Browse Topic: Architecture

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Development of a Diesel Combustion System for Next‑Generation Heavy‑Duty Engines Using a Synergistic Analysis‑and‑Testing Approach2026-37-0004To be published on 06/09/2026
Regulators and policymakers have introduced increasingly stringent limits on tailpipe CO₂ and pollutant emissions to accelerate the decarbonization of heavy-duty vehicle applications. The development of innovative propulsion technologies — such as advanced combustion systems, low-friction reciprocating components, and improved aftertreatment solutions — combined with hybridization and the adoption of alternative fuels (e.g., biogas, HVO, green hydrogen), represents a key pathway for meeting future emission and GHG targets. In this study, advanced combustion systems were developed for a 13‑liter diesel engine for heavy-duty truck applications, with the objective of complying with forthcoming Euro VII regulations while maximizing thermal efficiency. The combustion system architecture—including open-bowl geometry with high aspect ratio, injector nozzle with wider spray opening angle, and reduced swirl ratio—was optimized using a Machine Learning–algorithm trained on high-fidelity 3D CFD
Belgiorno, Giacomo, Centini, Maria Pia, Pezza, Vincenzo, Cozza, Ivan F., Pesce, Francesco C., Vassallo, Alberto, Colombo, Giovanni, Gallo, Alessandro, Mirzaeian, Mohsen, Borg, Jonathan
Passenger Car Powertrain for Voltages above 800V2026-37-0020To be published on 06/09/2026
The EU-funded innovation project High-Voltage fast-charging Efficient electric vehicle Powertrains (HiVEP) develops new technologies for mass-market electric vehicles (EVs) by advancing architectures operating above 800 V. These architectures integrate silicon carbide (SiC)-based power electronics, rare-earth-free electric machines with active winding reconfiguration, high C-rate batteries, and optimised thermal management systems. HiVEP aims to deliver sub-10-minute fast charging, reduce energy consumption by at least 25%, extend range by 20%, and cut system costs by up to 20% in volume production. This article expands on the project objectives, methodological approach and expected key innovations as well as technical, environmental, and societal impacts. The discussion situates HiVEP within the European research and innovation landscape, emphasising its role in accelerating adoption of sustainable mobility solutions.
Schernus, Christof, Nada, Shady, Neuhaus, Christoph, Ewald, Jens, Swierc, Daniel, Kallur-Krishnamoorthy, Rajesh, Vasiliadis, Harilaos
Electric Health-Monitoring Wiring (E-Wiring) for Aircraft2026-26-0785To be published on 06/01/2026
Modern aircraft depend on extensive electrical wiring networks for power distribution, avionics, and control systems; however, these wiring systems are vulnerable to wear, insulation degradation, and arcing over time, leading to safety risks and costly unscheduled maintenance. The proposed Electric Health-Monitoring Wiring (E-Wiring) system integrates temperature, current, insulation, vibration, and environmental sensors directly into aircraft wiring harnesses to enable continuous monitoring and intelligent fault detection. Data from these embedded sensors are processed through a distributed edge AI network, forming an Electrical Health Monitoring System (EHMS) capable of real-time diagnostics, predictive maintenance, and fault localization. The architecture comprises smart cable segments with sensor nodes, local harness gateways for edge processing, aircraft-level EHMS integration via AFDX/Ethernet, and cockpit or maintenance displays linked to ground-based cloud analytics for fleet
Tammana, Bala Sai Sri Rohit, Murthy, Harsha, Mendu, HarikaSivaniSunandha
Augmented Reality and Multimodal Interfaces for Astronaut Training and In-Orbit Operations2026-26-0796To be published on 06/01/2026
Augmented Reality (AR) and multimodal human–machine interfaces (HMI)—combining visual overlays, voice, gesture, eye-tracking, and biometric sensing—are now maturing into flight-relevant technologies capable of transforming astronaut training and in-orbit operations. These interfaces reduce task time, lower error rates, and mitigate cognitive load, thereby strengthening both crew autonomy and mission safety. For India, the timing is critical: Gaganyaan human spaceflight program is approaching decisive crewed missions in the late 2020s, and the Bharatiya Antariksh Station (BAS) is targeted for the 2030s. AR/MMI can be positioned not as an accessory but as a core enabling system for training throughput, real-time crew support, and resilient station operations. This paper provides (i) an overview of India’s current scenario, including Gaganyaan astronaut training ecosystems, IIT Madras collaborations on AR/VR, and Vyommitra humanoid readiness; (ii) synthesis of global case studies—NASA
Yadav, Anoop Singh
