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Announcements for SAE Mobilus

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Vertical Flight Society Technical Papers - Coming Soon to Mobilus
Vertical Flight Society papers coming soon to SAE Mobilus. In partnership with the Vertical Flight Society (VFS), SAE Mobilus will be hosting more than 17,000 technical papers and proceedings from VFS. Click here to see a sample of the content covering decades of rotorcraft and vertical flight technologies.
Important Update – Accessibility Enhancements on SAE Mobilus
We’re pleased to share that SAE Mobilus has recently completed a comprehensive set of accessibility improvements across the platform. As a result, Mobilus is now aligned with WCAG 2.2 Level AA standards in most key categories. These updates are part of our ongoing commitment to ensuring the platform is accessible to all users. Enhancements include improvements to keyboard navigation, screen reader compatibility, visual contrast, error handling, and overall usability across core areas of the site. An updated Voluntary Product Accessibility Template (VPAT v2.5 – March 2026) is now available, providing a detailed overview of our accessibility conformance. You can access the VPAT directly within the Help section. Accessibility is a continuous effort. We will continue to evaluate and improve the platform to better support all users and meet evolving standards. If you have any feedback or experience any accessibility issues, we welcome your input. - The SAE Mobilus Team
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 advanced air mobility and drone industry further enable the development of modern, safe, and practical WIG platforms. Click here to access the full SAE EDGETM Research Report portfolio.
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 mitigation of algorithmic biases. Click here to access the full SAE EDGETM Research Report portfolio.
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 actionable solutions to help stakeholders better navigate the transition toward a circular future for mobility. It further highlights the overlooked complexities in the Global South, emphasizing the importance of ethical market expansion and localized mobility strategies. Click here to access the full SAE EDGETM Research Report portfolio.
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 suggestions for future research will also be discussed. Click here to access the full SAE EDGETM Research Report portfolio.
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 track, as well as the economics of moving capital intensive automation systems toward a service-provision business model. Click here to access the full SAE EDGETM Research Report portfolio.
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 Control for Autonomous Vehicles presents a hybrid framework that combines data-driven insights with the robustness of physical models. It identifies key challenges in fusing these disparate methods and outlines potential solutions in developing robust fusion strategies, establishing generalized mixed dynamics models, and designing multi-objective robust control systems. In addition, the report explores future research directions to enhance learning efficiency, improve adaptability to rare but critical scenarios, and ultimately pave the way for secure, efficient, and human-like autonomous driving systems. Click here to access the full SAE EDGETM Research Report portfolio.
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 redefined what it means to be a "product." Click here to access the full SAE EDGETM Research Report portfolio.
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 intelligence (AI) is instrumental in processing such data, and in that context, “edge AI” is a more recent type of implementation. Edge AI involves AI algorithms in edge computing devices, which requires hardware operating close to where data is generated. This report explores the potential of edge AI in CCAM. Different perspectives on edge AI for CCAM are explored and definitions drafted. Primary applications are explored, and an outlook on further advancements in applications is presented. The report includes a discussion on the benefits, risks, and challenges related to the use of edge AI in this domain. Major issues such as privacy and cybersecurity are considered, as are misconceptions. Furthermore, potential learning benefits, using experiences gained in other sectors, are introduced. NOTE: SAE Edge Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. These reports are not intended to resolve the challenges they identify or close any topic to further scrutiny.
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 educational programs in leveraging AI for transportation applications are emphasized in this report. Click here to access the full SAE EDGETM Research Report portfolio.
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
Transportation Cybersecurity and Privacy
SAE International Journal of Transportation Cybersecurity and Privacy
Connected and Automated Vehicles
SAE International Journal of Connected and Automated Vehicles
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
Passenger Cars - Mechanical Systems
SAE International Journal of Passenger Cars - Mechanical Systems
Passenger Cars - Electronic and Electrical Systems
SAE International Journal of Passenger Cars - Electronic and Electrical Systems
Materials and Manufacturing
SAE International Journal of Materials and Manufacturing
Engines
SAE International Journal of Engines
Aerospace
SAE International Journal of Aerospace

Recent Books

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Composite Materials Handbook Volume 5 - Revision BR-4608/27/2026
The fifth volume of this six-volume compendium publishes technical guidance and properties on ceramic matrix composite material systems. The selected guidance on technical topics related to this class of composites includes material selection, processing, characterization, testing, data reduction, design, analysis, quality control, application, case histories, and lessons learned of typical ceramic matrix composite materials. Volume 5, which covers ceramic matrix composites, supersedes MIL-HDBK-17-5 of June 17, 2002. The Composite Materials Handbook, referred to by industry groups as CMH-17, is an engineering reference tool that contains over 1,000 records of the latest test data for polymer matrix, metal matrix, ceramic matrix, and structural sandwich composites. CMH-17 provides information and guidance necessary to design and fabricate end items from composite materials. It includes properties of composite materials that meet specific data requirements as well as guidelines for design, analysis, material selection, manufacturing, quality control, and repair. The primary purpose of the handbook is to standardize engineering methodologies related to testing, data reduction, and reporting of property data for current and emerging composite materials. It is used by engineers worldwide in designing and fabricating products made from composite materials.
July 2026 AMS IndexAMSJULY-20267/22/2026
Metallic Materials Properties Development and Standardization (MMPDS) Handbook - 2026 - Volume IB-994_VOLUME17/1/2026
The Metallic Materials Properties Development and Standardization MMPDS-2026 supersedes MMPDS-25 and prior editions of the MMPDS Handbook as well as all editions of MIL-HDBK-5, Metallic Materials and Elements for Aerospace Vehicle Structures Handbook that were maintained by the U.S. Air Force. The last edition, MIL-HDBK-5J was cancelled by the U.S. Air Force in March 2006. This document contains design information on the mechanical and physical properties of metallic materials and joints commonly used in aircraft and aerospace vehicle structures. All information contained in this Handbook has been reviewed and approved using a standardized process. The development and ongoing maintenance process involves certifying agencies, including the FAA, DoD, and NASA, and major material suppliers and material users worldwide. The information and procedures in this Handbook are continuously reviewed and modified or removed as determined to be appropriate. With advances in materials and fastener systems, and with the review process of existing information, periodic updates of the MMPDS should be expected. As such, it is recommended that the latest version of the MMPDS be used. Volume II: Process Intensive Materials and Joining Technologies.
Metallic Materials Properties Development and Standardization (MMPDS) Handbook - 2026 - Volume IIB-994_VOLUME27/1/2026
Volume II publishes the framework for generating and analyzing the data so that material sponsors can generate the right data, knowing how it will be analyzed by the Battelle Memorial Institute. MMPDS-2026 Volume II Process Intensive Materials and Joining Technologies does not currently have any material properties, focusing on additive manufacturing of metallic materials. MMPDS-2026 Volume II focuses on non-conventional processes and will be published each year. Volume I: Conventional Materials and Joint Allowables.
