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The purpose of this document is to expressly describe the method of calculating state of charge (SOC) related to the on-road traction battery management systems (BMS). This document will attempt to clearly educate and explain four key areas of BMS SOC: (1) basic SOC definition, (2) SOC calculation methods, (3) influence items for SOC, and (4) SOC warnings.
The purpose of this document is to expressly describe the method of calculating state of health (SOH) related to the on-road traction battery management systems (BMS). This document will attempt to clearly educate and explain four key areas of BMS SOH: (1) basic SOH definition, (2) SOH calculation methods, (3) influence items for SOH, and (4) SOH reporting.
This SAE Aerospace Standard (AS) establishes guidelines for enhancement to IEEE 1394-2008 Beta (formerly IEEE 1394b) PHYsical (PHY) layer. It encompasses enhancements to the IEEE 1394-2008 Beta PHY to reduce port connection times and increase port connection reliability. Therefore, this document contains extensions/restrictions to “off-the-shelf” IEEE 1394 standards and assumes that the reader already has a working knowledge of IEEE 1394. The enhancements covered in this document include: Detect loss of descrambler synchronization Fast-ReTrain (FRT) Fast Power-on Re-connect (FPR) Fast Connection Tone Debounce (FTD) Programmable invalidCount Bus Reset Cause This document does not identify specific environmental requirements (electromagnetic compatibility, temperature, vibration, etc.); such requirements will be vehicle-specific and even LRU-specific. One should refer to the appropriate sections of MIL-STD-461E for their particular LRU and utilize handbooks such as MIL-HDBK-454A and MIL-HDBK-5400 for guidance. This document is referred to as a “slash sheet” and accompanies the AS5643B base standard.
We present atomistic molecular dynamics simulations of linear and branched fluorinated polymer chains, with and without imidazole functionalization, designed as a model for solvent-free proton-conducting membranes for high-temperature fuel cell applications. Simulations conducted over a temperature range of 300 to 550 K, and diffusion-based relaxation times reveal that glass transition temperatures depend on polymer architecture: the branched and functionalized systems exhibit transitions near 400 K compared to 500 K for the linear chain. Fluorine-fluorine radial distribution functions demonstrate that branching disrupts local packing order, while imidazole substitution creates specific intra-chain interactions. Radius-of-gyration distributions reveal that branching significantly increases chain extension and creates multiple conformational families, whereas imidazole groups stabilize compact conformations at low temperatures that transition to more diverse states upon heating. These findings provide molecular-scale insight into how polymer architecture controls thermal transitions and structural organization in model high-temperature polymer electrolyte membranes.
Ground combat vehicles traditionally remain in service for decades, yet their rigid architectures make them costly to upgrade and slow to adapt to evolving threats. While the Department of War's 2025 Modular Open Systems Approach (MOSA) mandate aims to address this challenge, implementation barriers persist inconsistent vendor interpretations, physical and logical interoperability gaps, and IP complexities hinder progress. This paper proposes a reformed MOSA framework for ground vehicle Portfolio Acquisition Executives that redefines the government's role from system architect to ecosystem governor. The framework comprises four pillars: tiered standards balancing mandatory physical integration with vendor innovation, digital validation pipelines accelerating compliance verification, dynamic IP rights preventing vendor lock-in, and strategic portfolio management aligning investments with ground vehicle capability priorities. Special emphasis addresses integrating AI capabilities. This reformed approach enables rapid fielding of advanced ground vehicle capabilities at commercial innovation speed.
Ground Vehicle Systems Center (GVSC) conducted a Soldier Touch Point (STP) in the field comparing the use of Vitreous User Interface (UI) on a Helmet Mounted Displays (HMD) to a Soldier Machine Interface (SMI) on Vehicle Mounted Displays (VMD). Soldiers drove a Stryker Vehicle equipped with 360-degree indirect vision through a series of mobility obstacles at Camp Grayling. No significant differences were found in Soldier performance between the UI conditions, however, a significant performed better maneuvering through obstacles on the right-hand side of the vehicle in comparison to the left. This is a continuation of simulation only work previously presented at GVSETs 2023.