Hybrid Physics-Informed Neural Network and PID Control for Fault-Tolerant Autonomous Multi-copters2026-26-0770To be published on 06/01/2026
This paper addresses the critical challenge of fault-tolerant control in autonomous multi-copters, particularly under conditions of one or two rotor failures a scenario that often leads to severe instability and a complete loss of directional control due to unbalanced torque and resultant autorotation. Existing advanced control strategies, including optimal approaches such as LQR, typically require precise system modeling and state estimation, which are difficult to achieve in real-world, dynamic failure scenarios. Alternative methods like fuzzy logic, sliding mode control, and gain-scheduling either lack robust generalization or are impractical for enumerating all possible failure cases. In this work, a hybrid control framework integrating Physics Informed Neural Networks (PINN) with a standard PID controller is proposed for fault-tolerant operation of autonomous multi-copters subject to multiple actuator failures. PINNs incorporate governing physical laws as regularization in their
Charapalle, Samruddhi, Venugopalan, Nandagopalan, Nerkundram Muralidharan, Arun, Sundararaj, Laveen
Comparative study between MQTT 3.1 and MQTT 5.0: Evaluating Theoretical Enhancements Through Practical Throughput Benchmarks for Aerospace IoT Systems2026-26-0711To be published on 06/01/2026
As aerospace platforms increasingly adopt connected architectures for avionics, telemetry, and predictive diagnostics, lightweight messaging protocols have become foundational. MQTT (Message Queuing Telemetry Transport), due to its simplicity and minimal network overhead, is widely used in both ground and onboard aerospace systems. With the release of MQTT 5.0, the protocol introduces a suite of new capabilities designed to enhance scalability, reliability, and diagnostics. However, these benefits come with trade-offs in complexity and potential overhead, particularly in real-time and resource-constrained environments typical in aerospace. This paper presents a comparative evaluation of MQTT 3.1 and MQTT 5.0, exploring their theoretical differences — such as reason codes, session expiry, user properties, topic aliasing, shared subscriptions, and enhanced error feedback — and their impact on system-level performance. We analyze these aspects using practical throughput benchmarks
Bhuyar, Prabhudev, M, Meghana, Kaniraja, Christina, Thomas, Tinto
A GenAI-Based Approach for Real-Time Multilingual In-Flight Communication Systems.2026-26-0784To be published on 06/01/2026
In today’s global aviation industry, passenger experience is strongly influenced by effective communication. In-flight announcements, often limited to English and a single local language, can create confusion and stress for international travelers who may not be fluent in either. This communication gap not only impacts passenger comfort but also poses potential risks in conveying time-sensitive or safety-critical information. Recent advances in Generative Artificial Intelligence (GenAI), particularly in speech recognition, neural machine translation, and naturalistic text-to-speech, provide a pathway to overcome these challenges. This paper explores the concept of real-time multilingual in-flight announcements, delivered in each passenger’s preferred language through connected headphones or personal devices. The proposed system architecture integrates speech-to-text conversion, language translation, and speech synthesis with aircraft infotainment platforms. Potential applications range
Mishra, Ashwini, Kature, Kartik, Patil, Ashish
Automated Vehicle Marshalling SystemJ3292_202604 (Current)4/13/2026
The scope of this standard is Automated Vehicle Marshalling (AVM) of vehicles to enable remote control functionality for achieving SAE Level 4 (High Driving Automation according to the Surface Vehicle Recommended Practice SAE J3016) driving capabilities at controlled speeds within geofenced private controlled environments utilizing infrastructure-assisted sensing. It specifies a concept of operations which includes a reference-system architecture and use cases, system functional and performance requirements, multiple wireless communications protocols, and associated wireless messages to support AVM. AVM use cases such as plant marshalling, depot marshalling, valet parking, electric vehicle charging, etc. The Automated Vehicle Marshalling Central Server (AVM CS) infrastructure does detect objects, vehicles, vulnerable road users, and any obstructions that help guide the Automated Vehicle (AV) starting from uninitiated, activation, identification, automated control, unavailable and
V2X Core Technical Committee
J1939 Digital AnnexJ1939DA_202604 (Current)4/10/2026
This document is intended to supplement the J1939 documents by offering the J1939 information in a form that can be sorted and searched for easier use.