1D and MultiD Modeling Techniques for IC Engine Simulation Second EditionR-5805/29/2026
The internal combustion (IC) engine remains the most widely used thermal machine in the world and will continue to play a central role in global mobility for decades to come. Even as alternative powertrains expand, optimized IC engines—often integrated within hybrid and electrified architectures—are essential to achieving low fuel consumption, near-zero pollutant emissions, and reduced carbon dioxide output. Advances in combustion strategies, synthetic fuels, turbocharging, and aftertreatment systems are driving this evolution. This fully revised and expanded second edition reflects the rapid progress made in engine modeling and simulation over the past two decades. One-dimensional (1D), three-dimensional (3D), and coupled 1D–3D modeling techniques have become indispensable tools in modern engine design, enabling engineers to evaluate performance, efficiency, and emissions through advanced virtual engine environments. Bringing together contributions from leading international researchers and industry experts, this book presents the most significant and recent developments in IC engine simulation. Topics include 0D, 1D, quasi-3D, and full 3D modeling approaches; turbocharging and aftertreatment systems; co-simulation and real-time applications; hydrogen and two-stroke engines; and advanced thermal modeling techniques. Designed for engineers, researchers, and graduate students, this second edition serves as a comprehensive reference for understanding, comparing, and applying state-of-the-art IC engine modeling methodologies in an evolving mobility landscape. Chapter topics include: • state of the art of 1D thermo-fluid dynamic simulation models • virtual engine development: 1D- and 3D-CFD up to full engine simulation • advanced 0D and quasiD thermodynamic combustion models for SI and CI engines • compressor and turbine (turbocharger, supercharger, or turbocompounding device) as boundary conditions for 1D simulations • 3D-CFD combustion models for SI and CI engines • model order reduction for real-time simulation and control applications • modeling of EGR systems •1D engine model in XiL application-a simulation environment for the entire powertrain development process • coupling of 1D and 3D fluid dynamic models for hybrid simulations • extending the 1D approach to the simulation of 3D components: the quasi-3D approach • 1D simulation models for aftertreatment components• 3D simulation models for after-treatment systems • modeling of IC engine silencers and tailpipe noise: 1D and 3D approaches • hydrogen-fueled internal combustion engines • optimization of the gas exchange process in advanced 2-stroke engines • IC engine 0D/1D thermal modeling
Variable Compression Systems for Future Engines and FuelsR-5534/30/2026
Variable Compression Engines: Enabling Net Zero explores variable compression ratio (VCR): one of the most promising—and historically elusive—advancements in internal combustion engine (ICE) technology. Long recognized for its thermodynamic benefits, VCR has challenged engineers for decades due to its mechanical complexity. Today, as the global mobility landscape demands ever higher efficiency, lower emissions, and greater fuel flexibility, VCR has emerged as a viable and transformative solution. This book delivers a comprehensive and authoritative examination of VCR technology, quantifying its efficiency and CO2-reduction potential while surveying the wide range of mechanisms conceived, developed, and tested over more than a century of engine innovation. By combining historical insight with modern analysis, the authors reveal the remarkable ingenuity of past and present engine designers—and demonstrate that VCR is not only feasible, but increasingly essential. Positioned squarely within the context of global decarbonization, the book argues for a realistic, inclusive pathway to net-zero emissions—one in which ICEs continue to play a critical role. VCR technology enables higher efficiency across almost all operating conditions, supports advanced combustion strategies, and allows engines to operate effectively on a broad spectrum of low- and zero-carbon fuels, from biofuels to synthetic e-fuels. With VCR now in high-volume production and poised for broader adoption across automotive, heavy-duty, and marine applications, this timely volume is an essential resource for OEMs, suppliers, policymakers, researchers, and students seeking practical, scalable solutions for a sustainable energy future. Chapter topics include: • compression ratios and fuels • compression ratio limits • cylinder head VCR • cylinder block VCR • connecting rod variable compression • piston VCR • cranktrain and linkages VCR • modulated crankshaft eccentricity VCR • axial or barrel engine VCRs • high power and downsized engines • impact of VCR systems on engines • VCR applications for the future • variable compression for future engines and fuels • VCR cost–benefit analysis
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 include: • Holistic reliability across design, manufacturing, supply chain, and marketing. • Advanced experimental designs, including split-plot and fractional factorial methods. • Fault tree analysis (FTA) and FMEA for failure prediction and prevention. • Supply chain optimization through multivariate process control. • Production and field reliability using design for testability and diagnostics. • Electric vehicle system and battery reliability analysis. • Marketing reliability driven by voice of customer and data-based value analysis. This book is an essential resource for engineers, operations leaders, and technical managers who want to build resilient systems, unlock innovation, and achieve long-term competitive advantage through disciplined, data-driven reliability.
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 performance and emissions reduction potential; safety, certification, and market integration; and future projections supported by international initiatives and regulatory frameworks. By combining technical depth with practical relevance, Fundamentals of Sustainable Aviation Fuels serves as both a foundational reference and a forward-looking guide for researchers, policymakers, industry professionals, and students. It underscores that achieving sustainability in aviation depends not only on innovation but also on global collaboration, paving the way toward cleaner skies and a resilient, low-carbon future for air travel.
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 global standards, research, and innovations, this dictionary bridges the gap between theory and application. It fosters interdisciplinary collaboration, supports safer system design, and provides clarity for those engaged in regulatory development or technology deployment. As the pace of mobility innovation accelerates, precise and accessible communication becomes indispensable. SAE International remains committed to advancing global transportation knowledge—empowering professionals to navigate, contribute to, and shape the future of intelligent, connected, and sustainable mobility with confidence and clarity.

