon-neutral option to help reduce greenhouse gases in sectors where cutting emissions is especially difficult, such as transportation. However, while e-methanol avoids adding new carbon dioxide, burning it in internal combustion engines still releases harmful gases like oxides of nitrogen (NOx) and other toxic by-products like formaldehyde and formic acid that damage both health and the environment. This report explores a new strategy that combines methanol with hydrogen to run engines under “ultra-lean” conditions and its impact on emissions, performance and efficiency. Experiments were carried out on a single-cylinder spark ignition engine, with directly injected methanol and port fuelled injection of hydrogen. The findings show that adding about 10% hydrogen (energy basis) at low engine loads can extend the lean
Ambalakatte, Ajith, Geng, Sikai, Cairns, Alasdair, Varaei, Amirata, Harrington, Anthony, Hall, Jonathan, Bassett, Mike, Cracknell, Roger
Quantifying Highway Flow Improvements via CACC-Based Truck Platooning: A Statistical Modeling Approach2026-01-04634/7/2026
The advancement of Cooperative Adaptive Cruise Control (CACC) technology enables vehicle platooning on public roads, offering significant potential to enhance urban mobility, driving safety, and energy efficiency. Among various applications, truck platooning has become a promising strategy to increase highway flow rates by reducing vehicle headways, improving coordination, and optimizing space utilization. This paper presents a quantitative assessment of a CACC-based truck platooning system, focusing on its effectiveness in enhancing highway mobility under varying traffic conditions. A statistical regression model is developed and calibrated using simulations of real-world highway networks to identify key influencing factors and evaluate the resulting improvements in traffic flow. The analysis considers five primary variables: desired platoon speed, platoon size, space headway, percentage of platooning trucks, and non-platoon traffic flow. The study systematically examines the impact
Karbasi, Amir Hossein, Wang, Jinghui, Yang, Hao
Advancing Safe Battery Design through Experimental Cell Characterization of Thermal, Electrical and Mechanical Properties2026-01-04084/7/2026
Lithium-ion batteries are critical to Electric Vehicles (EV) and grid-scale energy storage. Safe design of battery systems relies on accurate simulation of thermal runaway under electrical, thermal, and mechanical abuse. A predictive battery simulation requires characterization of electrical, thermal, and mechanical properties at the full cell and cell-component levels. In this study, a commercial cell from an EV was disassembled, and tested to support both homogenized and detailed computational models. At the cell level, electrical properties were characterized using Hybrid Pulse Power Characterization (HPPC) testing to assess the cell’s power capability. Full cell compression tests were conducted to characterize mechanical behavior under deformation and used to develop a multi-physics homogenized cell model. On the other hand, detailed cell modeling that includes different component layers could help users understand localized cell integrity under mechanical deformation. At the
Challa, Vidyu, Rostami-Angas, Masoud, kong, Kevin, Wang, Leyu, Reichert, Rudolf, Kan, Cing-Dao
A Study on Fail-Safe and Limp-Home Strategies for Electric Drive Failures in P1-P2 (P)HEVs2026-01-04274/7/2026
Hyundai Motor Company’s TMED-II hybrid system adopts a P1–P2 parallel motor layout, which improves power distribution flexibility but increases reliance on electric drive components. Failures in motors, inverters, or other power electronics can critically affect drivability and safety, making robust Fail-Safe strategies essential. This study proposes a three-stage, sequential Limp-Home strategy for P1–P2 HEVs under P2 motor system failure. Unlike conventional methods that open the main relay and rely solely on the engine, the proposed approach keeps the high-voltage (HV) system active whenever possible to maintain performance, safety, and comfort. Stage 1 – P1 motor-based State of Charge (SOC) control: Keeps the main relay closed and uses the P1 motor to maintain SOC within set limits. Overcharge is mitigated by operating the motor in discharge mode, and overdischarge is mitigated through regenerative operation. Engine torque is adjusted to match motor torque demand, preserving launch
Rho, Jeongwon, Park, Sangcheol, Oh, Sung Hwan
Evaluation of Current and Next-Gen Diesel Particulate Filter Performance for US EPA MY27+ LD/MD Particulate Mass Emissions Regulation2026-01-03694/7/2026
Diesel particulate filters (DPF) have been part of vehicle after-treatment solutions in the US since being adopted in 2007 as the “go-to” solution for meeting particulate mass (PM) standards as set by the EPA for HD diesel engines. Within the highly popular LD/MD truck segment, defined as trucks weighing between 8501lb-14000lb, these limits have seen additional reduction in PM levels to 8 or 10 mg/mile as these vehicles have transitioned mostly over to chassis-based certification since 2014-2017. However, these reductions in PM requirements have been relatively minor, allowing for DPF technology used on these platforms to remain mostly unchanged over the same time period. With the finalization of MY27+ LD/MD vehicle emissions standards; PM limits are now set to make significant reductions down to 0.5 mg/mile, with phase-in to be completed by MY31. While the new limits present significant challenges for gasoline vehicles and most likely will require the use of gasoline particulate
Warkins, Jason, Sadek, Ghadi, He, Suhao
Characterizing Regenerative Braking of the Tesla Model 3 Using Chassis Dynamometer Testing2026-01-04534/7/2026
Regenerative braking has a strong influence on the energy efficiency and drivability of battery-electric vehicles. This study establishes an empirical baseline analysis under controlled conditions of the regenerative braking behavior of the 2020 Tesla Model 3 to support the interpretation of on-road performance and serve as a reference for subsequent testing and analysis. The tests were performed on a four-wheel-drive chassis dynamometer at Argonne National Laboratory, combining Multi Cycle Testing (MCT) to simulate real world driving patterns (city, highway) with coast-down tests to isolate periods where the motor is operating in regen mode and compare the behavior across different parameters. Vehicle data was collected from the vehicle using taps in the Controller Area Network (CAN) bus as well as a high-resolution power analyzer. The vehicle displayed the highest efficiency during simulated city driving conditions (3.62 miles/kWh followed by highway (3.40 miles/kWh) and aggressive
Pierce, Benjamin Branch, Di Russo, Miriam, Das, Debashis, Zhan, Lu, Stutenberg, Kevin
Effects of Temperature and Mechanical Load on Li-Ion Battery Aging: Insights from Multi-Rate Reference Performance Test2026-01-03834/7/2026
As the utilization of lithium-ion batteries in electric vehicles expands, monitoring the usable cell capacity (UCC) is essential for ensuring accurate state-of-health (SOH) estimation. Battery performance degradation is influenced by temperature and constraints. Capacity tests in laboratory settings are typically conducted at low C-rates to approximate equilibrium conditions, whereas in real vehicle applications, charging currents are often much higher. This discrepancy in rates frequently results in deviations between laboratory characterization and on-board Battery Management Systems (BMS) capacity estimation. To investigate how C-rate of diagnostic Reference Performance Test (RPT) modulates aging effects under temperature and mechanical loading, we conducted long-term cycling tests on lithium iron phosphate/graphite pouch cells at 25°C and 45°C under different constrained conditions. The cycling protocol is a tiered multi-rate protocol. Cells were aged at Block1 under 1C, and UCC
Zhang, Shan, Niu, Zhice, Xia, Yong