Truck and Bus Control and Communications Network Committee
Model-Based Development of the “Big 5” in Fuel Cell Systems2026-01-00724/7/2026
Fuel cell systems are gaining traction across heavy-duty applications, driven by global decarbonization targets. Managing their inherent complexity and diverse architectural requirements, commonly organized into the “Big 5” fuel cell subsystems (stack, thermal, electric, anode, and cathode), necessitates advanced Model-Based Development (MBD) approaches. This paper presents and validates a constraint-graph-based, equation-oriented, acausal MBD methodology for fuel cell system (FCS) development, implemented in an industrial modeling environment. This methodology supports scalable functional and software development from 75 kW single-stack systems to twin-stack configurations exceeding 250 kW. It facilitates robust parameterization and reuse of consistently formulated, subsystem-level physical models across Model-in-the-Loop (MiL) to Hardware-in-the-Loop (HiL) environments, ensuring numerically robust software architectures and improved embedded control quality. Industrial application
Bandi, Rajendra Prasad, Bleile, Thomas
Architecture-Driven Fixed-Point Scaling for AUTOSAR-Based ECUs: A Production-Feasible Architecture for Centralizing Scaling Semantics2026-01-00694/7/2026
Floating-point arithmetic is widely used in automotive embedded software to scale Controller Area Network signals and calibration parameters with fractional factors such as 0.1. However, floating-point operations, even on microcontrollers equipped with floating-point units, can increase execution time and CPU load. In AUTOSAR architectures, converting floating-point scaling to fixed-point is not trivial because scaling semantics must be integrated consistently across components, yet AUTOSAR platform toolchains offer only limited automation at the Application Data Type level. Although CompuMethod definitions can express scaling, integration typically remains manual and distributed across application software components, reducing consistency and reusability. This study presents an architecture-driven methodology that formalizes fixed-point scaling as a centralized architectural service, realized through a parser-driven fixed-point macro generation pipeline. Standardized CAN DBC and
Lee, Hoseok, Ko, Donggun
Understanding Adversarial Transferability in Vision-Language Models for Autonomous Driving: A Cross-Architecture Analysis2026-01-01704/7/2026
Vision-language models (VLMs) are increasingly used in autonomous driving because they combine visual perception with language-based reasoning, supporting more interpretable decision-making, yet their robustness to physical adversarial attacks, especially whether such attacks transfer across different VLM architectures, is not well understood and poses a practical risk when attackers do not know which model a vehicle uses. We address this gap with a systematic cross-architecture study of adversarial transferability in VLM-based driving, evaluating three representative architectures (Dolphins, OmniDrive, and LeapVAD) using physically realizable patches placed on roadside infrastructure in both crosswalk and highway scenarios. Our transfer-matrix evaluation shows high cross-architecture effectiveness, with transfer rates of 73–91% (mean TR = 0.815 for crosswalk and 0.833 for highway) and sustained frame-level manipulation over 64.7–79.4% of the critical decision window even when patches
Fernandez, David, MohajerAnsari, Pedram, Salarpour, Amir, Pese, Mert D.