Recently Published

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Smart Home–Truck Cabin Temperature Synchronization2026-28-021111/5/2026
Temperature management in both residential environments and commercial vehicle cabins are rapidly evolving toward intelligent, interconnected climate-control ecosystems. However, current systems operate in isolation, with no mechanism for synchronizing thermal comfort preferences between a user’s smart home and the cabin environment of a truck used for either personal mobility, last-mile delivery, or commercial logistics. This disjointed control results in inconsistent comfort experience, unnecessary HVAC energy consumption, and the inability to anticipate user needs based on transition patterns (e.g. leaving a cool home for a hot truck cabin) between home and vehicle. To address this technical challenge, this research paper provides bidirectional communication temperature-synchronization framework in which a smart-home HVAC app communicates seamlessly with the truck’s cabin climate-control system, integrating user-specific comfort profiles(such as temperature preferences, real-time cabin and home temperature, humidity levels, HVAC operating states, outside weather forecasts, trip start times, thermal-soak estimates etc.) and environmental prediction models to maintain consistent thermal comfort across both environments. Based on user’s comfort profile, our framework derives the Comfort Deviation Index (CDI), Transient Temperature Gradient (TTG) and applies predictive algorithms to precondition the cabin or home environment accordingly. The AI technique Long Short-Term Memory (LSTM) network is used for forecasting temperature transitions across home-to-vehicle. Our technique becomes even more pronounced in small-haul trucks that operate under frequent stop-and-go conditions, where rapid cabin temperature fluctuations are common. There is currently no existing research or framework that enables seamless temperature synchronization between a user’s home environment and the truck cabin. A smart-home–to-cabin synchronization approach would fill this gap by intelligently accounting for outside weather, trip duration, thermal soak, and energy-efficiency constraints. Additional challenges include ensuring secure communication over cloud and edge networks, managing latency constraints for real-time actuation, and preventing conflicts between home HVAC automation and automotive HVAC control logic
Prajapati, Saurav, Sagar, Kshitij Singh, B. S., Sanchitha
Numerical Investigation of Transient Heat Dissipation Using Solid-Fin-Enhanced Phase-Change Material in High-Voltage Connectors2026-28-020611/5/2026
High-voltage (HV) cables and connector terminals in electric vehicles (EVs) are susceptible to high current loads. Due to high current, there is excessive temperature rise near the terminals and crimping region inside the connectors. Excessive temperature beyond critical limits may pose risks to connected components and compromise passenger safety. Conventional cooling approaches are often energy-intensive and rely on active thermal management systems, which may not always be suitable for high power applications. Therefore, there is a need for a localized, rapid, and reliable passive thermal management strategy that can be retrofitted into existing connector designs with some structural modifications that would provide significant safety enhancements. This study proposes a novel approach by incorporating phase-change material along with solid fins inside the connector. The PCM has been placed co-axially surrounding the terminals of the connectors and enclosed inside a casing to ensure no direct contact with the connector components. The fins are also enclosed inside the same casing with projections going into the PCM. The PCM acts as heat storage unit, delaying the time for the peak temperature to rise and the fins distribute the heat rapidly inside the PCM. The objective is to maintain the terminal temperature below 100°C with 125 A current rating. A 3-D transient thermal analysis was performed on a CAD model encompassing PCM with solid fins inside the female connector. A comparative analysis between a traditional connector to a fin enhanced PCM connector showcased a drastic delay in temperature rise (acting time) by 115 seconds. Furthermore, the slope of rise in temperature decreased significantly beyond 100°C, slowing the rapid heating of the terminals. Thus, a passive sustainable system is designed for localized thermal management of HV Connectors.
Neogi, Angshuman, Singh, Jitendra, Kumar, Ashutosh
Automotive System Architecture Strategy for Bridging Long-Lifecycle Hardware & Fast-Evolving Software in SDV-AI Era2026-28-021311/5/2026
The Software-Defined Vehicle (SDV) paradigm is reshaping automotive electronic architectures by enabling continuous software rollout & artificial intelligence (AI) evolution on platforms over the total service life of the hardware. This creates a fundamental challenge for electronic control unit (ECU) design: to enable sustained deployment, adoption & maintenance of AI models over vehicle’s lifetime. This paper presents a technology independent framework for integrating AI lifecycle management into SDV ECU architectures, while decoupling AI & software innovation from hardware constraints. It further discusses the role of scalable computing platforms based on high-performance CPUs, GPUs & SoCs, in supporting increasing AI workloads over time. In addition, the evolution of AUTOSAR-based hardware abstraction mechanisms is examined as an enabler for software portability & hardware independence. The discussed framework addresses key lifecycle aspects, including AI model deployment, versioning, rollout strategies & long-term maintainability within established automotive safety, cybersecurity frameworks. The presented work contributes to future-ready SDV systems capable of sustaining continuous AI innovation over extended operational lifetime.
Ghosh, Sayantan, Anudeepika K., Durga, Nagaraju, Yamuna
Application of Machine Learning for the Prediction of Automotive Panel Oil Canning to Accelerate Design Exploration2026-28-018711/5/2026
Oil canning, characterized by localized buckling and surface waviness in thin metal panels, is a critical quality parameter in the automotive industry. Since outer panels serve both structural and aesthetic functions, even minor surface distortions can adversely affect perceived vehicle quality. Traditionally, evaluation of oil canning relies on extensive nonlinear Finite Element simulations. For example, assessing a single hood design variant may requires thousands of nonlinear Finite Element analyses. Considering multiple design iterations across major vehicle panels including hood, roof, doors, fenders, and trunk, the total number of simulations can exceed tens of thousands, leading to substantial computational cost and development time. Consequently, this conventional approach is both time intensive and resource demanding. To address these limitations, advanced Machine Learning techniques are employed to significantly reduce simulation effort as well as accelerate design exploration. Initially, a deep learning based one-dimensional Reduced Order Model is developed to predict load–displacement response and oil canning propensity for a given panel. Indentation points on the hood panel are selected using a space filling Latin Hypercube sampling method. The Machine Learning based one-dimensional Reduced Order Model is trained by using 300-500 indentation points and validated using approximately 40-50 unseen indentation points. The verification is performed on multiple hood geometries. The predicted results demonstrate strong correlation with Finite Element generated load–displacement curves with that of ML based 1-dimensional reduced order model. Furthermore, to enable rapid design exploration across different panel geometries, a dataset of 40 different hood designs is utilized. A geometric deep learning approach is applied to cluster panel geometries, and the resulting representations are used within a 3-dimensional deep neural network tool Ansys SimAI™ to predict oil canning propensity and quantify it using the canning ratio. Validation study is carried out on unseen panel geometries, and the Machine Learning predicted oil canning zones show good agreement with Finite Element identified critical regions. The proposed Machine Learning framework reduces prediction time from hours to minutes, offering a scalable and cost-effective solution for early-stage design evaluation and mitigation of oil canning in automotive panels.
Sarnobat, Rajvardhan Vilasrao, Kumar, Dinesh, Jawale, Akshay, Balaji, Ananya, Kulkarni, Shreya
Development of a Virtual-Physical Correlation Framework for Vehicle Dynamics under Non-Linear Temporal Shifts Using Numerical Digital Twin Testing2026-28-018611/5/2026
The automotive industry is leaning heavily into virtual testing to speed up vehicle design and cut physical prototyping costs. However, checking if a simulation matches a real car still relies on older, rigid math formulae and manual checks. This makes virtual testing less reliable for highly dynamic driving maneuvers, required in vehicle design. To address this, following paper introduces an automated software framework built to accurately measure the differences between physical test data and virtual simulations. The methodology was validated using a lightweight off-road prototype at the National Automotive Test Tracks (NATRAX) on a custom-engineered track inducing high-fidelity roll, pitch, and yaw excitations. To guarantee accuracy, an identical digital twin of this complex topography was constructed within a high-fidelity vehicle-dynamics simulation environment. The system features an algorithmic engine that automatically reads, cleans, and synchronizes distinct physical and virtual telemetry streams. Initial validation using standard metrics like NRMSE, RMSE, and S&G proved insufficient, failing to quantify errors in data exhibiting non-linear temporal shifts caused by stochastic human driver latencies. To overcome these limitations this study applies Windowed Dynamic Time Warping (DTW) to operationalize the ISO/TS 18571:2024 standard. This approach leverages the optimal warping path to extract continuous, vector-based error profiles, isolating phase and amplitude inconsistencies across the entire event. This automated framework significantly reduces correlation time by transitioning from subjective scalar scores to high-resolution diagnostic error vectors. By quantifying dynamic fidelity through this automated, high-precision DTW computational tool, the methodology validates virtual models as robust predictive tools, allowing simulation data to yield results comparable to physical benchmarks. This capability enables faster vehicle development by allowing rapid evaluation of multiple parameters in a virtual environment. It supports performance fine-tuning with reduced physical testing and can be integrated into automated workflows for repeatable data correlation, improving validation efficiency and reducing time.