Model-Based Systems Engineering (MBSE) has become a mandated practice for Department of Defense acquisition programs, yet measured benefits remain elusive. The 2024 Defense Science Board found that less than one percent of published literature actually quantified MBSE outcomes, and flagship ground vehicle programs such as the XM30 Infantry Fighting Vehicle have experienced schedule delays attributed directly to insufficient proficiency with model-based approaches. This paper presents the Digital Safety Twin concept: an AI-powered safety intelligence architecture that addresses three of the most labor-intensive and error-prone MBSE workflows. First, the architecture uses hybrid natural language processing and large language model (NLP/LLM) pipelines to auto-formalize unstructured natural language documents into formally structured, traceable requirements. Second, it auto-generates and continuously maintains traceability relationships across requirements, design elements, hazard analyses, and verification artifacts. Third, it provides continuous safety case completeness and confidence assessment through automated Goal Structuring Notation (GSN) synthesis connected to live evidence sources. The approach is grounded in Systems-Theoretic Process Analysis (STPA), the OMG Risk Analysis and Assessment Modeling Language (RAAML), MIL-STD-882E system safety practice, and the UL 4600 safety case framework. We present the methodology, its alignment to the DoD Digital Engineering Strategy, and its applicability to ground vehicle autonomy programs including next-generation infantry fighting vehicles and robotic combat vehicles. We also discuss the limitations, risks, and cultural barriers that must be addressed for AI-augmented safety engineering to achieve acceptance in mission-critical defense applications.
The impending formal adoption of SAE J1939-91C creates an urgent need for rigorous, repeatable validation methods that extend beyond functional conformance. This paper presents a structured validation and benchmarking framework, with a focus on performance characterization across dynamic vehicle configurations. Building on prior work in secure network formation, rekeying, and Golden Tester concepts, we define a minimal, transport-agnostic set of cryptographic and protocol test vectors for deterministic validation of secure message authentication, alongside simulation-based methods for evaluating network formation and rekey behavior. The framework integrates performance metrics such as secure message latency, rekey time and throughput, while also introducing cybersecurity-specific diagnostics and logging requirements for gateway module implementation. Security validation scenarios are mapped to explicit detection and response benchmarks. The resulting methodology provides OEMs, Tier-1 suppliers, and research organizations with a practical, reproducible approach to validating J1939-91C implementations, supporting both development-phase evaluation and ongoing lifecycle assurance.
This paper details the successful scaling demonstration of a comprehensive supply chain screening process for commercial off-the-shelf (COTS) motherboard subassemblies used in tactical servers for naval applications. Our approach leverages Power Fingerprinting (PFP) technology, which uses unintended analog emissions and machine learning to provide independent, non-destructive, and scalable integrity assessment of microelectronics. The primary goal of the effort was to demonstrate the effectiveness and scalability of the PFP screening process without disrupting or delaying the manufacturing workflow. The screening successfully detected hardware and firmware modifications and identified two cases of abnormal behavior: unusual BIOS power reset and elevated CPU sensor readings on two motherboard subassemblies. Following our quality control forensic analysis, we determined the root cause of these anomalies and their potential impact on the host platform.
This paper presents an efficiently structured and integrated Reliability and Maintainability (R&M) process for implementation in a Digital Engineering (DE) environment during the product design phase, in support of the Army Transformation Initiative. This process links key R&M tasks to influence their execution and clarifies the relationship between a system's design risk and its R&M performance. The significant contributions of this process are: 1) establishing intra- and inter-task linkages for R&M activities within a comprehensive, systemic design process; 2) defining the direct impact on component and system-level R&M as a function of strategic design risk mitigation activities through a central Design Failure Modes and Effects Analysis (DFMEA); and 3) executing R&M tasks within a fully integrated, closed-loop R&M modeling and risk assessment approach, supported by a DE and AI-enabled environment.