Accelerating Electrification through Modular Propulsion: GM’S Strategy for BEV and BET Platforms2026-01-04104/7/2026
General Motors (GM) continues to advance its electrification strategy through the development of scalable Battery Electric Vehicle (BEV) and Battery Electric Truck (BET) platforms. This paper highlights GM’s latest BEV and BET products that leverage shared Drive Unit (DU), Rechargeable Energy Storage System (RESS), and integrated power electronic (IPE) components across multiple vehicle programs. By adopting a modular and commonized propulsion architecture, GM achieves significant benefits in manufacturing efficiency, cost optimization, speed to market, and product flexibility. The shared DU, RESS, and IPE components are engineered to meet diverse performance requirements while maintaining high standards of energy efficiency, thermal management, and durability. This approach enables rapid deployment of electrified solutions across various segments, from passenger vehicles to full-size trucks, without compromising on capability or customer experience. The paper outlines the technical
Liu, Jinming, Sevel, Kris, Anwar, Mohammad, Oury, Andrew, Welchko, Brian, Gagas, Brent
Simulation Study on Rear-Wheel Steering Control of FSAE Vehicles Based on Temporal Convolutional Network2026-01-06504/7/2026
The Formula SAE (FSAE) race track is characterized by a large number of corners, making cornering performance a key factor affecting lap time. Based on the proportional control strategy for rear-wheel steering angles, this paper proposes a steering angle optimization method using a Temporal Convolutional Network (TCN). The TCN model features a faster training speed than traditional sequential neural networks. In addition, dilated convolutions enable an exponential expansion of the receptive field without increasing computational costs, making it particularly suitable for capturing the temporal dependencies of vehicle states. By processing vehicle dynamic parameters including front-wheel steering angle, vehicle speed, yaw rate and sideslip angle, the model calculates the correction value of the rear-wheel steering angle. This correction value is then superimposed with the reference value of the rear-wheel steering angle derived from the proportional control strategy, which serves as the
Liu, Xiyuan
Efficient Embedded Control of Nonlinear Systems for Software-Defined Vehicles2026-01-00754/7/2026
Software-defined vehicles (SDVs) are reshaping automotive control architectures by shifting intelligence to embedded systems, where computational efficiency is paramount. This paper presents a systematic evaluation of control strategies (PID, LQR, MPC) for the classical control problem involving inverted pendulum on a cart under strict embedded constraints representative of software-defined vehicle ECUs. The objective is to evaluate and compare the performance of advanced control algorithms under varying control objectives when deployed on microcontrollers with constrained computational and memory resources, representative of the limitations encountered in embedded platforms used for SDVs. Furthermore, the study illustrates systematic optimization strategies that enable these algorithms to achieve real-time execution within such resource-constrained environments. Each control strategy is implemented with careful consideration of algorithmic complexity, real-time responsiveness, and
Vupparige, Varun, Pandya, Vidit
Leveraging Large Language Models for Natural Language-Driven CAD Automation and Multi-Objective Optimization in Thermal Component Design2026-01-05064/7/2026