Umorya, Divyansh, Pithadia, Chirag, S. J., Srihari, Jaiswal, Manish, Hardiya, Karan, Marmat, Hemant
State-Dependent Event Tree Analysis: A Dynamic Methodological Extension for Safety of the Intended Functionality in Automotive Systems2026-28-014011/5/2026
As automotive architectures evolve toward Software Defined Vehicles (SDVs) and high levels of driving automation, traditional safety analysis techniques rooted in hardware reliability require significant adaptation. The Classical Event Tree Analysis (ETA), a cornerstone of ISO 26262 functional safety compliance, relies on static, binary branching logic where each function either fails or succeeds. While highly interpretable and effective for modelling random hardware failures, classical ETA struggles to capture the dynamic, non-linear interactions, temporal degradation, and complex human-machine interface (HMI) dependencies that are characteristic of modern Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. This paper proposes State Dependent Event Tree Analysis (SD-ETA), a methodological extension designed to integrate discrete state vectors and temporal dependencies into the classical ETA framework. SD-ETA assigns probabilities to the branches for the dynamic nonlinear interactions, temporal degradation, and complex human-machine interface (HMI) dependencies instead of binary values, thereby allowing the system state (comprising vehicle physics, environmental conditions, and driver attention metrics) to dynamically update based on the outcomes of the preceding nodes. This approach retains the visual hierarchy and regulatory familiarity of standard event trees while significantly enhancing model fidelity for Safety of the Intended Functionality (SOTIF) scenarios governed by ISO 21448. To demonstrate the necessity and utility of this methodological evolution, this paper presents a comparative case study analysing a system operating under degraded environmental conditions. The quantitative application reveals that while classical ETA predicted a relatively low collision risk by treating sensor performance as a static binary state, the dynamic SD-ETA simulation exposed a critical hidden safety gap, calculating a higher collision probability due to compounded latency and degraded physical operating parameters. Ultimately, SD-ETA equips safety practitioners and automotive regulators with a modernized, time-aware framework to accurately evaluate the complex, interacting risks of autonomous systems without sacrificing the transparency required for rigorous safety certification.
Sharma, Dhiraj Mohan, Mutagi, Megha, Reddy, Kakarla Subba
Smart-ROPS — Machine Learning-Based Rear Occupant Protection System2026-28-014911/5/2026
Children dying in hot cars is not a new problem. Since 1998, more than 1,000 children have died this way in the United States alone — roughly 37 every year. That number has barely been reduced, which tells something alarming: the solutions built so far aren't solving it completely. Door-based reminders are a good example of why. They trigger when a rear door opens, but opening a door doesn't confirm a child was left behind. Single-sensor setups have their own gaps too; no single sensor handles every real-world scenario reliably on its own. Multi-sensor systems get closer, but most use rigid threshold-based rules that fall apart the moment conditions deviate — a sleeping infant barely moves, a child under a blanket reads completely differently on a thermal sensor. This paper proposes Smart-ROPS (Smart Rear Occupant Protection System), an embedded ML architecture that targets these specific failure modes without needing expensive hardware or complex software. Four sensor types feed the system: millimeter-wave radar, CO₂, thermal infrared, and ultrasonic. Data from these passes through three layers — the Perception Layer handles initial sensor fusion, the Processing Layer runs a decision tree as the primary classifier, and when confidence is low, a shallow neural network steps in for ambiguous cases. If a risk is confirmed, the Intervention Layer manages the response: starting with a smartphone alert and cabin ventilation, escalating to emergency services if needed. This is presented as an architectural design contribution — hardware prototyping and real-world testing are left for future work.
Shrimal, Harsh
Overtake Safety Margin Formula Blind Spot Characterization Using a Micro Transformer with Monte Carlo Dropout Uncertainty and CARLA Validation2026-28-014111/5/2026
Overtaking another vehicle on a two-lane road can turn dangerous within a few seconds, especially when the gap to an oncoming vehicle is short or that vehicle is moving fast. Many driver-assistance systems rely on a simple, fast rule called the Overtake Safety Margin formula to decide whether an overtaking maneuver is safe, without running a full traffic simulation. This study checks the formula's reliability using a physics-based simulator that models three vehicles: the car overtaking, the car being overtaken, and the oncoming car. One million generated overtaking scenarios were evaluated using both the OSM formula and the kinematic simulator to identify cases where the formula classified a maneuver as SAFE while the simulator predicted an UNSAFE outcome. These mismatches, called blind spots, represented 3.98% of the evaluated scenarios and were concentrated in high oncoming-speed, short-gap situations, pointing to a specific weakness rather than random error. To catch these blind spots automatically, a small and efficient machine-learning model, a micro-transformer, was trained on the simulator's results. It reached 96.3% overall accuracy and correctly flagged 86.9% of the formula's blind spots, outperforming LightGBM, which detected 73.2%. The model's confidence scores were well calibrated, so they can be trusted to trigger an extra safety check when needed. An uncertainty-based search then used these confidence scores to find risky scenarios efficiently: 187 of the 500 most uncertain cases turned out to be unsafe, about 9.4 times more effective than random selection. Finally, the 30 highest-priority cases were replayed in the CARLA driving simulator, and 27 were confirmed to reproduce unsafe outcomes. Together, these results show that pairing a fast rule-based formula with a calibrated learning-based monitor can catch dangerous situations the formula alone would miss, supporting the safety documentation required for automotive safety-of-the-intended-functionality standards.