Ground vehicle autonomy increasingly depends on human-on-the-loop (HOTL) supervision, yet supervisors are often overloaded by visual interfaces that can obscure emerging risks. This paper presents an AI-driven predictive sonification architecture that converts short-horizon forecasts of platoon behavior into structured auditory cues for supervisory monitoring. A forecasting engine predicts future vehicle interaction states and evaluates predicted and active violations to generate a composite risk indicator. When risk exceeds defined thresholds, a sonification module conveys risk magnitude and trajectory through changes in pitch, loudness, modulation, and spatial panning. The paper describes the system architecture, sonification design, operational use cases, and a planned human-subject evaluation. The proposed framework is intended to improve early awareness of emerging instability and support more timely supervisory intervention.
Unmanned Aerial Systems (UAS) pose a growing threat on the modern battlefield, demanding rapid detection and characterization capabilities for the warfighter. Existing single-model solutions are inadequate for Counter-UAS (C-UAS), as they struggle across varying ranges and cannot provide detailed contextual information beyond bounding boxes. We present ZEUS (Zero-shot Explainable Universal Segmentation), a multi-model detection and recognition system that integrates several machine learning approaches. ZEUS employs a high-performance UAS detector trained on synthetic, internally collected, and open-source datasets, with real-time capability demonstrated on edge hardware across both electro-optical and infrared modalities. For classification, ZEUS uses a zero-shot approach: detected UAS are segmented and compared against a library of 3D reference models rendered at various poses, enabling identification of new UAS types without retraining. This methodology additionally provides UAS pose and range estimates critical for threat assessment and engagement decisions.
This paper details the development of an intelligence and inspection platform consisting of an attritable sub-250g UAV, a ground control station, and a visualization interface for users. The UAV architecture combines onboard obstacle detection and avoidance along with simultaneous localization and mapping to have full autonomous navigation inside of complicated GPS-denied environments. The ROS 2-to-Unreal Engine data pipeline allows for sensor fusion, data cleansing, and initial analysis as well as creation of a high-fidelity real-time 3D digital twin. The visualization interface allows users to easily identify critical features and turn data into intelligence to support decision making by soldiers and first responders.
Shrike Nano provides forward observers and small unmanned aerial system (sUAS) operators with an integrated solution to enhance target prosecution using sUAS video feeds and indirect fire systems. Operable within the Android Tactical Assault Kit (ATAK) ecosystem, Shrike Nano functions as a software plugin that interacts seamlessly with existing tools, including UAS Tool, Robot Picker, and Network Monitor. By utilizing either aided threat recognition (AiTR) or manual targeting workflows, along with passive single-camera geolocation, operators can nominate targets and correct shot placement via digital messaging to enterprise fires terminals such as the Advanced Field Artillery Tactical Data System (AFATDS). The system offers key advantages, including operator standoff capabilities, accurate geolocation, and streamlined fires messaging workflows, all while leveraging low-observable platforms. Shrike Nano seeks to bridge gaps in traditional targeting processes by providing a cohesive and efficient sensor-to-shooter workflow that reduces cognitive load and enables faster, more reliable fire missions at the tactical edge.
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Defense acquisition often struggles to match the pace of private investment, slowing the transition of mature commercial technologies into military use. This paper examines how aligning government acquisition with venture-oriented business models can increase industry participation, accelerate fielding, and reduce government program office risk. Using autonomous construction as a case study, it highlights how commercial investment has advanced autonomy while traditional procurement limits adoption. The paper outlines approaches such as non-traditional partnerships, phased acquisition, and performance-linked revenue structures to improve flexibility, leverage private capital, and expand the Defense Industrial Base while speeding operational capability delivery. Citation: Mazzara, M., San Nicolas, A., Gadea, J., Himmel, M., Kruger, J., Gill, C., & Simon, A., Soylemezoglu, A., Netchaev, A., Nottage, D., Klein, J. “Mobilizing Innovation: Venture Capital Alignment for Defense with Autonomous Construction Case Study” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA Michigan Chapter, Novi, MI, August 11–13, 2026.
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