The design of thermal components (such as automotive heat exchangers) requires balancing multiple competing objectives—thermal performance, aerodynamic efficiency, structural integrity, and manufacturability. Traditional design workflows rely on manual Computer Aided Design (CAD) modeling and iterative simulations, which are both labor-intensive and time-consuming. Recent advances in Large Language Models (LLMs) present untapped potential for automating parametric CAD generation. However, current LLM-based approaches primarily handle simple, isolated geometric primitives rather than complex multi-component assemblies. This work introduces a progressive framework that leverages fine-tuned LLMs (Qwen2.5-3B-SFT) integrated with the CadQuery CAD kernel to automatically generate parametric geometries from natural language descriptions. As a foundational study, this work focuses on Step 1 of the framework: generating and optimizing isolated geometric primitives (cylinders, pipes, etc.) that
Chaudhari, Prathamesh, Tovar, Andres
Backward Fuzzy Driving Control for 6×4 Off-Road Vehicles2026-01-01484/7/2026
Off-road autonomous vehicle systems must be able to operate across unstructured and variable terrain while avoiding obstacles. This presents significant challenges in vehicle and control system design, especially for less conventional platforms such as 6×4 vehicles. While forward driving autonomy has developed and matured in recent years, effective reverse navigation remains an under-explored area of vehicle co-design. Reversing 6×4 vehicles have limited rear steering authority, an extended wheelbase, and asymmetric traction, which introduce complex dynamics into any control system that is used. To address this need, a robust and experimentally validated fuzzy logic control architecture for 6×4 reverse navigation was developed during the course of this project. This architecture incorporates both near-field and long-range path data with adaptive outputs controlling steering and velocity based on a rule base that covers the whole vehicle state space. This method has low computational
Dekhterman, Samuel R., Sreenivas, Ramavarapu S., Norris, William R., Patterson, Albert E., Soylemezoglu, Ahmet, Nottage, Dustin
Beyond PFAS: Unlocking the Potential of R290 and R744 for EV Efficiency2026-01-01274/7/2026
The anticipated PFAS ban in the US by 2029 has created a need to evaluate alternative refrigerant solutions for automotive thermal management systems. This work compares three candidates—Propane (R290), Carbon Dioxide (R744), and R1234yf—through system-level testing and demonstration projects. R1234yf remains the current industry baseline. Test results show that Propane (R290) delivers comparable efficiency while offering a significantly lower global warming potential. However, its flammability presents integration challenges, not present with R1234yf or R744. CO₂ (R744) demonstrated promising performance as well. To address safety concerns with Propane, AVL developed mitigation measures including rapid leak detection, robust containment strategies, and optimized circuit layouts designed to reduce ignition risks. These countermeasures were validated in practice through the European Commission’s QUIET project. Within this program, a Honda B-segment electric vehicle was equipped with a
bires, Michael, Possegger, Jonathan
A Shared Autonomy Architecture: Utilizing an Advanced Personalized Control System (APeCS) and Shared Autonomy Arbiter (SAB)2026-01-01474/7/2026
The shared autonomy framework has become an option with great potential in the field of autonomous vehicles. Human and machine control decisions typically demonstrate strengths in different scenarios. As a result, the robustness of systems can be enhanced by the collaboration between humans and autonomy. A shared autonomy architecture that takes into account both human and environmental factors was proposed in this work. The authority distribution between the human operator and the autonomy algorithm was determined by the Shared Autonomy Arbiter (SAB). Designed with a two-tier structure, the SAB incorporated a policy-level decision module, as well as a numerical-level arbitration tuning module. A fuzzy inference system (FIS) was incorporated to enhance the noise tolerance of the policy selection module. Furthermore, the human factor was taken into account by applying a projection to the users’ control input. The human operator’s control decision was projected by the Adaptive
Sang, I-Chen, Norris, William, Patterson, Albert, Sreenivas, Ramavarapu S., Soylemezoglu PhD, Ahmet, Nottage, Dustin S.