Sekaran, Nikhil, Sehgal, Vishal
Scalable Physics-Informed Neural Network for Real-Time Thermal Analysis for Battery Packs2026-28-019811/5/2026
Accurately predicting Thermal and Flow parameters in Battery Pack Thermal Management System is very important to ensure maximum system efficiency, long range and vehicle safety. Traditionally, conventional Computational Fluid Dynamics (CFD) methods are used to get detailed insights into these parameters. But these methods remain computationally very expensive, which limits their application in real-time predictions. This study presents an alternate structured simulation framework that combines CFD fundamentals with Physics-Informed Neural Networks (PINNs) to enable fast and efficient prediction of thermal and flow behavior. PINNs incorporates the governing partial differential equations (PDEs) - mass, momentum and energy – directly into the neural network's loss function, enabling efficient learning with limited dependency on training datasets. In this study, we present a PINN framework trained on limited datasets derived from general CFD simulations of flow and Conjugate Heat Transfer (CHT) cases for a battery pack. The trained PINN model is then validated against a separate set of test datasets obtained from independent studies, across key performance parameters including pressure drop, velocity, temperature distribution, and hotspot location. Comparative analysis indicates that the proposed PINN framework predicts key parameters within 5% deviation from test dataset values. The PINN model also significantly reduces computational time from 5-8 hours per case for traditional detailed CFD to 2-3 seconds. Additionally, the PINN framework requires only a closed-volume CAD geometry and does not rely on 3D-mesh. Limited data can be used to speed up the training process, thereby eliminating the time associated with pre-processing, repeated model setup for different cases, and subsequent corrections. Findings from this study demonstrate PINNs as a potential surrogate modelling tool for real-time thermal monitoring and rapid design iteration in battery cooling systems. This reduces dependency on computationally expensive simulations, thereby reducing development cycles and faster time for customers. This work shows a scalable physics-informed neural-network framework that can be extended beyond battery systems to other components at system and vehicle levels.
Sarkar, Shantanu, Lakshmanan, Yoganathan, A., Raju
Evaluation of Safety Critical Battery Management System Functions Using Hardware-in-the-Loop Testing2026-28-018411/5/2026
Rapid electrification of the automotive industry has significantly increased the functional complexity and safety expectations of Battery Management Systems (BMS) used in electric vehicles. While Model-in-the-Loop (MIL) testing remains a standard practice for early-stage verification of control algorithms, it is inherently limited in its ability to represent real time execution effects and physical hardware interactions. Critical BMS functions, including protection timing validation, sensor interface behavior, contactor actuation logic and fault detection mechanisms are strongly influenced by hardware characteristics such as signal conditioning, analog to digital conversion, execution latency, and nonlinear component behavior, which cannot be accurately captured in a software only environment. This paper presents the application of Hardware-in-the-Loop (HIL) testing as an essential validation methodology for safety critical BMS functions by interfacing the production intent BMS controller with a real time simulated battery environment. The proposed HIL setup enables realistic interaction between embedded software and physical I/O interfaces, allowing systematic evaluation of BMS behavior under normal operating conditions as well as controlled electrical, thermal, and communication fault scenarios. Key validation aspects include protection of response timing, robustness of fault handling logic, and system behavior under transient and abnormal conditions. The study demonstrates that HIL testing effectively bridges the validation gap between MIL simulations and vehicle level testing by enabling early identification of hardware–software integration issues. This approach enhances confidence in functional safety readiness, reduces development risk, and supports compliance with established automotive development workflows. The presented methodology aligns with established automotive development practices and provides a practical framework for robust BMS validation at bench level prior to vehicle level.
Patowary, Suman, Kumar, Aashish, Choudhary, Abhilash, Kumar, Abhishek, Bora, Abhijit
Influence of Transversely-Isotropic Lining Elastic Constants on Brake Squeal in S-Cam Drum Brakes: An Optimization Framework2026-28-016111/5/2026
Brake squeal in heavy-duty S-cam drum brakes remains a persistent NVH challenge, primarily driven by friction-induced modal instabilities. Existing studies on drum brake squeal mitigation largely emphasize geometric modifications, stiffness tuning, or the application of damping treatments, while the influence of directional stiffness characteristics of friction linings remains insufficiently explored. This research presents a novel, material-centric approach by modelling the brake lining as a transversely isotropic material, characterized by six independent elastic constants, and systematically quantifying their influence on squeal-prone unstable modes using Complex Eigenvalue Analysis (CEA). A validated finite-element model of a production S-cam drum brake assembly is developed and correlated through experimental modal analysis, ensuring close agreement between predicted and measured natural frequencies and mode shapes. The validated model is then integrated with a Design of Experiments (DOE) within commercially available FEA software to quantify both main effects and higher-order interactions across practical ranges of in-plane and out-of-plane elastic moduli and shear constants. To enhance the predictive accuracy of the surrogate models, a subsequent sampling–fit study is conducted, significantly improving the coefficient of determination (R2) of the response surfaces. The refined surrogate models are employed within a Global Response Surface Methodology (GRSM) framework to perform multi-parameter optimization aimed at minimizing squeal propensity under representative operating conditions. Finally, complex eigenvalue analysis quantifies the impact of the optimized transversely isotropic property combinations on modal stability, establishing a predictive, optimization-driven methodology for robust, noise-free drum brake design. The study culminates in the identification of an optimized transversely isotropic elastic-constants signature for the friction lining, providing actionable targets for friction material formulation and reinforcement alignment. Unlike prior research focused on isotropic material assumptions or component-level design changes, this first-of-its-kind methodology advances the state of the art by enabling friction material– centric NVH optimization for heavy-duty drum brake systems.
Philip, Rony, Muralidharan, Sudharsan, Mohanam, Gopalakrishnan
Enhancing FEA Preprocessing Efficiency through a Multi AI Model Strategy2026-28-017411/5/2026
Finite Element Analysis (FEA) plays a vital role in accelerating product development and reducing overall engineering costs. Accurate simulation of powertrain components requires extensive preprocessing of solid models. Detailed contact definitions and bolt representations are required to properly evaluate screw joints. Pipe joints present similar challenges, as they often incorporate various types of nuts and connectors. As a result, engineers spend a significant amount of time manually modelling these joints. Automating the modelling process is difficult due to the high variability in bolt sizes and pipe joint designs. This paper proposes AI-based models to automatically classify these components and extract their distinctive geometric features. Because bolts and pipe joints have wide feature variations, developing a generalized AI model requires a large dataset which is often hard to obtain. To address this limitation, this paper explores strategies for generating smaller, specialized AI models tailored to each unique joint type. This study describes the development of deep learning neural network models capable of learning distinguishing features of different joint configurations from limited training data. A multilayer graphical neural network architecture is employed, enabling efficient extraction and representation of these unique geometric characteristics. The proposed models can robustly generalize different joint types while maintaining high classification accuracy and reliable feature detection despite constrained training data availability. Automated workflows have been implemented in commercially available preprocessing tools to validate the proposed AI methodology and demonstrate its effectiveness in practical engineering applications. The integration of AI model automation in preprocessing tools significantly reduces engineering effort and repetitive manual preprocessing steps. This work highlights the potential of hybrid AI models to accelerate CAE workflows and provides scalable framework for future automation in FEA.