Predictive Modeling and Sensitivity Analysis of Thermal Runaway in 800V Lithium-Ion Battery Module Using DFSS Methodology2026-01-01244/7/2026
Thermal runaway in high-voltage lithium-ion battery modules should focus on critical safety and design challenges in electric vehicle applications, which need predictive methods that enhance passenger safety and support regulatory compliance. The primary purpose of a lithium-ion battery in an electric vehicle is to provide reliable energy storage while maintaining safe operation under different operating conditions. This study proposes a Design for Six Sigma (DFSS) methodology to virtually predict and correlate thermal runaway and its propagation in an 800V high-power lithium-ion battery pack module. Conventional propagation analysis relies heavily on physical testing, whereas the DFSS-based virtual framework enables cost-effective evaluation at early design stages. Input factors included are heat transfer pathways, which are sensitive to the temperature changes, as well as thermal propagation time. Control factors are the design or process parameters that engineers use to establish
Dixit, Manish, Raja, Vinayak, Gudiyella, Soumya
The Golden Tester2026-01-00924/7/2026
The objective of this paper is to understand the effort required to integrate the hardware and software of in-vehicle cybersecurity systems. The in-vehicle cybersecurity method discussed is the SAE J1939-91C, which involves Network formation, Rekeying, and secure Message Exchange between Electronic Control Units (ECUs). The SAE J1939-91C network security protocol operates over a CAN-FD network to perform necessary cryptographic operations and key generation. To evaluate the method, test vectors were created to validate SAE J1939-91C key generations and cryptographic operations on the simulated ECU in-vehicle network system hardware (such as the Beacon or Pi devices). We introduce a lightweight, transport-agnostic benchmark comprising deterministic AES-CMAC test vectors and a simple verification utility, requiring no specialized hardware or build system. This minimal artifact set enables reproducible and machine-parsable validation of SAE J1939-91C security across diverse lab
Zachos, Mark, Medam, Krishna Teja
Evolutionary Paths Toward Vehicle Zonal E/E Architecture: A Unified Architectural Framework2026-01-00714/7/2026
The automotive industry is undergoing a fundamental transformation in Electrical/Electronic (E/E) architecture, evolving from traditional distributed and domain-based designs toward zonal configurations. The rapid growth of software-defined functionality, cross-domain integration, and centralized computing has exposed inherent limitations of legacy architectures in scalability, wiring complexity, and system integration. Zonal E/E architecture addresses these challenges by consolidating computing and Input/Output (I/O) resources into high-performance controllers distributed across physical zones of a vehicle. This transformation, however, cannot occur instantaneously, as contemporary vehicle designs and E/E system solutions are the result of decades of incremental development based on distributed and domain-based paradigms. Moreover, key enabling technologies for zonal E/E architecture—such as high-performance Central Compute Platform (CCP) and zonal controllers, high-speed automotive
Jiang, Shugang
The In-Vehicle Connectivity Foundation Powering Autonomous Driving2026-01-00094/7/2026
Achieving full vehicle autonomy is not just about adding sensors or compute - it requires a fundamental shift in how vehicles are architected. Autonomous systems rely on higher-resolution sensors, massive processing power, and the ability to fuse data from multiple sources in real time. Centralized in-vehicle architectures, which consolidate computing and enable sensor fusion, place unprecedented demands on connectivity. Precise time synchronization across systems becomes critical, as does advanced control to ensure safe and reliable operation. Any delay or data loss can impact decision-making, making robust, resilient communication links essential. High-performance connectivity is the backbone of this evolution. It must deliver the highest bandwidth to handle massive streams of sensor data, support long-reach connections across the vehicle, and maintain error-free performance even in the most challenging electromagnetic environments. This combination of speed, reach, and reliability
Shwartzberg, Daniel
Ultra-Low Latency Video Pipeline Optimization for Perception-Centric Autonomous Systems2026-01-00224/7/2026