Jayachandran, Janarthanan, Agarwal, Deepak
An Optimization Framework for Dynamic Vehicle and Driver Fleet Allocation2026-28-014211/5/2026
EV fleets in the EU/US region are owned by a fleet manager who manages the entire fleet operation. Currently, the telematics ECU transmits a significant amount of information that can be used to our advantage for vehicle–route assignment. The beneficiaries of this solution would be the fleet managers, who will receive an optimized vehicle–route plan, and the drivers, who will be able to see their entire week’s schedule. This can be achieved using models and applications in operations research, which take in EV vehicle parameters and provide an optimal solution. Existing fleet and vehicle control strategies typically rely on fixed actuation limits or rule-based logic that are configured without direct awareness of fleet-level objectives or task-specific requirements. As a result, vehicles may operate inefficiently, experience unnecessary energy consumption, or incur accelerated component aging. There is therefore a need for a system in which fleet and task information directly influence vehicle actuation in a dynamic and coordinated manner. This approach enables fleet operators to reduce energy cost, battery degradation, and unplanned downtime by dynamically aligning vehicle actuation with actual task and fleet needs rather than static calibrations. It improves fleet utilization and scalability by allowing vehicles to automatically adapt their energy and power behavior as operational conditions change. As electrified and mixed fleets grow and complexity increases, such optimization-driven actuation becomes critical for achieving cost-efficient, reliable, and sustainable fleet operations. The scheduling is modelled in three distinct layers that is vehicle-task scheduler, charging scheduler, human driver scheduler, thereby ensuring a clean modular design.
Anavekar, Abhishek Anil, Bhowmick, Saikat
Thickness Optimization of Constrained Layer Damping Shims for Reducing Friction-Induced Instabilities in Drum Brakes2026-28-016011/5/2026
Commercial vehicles predominantly employ drum brakes because of their low cost, robustness, and simple construction. However, with the increasing push toward quieter powertrains, particularly in electric commercial vehicles, brake squeal has become a dominant noise source. The inherent complex and chaotic nature of brake squeal makes its prediction and suppression especially challenging. Traditional squeal mitigation strategies, such as modifying component mass and stiffness or using materials with higher native damping (e.g., cast iron), are inherently constrained in their capacity to mitigate instability. In such scenarios, constrained layer damping (CLD) shims have proven to be an additional, effective and practical solution in disc brakes, which is motivating growing interest in extending similar concepts to drum brake applications. A key challenge in adopting shims for drum brakes lies in identifying and achieving the required damping level necessary to suppress squeal while adhering to stringent packaging, manufacturability, and cost constraints of foundation brakes. Establishing a clear relationship between squeal propensity, noise intensity, and target damping is therefore essential. While past CLD studies discuss layer-thickness effects in generic structures and caliper brakes, no work applies thickness optimization to brake shims in a drum brake assembly. This paper presents a systematic and target-driven framework for the design of CLD shims for drum brake applications. The approach initially defines the damping requirement for squeal suppression using system noise metrics and dynamic stability evaluations. Based on this requirement, a design optimization approach is employed to determine the appropriate thickness distribution of the individual shim layers, while accounting for practical constraints such as packaging limitations, material feasibility, and manufacturing considerations inherent to foundation brake systems. The optimized CLD shim configurations obtained through this process are subsequently integrated into the drum brake assembly and evaluated using complex eigenvalue analysis. The simulations utilized a validated FE model of the S-cam drum brake assembly, whose accuracy was established through experimental modal analysis–based correlation. The results indicate that the proposed target-based shim design methodology leads to a measurable improvement in modal damping and system stability, thereby demonstrating its effectiveness as a practical and robust approach for brake squeal mitigation in drum brake systems.
Muralidharan, Sudharsan, Philip, Rony, Mohanam, Gopalakrishnan
Hitch Position Sensitivity on Axle Loads and Braking Performance in Tractor-Trailers2026-28-015611/5/2026
The longitudinal dynamics during braking of articulated heavy vehicles is primarily governed by coupled load transfer mechanisms, yet the isolated influence of hitch location on axle load distribution and braking response remains insufficiently quantified. This study investigates the sensitivity of hitch height variation of a twin-axle tractor with a single-axle on the axle loads and consequently braking performance. A rigid-body longitudinal dynamics model is utilised and evaluated against a high-fidelity multibody simulation across a structured test matrix spanning friction levels and trailer loading conditions. Results show that hitch location primarily affects braking behaviour through redistribution of axle normal loads via hitch-induced pitching moments. A 400~mm increase in hitch height produces up to 5% variation in drive axle load in the unladen case, rising to approximately 16% under laden conditions. Longitudinal hitch variation yields comparable sensitivity in drive axle load, with increased influence on steer axle loading, while trailer axle load remains largely unaffected. Despite these variations, the overall braking metrics such as stopping distance and steady-state deceleration exhibit limited sensitivity to hitch location under most operating conditions. However, near friction limits, hitch-induced load redistribution significantly alters axle-wise friction utilisation and wheel lock sequence, steer axle-first at low deceleration and rear-axle-first at higher deceleration. The results demonstrate that while hitch location has a secondary effect on overall braking efficiency, it plays a critical role in stability-relevant behaviour mainly in the laden vehicle configurations. These findings highlight the importance of accounting for hitch-induced coupling in load-aware braking strategies for articulated vehicles.
Devadiga, Shravan S., Mandal, Indra, Gaurkar, Pavel, Vivekanandan, Gunasekaran, Sivaram, Sriram, Shankar Ram, C. S.
Software Optimized Multicore ECU Architecture for Next Gen Vehicles2026-28-014411/5/2026
Modern vehicles increasingly rely on software-intensive functions, which continue to drive the growth and complexity of Electronic Control Units (ECUs). Conventional architectures based on single core or statically partitioned multicore microcontrollers are becoming inadequate for meeting emerging requirements such as deterministic real time performance, high communication throughput, synchronized time bases, and secure message handling. This paper introduces a software driven multicore consolidation approach that enables multiple ECU functions to be integrated on a single high-performance microcontroller while maintaining essential attributes of safety, determinism, and responsiveness. The proposed architecture emphasizes the co-location of dependent software modules to reduce cross core communication overhead and enhance runtime efficiency. By leveraging AUTOSAR compliant multicore Basic Software (BSW) stacks, including Communication, NVM, STBM, Diagnostics, and OTA, the system supports improvements in timing accuracy, data throughput, and task synchronization. In addition, core-specific communication channel assignment, interrupt-based routing, HSM supported secure frame handling, and multicore enabled NVM with a master–satellite design collectively contribute to more efficient CPU utilization and reduced latency under high load communication scenarios. The approach supports key industry relevant capabilities (e.g., runtime remote software updates, sensor fusion) and enables efficient handling of high-volume communication activity, making it adaptable for emerging multicore ECU architectures in next generation vehicles. The theoretical analysis presented in this paper describes how the proposed software driven consolidation can reduce system overheads, improve the organization of multicore workloads, and support more structured parallel execution. The study further highlights that a well-designed multicore software strategy can serve as a practical and scalable direction for future automotive electronics, supporting higher levels of feature integration and communication flexibility, along with lower CPU load compared to traditional multicore ECU implementations. Considerations for future enhancements in software component grouping and communication stack adaptability are also discussed.
Sadhu, Santosh, Toshniwal, Sachin
Advanced Semiconductor Derating Methodology for Electronic Circuits and Validation2026-28-017211/5/2026
In current and futuristic electronic designs, ensuring the accuracy and reliability of electrical circuits over all component parameter variation and operating condition, requires validation across multiple domains - mathematical modelling, simulation, and physical measurements. Before committing to physical board fabrication, simulation has become a critical step in the circuit design process. With increasing design complexity, engineers rely heavily upon simulation tools to predict circuit behavior, optimize performance, and reduce the number of design iterations. The effectiveness of simulation lies not only in its ability to provide accurate representations of real-world circuit behavior, but also capturing key electrical characteristics such as voltage levels, frequency response, accurate modeling of component tolerances and coefficients, etc. It should also excel in efficiency-allowing rapid iteration, testing under varied conditions, evaluation of multiple scenarios without excessive computational costs, and quick comparisons of design alternatives. This paper presents an advanced semiconductor derating methodology that addresses the limitations of conventional approaches through automation. The derating algorithm has been developed and implemented using Maple (Mathematics based Tool) to generate precise worst-case values of semiconductor component parameters adjusted for specific circuit operating conditions. This automated tool significantly enhances both the efficiency and accuracy of mathematical analysis results, providing engineers with more reliable performance predictions for electronic circuit design. This paper also presents a comparative study that analyzes and correlates the performance of an electrical circuit obtained through worst-case mathematical analysis using internal tool, simulations using industry-standard tools, and the result collected during internal testing of the product. The objective is to evaluate how closely theoretical predictions and simulations align with real-world behavior and to identify factors contributing to any discrepancies.
Mishra, Anshul, M. S., Suhas
Simulation Based Approach to the Verification and Validation of Camera Based Autonomous Emergency Braking Systems for Commercial Vehicles2026-28-014811/5/2026
Autonomous Emergency Braking (AEB) is a safety-critical Advanced Driver Assistance System now mandated for commercial vehicles in India.to reduce crash frequency and severity. This paper presents a structured, simulation-based V&V approach for camera-based AEB that integrates TTC braking evaluation, Forward Collision Warning assessment, and real-time object detection validation. High-fidelity tools Hexagon VTD & ROD, and MATLAB Roadrunner are employed to generate photorealistic, India-specific scenarios capable of modelling detailed road geometry, marked and unmarked lanes, mixed traffic behavior, and diverse environmental and weather conditions. Scenario-driven testing enables frame-accurate, end-to-end assessment of detection, decision-making, and actuation without requiring access to internal algorithms (black-box validation). Across representative scenarios, the test campaign achieved up to 90% accuracy in detecting relevant targets. This validation aligns with AIS & MoRTH guidance, and adheres to UN Regulation (UN R131), while incorporating applicable ISO 26262 functional safety principles. Requirement traceability matrices are used to evaluate system behavior under sensor degradation, misclassification, and other critical edge-case conditions prevalent in Indian driving environments. The proposed methodology enables consistent, repeatable, and regulation aligned validation of camera based AEB systems, supporting safer and more reliable commercial vehicle deployment in India.
S., Nagabhushana N., P., Gururaaj, M. L., Sankar
NV Enhancement of Automotive Powertrain through Hardware Refinement and ECU Parameters Optimization While Preserving Performance and Emissions Compliance2026-28-016311/5/2026
To comply with increasingly stringent emission norms and simultaneously enhance engine efficiency, durability, and power output, continuous improvements in engine performance have become essential for modern automotive engineering. However, these advancements often result in unintended consequences, particularly in the form of elevated noise and vibration (NV) levels, which negatively impact overall vehicle refinement and customer comfort. Managing NV without compromising performance has therefore emerged as a critical challenge in contemporary powertrain development. The study addresses the optimization of NV characteristics while maintaining engine functionality through a hybrid approach. This method combines transfer path of noise and vibration with a detailed evaluation of engine electronic control unit (ECU) parameters. By combining these analytical techniques, critical contributors to NV were identified, enabling the formulation of a targeted countermeasure strategy. Experimental validation was carried out on the engine intake system under controlled conditions to assess the effectiveness of proposed solutions. Results indicate that modifications to the intake system significantly reduce noise levels at the engine intake side, thereby improving acoustic comfort without adversely affecting combustion efficiency or power delivery. Furthermore, systematic optimization of ECU parameters contributed to smoother engine operation and reduced vibrational excitation across multiple operating points. The findings demonstrate that accurately targeting the appropriate engine subsystem and employing an integrated analytical-experimental framework can achieve balanced performance and acoustic refinement. This work highlights the effectiveness of integrated analytical and experimental methods for achieving balanced performance and acoustic refinement in advanced engines.
Singh, Karamdeep, Uniyal, Ravinder, Yadav, Manish, Mishra, Durga Prasad
Enhancing SDV Architecture by Rethinking In-Vehicle Connectivity2026-28-021011/5/2026
The rapid evolution of automotive electronics has pushed traditional vehicle electrical and electronic (E/E) architectures to their limits. Conventional designs, characterized by point-to-point wiring harnesses, distributed domain-specific ECUs, and communication buses such as LIN, CAN, and Ethernet, impose significant drawbacks in terms of vehicle weight, integration complexity, and long-term scalability. As the industry moves toward the Software-Defined Vehicle (SDV) paradigm, there is a compelling need to rethink the foundational principles governing in-vehicle connectivity. This paper proposes a wireless-centric SDV architecture in which non-functional-safety-relevant sensors and actuators communicate over standardized wireless protocols, eliminating the need for their associated physical wiring. By decoupling these peripheral nodes from the wired backbone, the proposed architecture reduces harness bulk, lowers vehicle mass, minimizes redundant compute hardware, and significantly reduces integration cost. Safety-critical systems including those governing powertrain, braking, and steering are explicitly retained on hardwired, deterministic communication channels in compliance with ISO 26262 functional safety requirements. This paper additionally defines explicit assumptions and fallback behavior governing that wired/wireless boundary. The architectural framework is evaluated against key SDV design objectives such as modularity, over-the-air (OTA) updateability, and feature scalability, assessed qualitatively against published engineering precedent, with a structured validation roadmap defined to quantify latency, reliability, and coexistence performance in follow-on work. The analysis demonstrates that a hybrid wired-wireless topology can substantially simplify vehicle E/E architecture while maintaining the reliability and latency guarantees demanded by automotive-grade systems. This work contributes to a structured transition roadmap for OEMs and Tier-1 suppliers seeking to adopt SDV principles without compromising vehicle safety or regulatory compliance, together with an explicit protocol-selection framework and a functional-safety argument for the wired/wireless boundary.
Hebbale Ramkumar, Ramya, Sarkar, Dibas
A System and Method for Software Computation in a Software Defined Vehicle2026-28-021411/5/2026
Modern day vehicles are equipped with multiple features to enhance user experience, increased safety and continuous over the air updates for upgradability and updatability of vehicles over lifetime. The implementation of every embedded automotive software feature consumes microcontroller resources (like RAM and ROM) which are highly critical and need careful handling. In an SDV architecture, where continuous software upgrades and updates are enabled, optimal feature handling becomes necessary. The microcontroller resources chosen during the development phase often reaches the threshold limit even before series production. In such scenarios, software feature enhancement/updates are challenging. When the memory is limited and becomes a constraint for installing newer features / upgrades, OEM has minimal provision to enhance or introduce new features. The paper presents framework to OEM to get a summary of how to manage functionality in a vehicle variant depending on specific demographic location and user preferences. The summary of the functionality usage is reported to end user in the vehicle system.
Chandrashekar, Sandhya, Hubli, Ashwini
An Agent Driven Approach for Test Specification Generation for Software Defined Vehicle Features2026-28-021211/5/2026
Automotive industry is currently experiencing a major transition towards Software-Defined Vehicles (SDV) to facilitate seamless new feature integration via over the air (OTA) update. SDVs integrate more than 50 + ECUs with more than 400+ software functions, leading to significant software complexity and a substantial need for comprehensive bench level testing to prevent software issues at vehicle level. To address these challenges, a robust and edge case focused test specification is essential, ensuring maximum requirement coverage across all scenarios while incorporating insights from past bugs and field issues. Currently, the automotive industry faces challenges in producing high-quality test specifications mainly focusing on functional testing (both positive and negative scenario), fault injection testing, ISO standards testing, network management testing which often exceed 2,000 + test cases for SDV features in parallel with maintaining consistency over time, ensuring maximum system-requirement coverage, and meeting project milestones. This paper introduces a new method to overcome above said problems. We provide a comparative study involving our proposed method with other methods to show the efficacy of the proposed method.
Arumugam, Jayalakshmi
A Surrogate Modeling Approach for the Weight Optimization of a Two Wheeler Chassis with PSD Based Durability Validation2026-28-020711/5/2026
As the global automotive industry pivots toward sustainable mobility, reducing vehicle curb weight remains a paramount objective for enhancing energy efficiency and range. This paper presents a robust and comprehensive methodology for the light weighting of a two-wheeler chassis through the integration of Design of Experiments based optimization and Power Spectral Density durability analysis. The study begins by establishing a baseline durability profile for the vehicle using frequency-domain durability simulations under random vibrational loading, ensuring a benchmark for structural integrity. To explore the design space effectively, frame tube thicknesses are parameterized, and a Design of Experiments design matrix is constructed using Modified Extensible Lattice Sequence technique. This design matrix is solved, and a response matrix is built. These two matrices are combined, and appropriate curve-fitting techniques are applied to construct a Reduced Order Model. This mathematical surrogate model enables the rapid evaluation of the frame’s response under varying frame thickness combinations, thereby avoiding computationally expensive traditional Finite Element Analysis iterations. The optimum combination of frame tube thicknesses is identified using the Global Response Surface Method. This configuration minimizes the overall mass of the vehicle while strictly maintaining predefined durability and safety targets. The final optimized frame is evaluated by a secondary Power Spectral Density durability analysis to validate the accuracy of the surrogate model’s predictions. The integration of this metamodel-based design approach results in a significant reduction in structural weight. Ultimately, this framework accelerates the design cycle by developing a lightweight, cost-effective, and durable structure, thereby minimizing the overall time-to-market for sustainable automotive products.
Chettiar, Alfred Selvamani, V., Vinothkumar, S., Deepak, Praveen, Kannan
A Physics Artificial Intelligence Framework for Durability Analysis of Motorcycle Brake Pedal2026-28-018811/5/2026
The brake pedal assembly is a critical load bearing automotive component that must perform reliably under severe service conditions, especially during emergency braking events. Structural assessment is commonly carried out using computationally intensive non-linear finite element analysis, which can limit rapid design iterations during early product development. This study presents a physics informed surrogate modelling framework for predicting displacement and von Mises stress responses of a brake pedal directly from unstructured three dimensional mesh geometry. A structured dataset was created through parametric morphing and a Design of Experiments approach by varying pedal arm angle, pedal moment length, flange/web thickness, and combined geometric conditions. The simulation database was developed using conventional solvers and the generated cases were used to train Physics Artificial Intelligence based on the Transformer Neural Simulator architecture. The trained model showed close agreement with conventional solver results for both peak response values and contour distributions. For representative testing cases, predicted displacement and stress responses remained within acceptable engineering tolerance compared with the finite element baseline. After training, the PhysicsAI workflow reduced the prediction turnaround time compared with the conventional solver workflow. The results suggest that Design of Experiments driven datasets combined with geometric deep learning can provide an efficient workflow for rapid brake pedal structural assessment during concept development and iterative design studies.
N., Rengarajan, Praveen, Kannan, Philip Thomas, Febin
Option for Introducing Vehicle Crashlytics with SOVD2026-28-021511/5/2026
Automotive E/E architecture is getting transition from distributed ECUs to high performing Zonal architectures. Traditional diagnostic methodologies based on Unified Diagnostic Services (UDS/ISO 14229) is being followed for providing failure analysis. It is difficult when debugging complex software and to find issues where lies exactly at modular level called “stack trace”. This could provide solutions for software bugs, failures or even container-level crashes in Software-Defined Vehicle (SDV). This paper provides a remote diagnostic framework that makes use of the ISO 17978 (SOVD) standard to visualize a “Vehicle-wide Crashlytics” as analogous to modern mobile application analytics with Firebase SDK. Also, the comparison of failure management with traditional diagnostic method to handling fault management in remote framework with both ISO 17978 (SOVD) and ISO 20077/20078 (Remote Diagnostic) is made. It also speaks how methodology can be formed for software logs capturing and traces execution triggered by function software level anomalies. Also, the constrains such as V2X bandwidth and data is monitored and controlled with Diagnostic Saliency Algorithm that helps to determine only relevant telemetry is transmitted via 5G or V2N. This framework proposal is checked with a simple application failure scenario, which leads to the reduction in “Mean Time to Repair” (MTTR) for software bugs. The reduction in MTTR is achieved since actionable root cause of software issue is shared directly from vehicle’s run-time environment. Future development of vehicle functions (SaaS) is expected to be integrated with SOVD and cloud-native observability patterns. This is much needed towards the scalable and quick development and deployment of applications into Vehicle as Digital Hub.
Kalirajan, Vignesh, Annamalai, Viswachandar, Balakrishnan, Arun Shankar