Autonomous platforms such as self-driving vehicles, advanced driver-assistance systems (ADAS), and intelligent aerial drones demand real-time video perception systems capable of delivering actionable visual information at ultra-low latency. High-resolution vision pipelines are often hindered by delays introduced at multiple stages—sensor acquisition, video encoding, data transmission, decoding, and display—undermining the responsiveness required for safety-critical decision making. This study introduces a holistic system-level optimization framework that systematically reduces end-to-end video latency while maintaining image fidelity and perception accuracy. The proposed approach integrates hardware-accelerated encoding, zero-copy direct memory access (DMA), lightweight UDP-based RTP transport, and GPU-accelerated decoding into a unified pipeline. By minimizing redundant memory copies and software bottlenecks, the system achieves seamless data flow across hardware and software
Indrakanti, Rama Kiran Kumar
Target Cascading Kinematics and Compliance Tables to Suspension Design2026-01-05844/7/2026
During the initial design phase, automotive Original Equipment Manufacturers (OEMs) require the adaptability to examine various suspension system architectures while maintaining focus on the specific performance objectives. Those requirements are expressed by Kinematics and Compliance (K&C) look-up tables and represent the footprint of what the suspension should look like in real-world applications. However, translating those requirements into the full geometric hardpoint layout is not straightforward. This process often relies on trial-and-error approaches, making it time consuming and requiring significant expertise. This challenge, known as ”target cascading,” remains a major hurdle for many engineers. The main objective of this paper is to cascade the suspension requirements from K&C look-up tables to hardpoint locations by adopting an automatic workflow and ensuring respect for constructive and feasibility constraints. Design space exploration was conducted using a robust
Brigida, Piero, Di Carlo, Paolo, Di Gioia, Niccolò, Geluk, Theo, Tong, Son, Alirand, Marc, Gorgoretti, Davide, Occhineri, Marco, Tassini, Nicola, Berzi, Lorenzo
High-Speed Flashing in the Age of the Software-Defined Vehicle2026-01-01124/7/2026
In recent years, the use of software-defined platforms has become increasingly prevalent. As a result, flashing ECUs has become an important factor in ensuring efficiency, quality, and compliance in vehicle production. Conventional approaches, such as final end-of-line flashing, are increasingly unsuitable for the growing amounts of data, complex dependencies, mixed physics and protocols, and traceability requirements. This SAE paper presents the current trends and challenges in ECU flashing. It highlights the impact of the exponential growth in software payloads and the necessary migration to offline and parallel workflows. This can only be achieved through closer integration with automated and robot-assisted production, considering the requirements of cybersecurity and verifiability. It also addresses the shift toward end-to-end flashing ecosystems, where updates are performed consistently from a single source covering the assembly line, warehouses, yards, workshops, and over-the-air
Böhlen, Boris, Budak, Oguz, Wells, Michael
In-Use Life Digital Twin (Redundant Storage) and Swappable Use Case for Battery Durability2026-01-03974/7/2026
As regulatory frameworks for zero-emission vehicles (ZEVs) and battery electric vehicles (BEVs) continue to evolve, there is growing emphasis on monitoring battery durability and usage throughout the vehicle lifecycle. These regulations increasingly specify the use of data monitors and tracking mechanisms to assess battery health and performance. In addition, regulations require anti tampering mechanisms especially for monitors that have external write access. Historically, regulations focused primarily on vehicle warranty; however, with the introduction of battery durability monitors, clarity is needed for the new battery durability monitors. More specifically if the battery durability monitors track with the lifetime of the vehicle or if they follow the lifetime of the battery. Furthermore, current regulations provide no guidance on high-voltage (HV) traction battery service strategies or methods to protect monitors from tampering by external customers. This paper will classify
Laskowsky, Patricia, Bunnell, Justin, Zettel, Andrew, Albarran, Josue
Mathematical Modeling of Trust Calibration for Human–Automation Safety2026-01-05304/7/2026
Trust calibration is vital for safe human–automation interaction but remains largely qualitative. This study develops multiple quantitative frameworks modeling trust as a function of automation reliability. Four progressive models of binary, linear, triangular, and logistic formalize the calibrated trust zone, defining where human reliance aligns with system performance. The framework corrects major misconceptions: that trust is purely qualitative, that low trust–low reliability states are acceptable, and that overtrust and distrust pose equal risk. It establishes a minimum reliability threshold for meaningful trust and identifies distrust as the safer default in high-risk contexts. A case study on an empirical observation of 32 AI applications plotted in the trust–reliability space confirms the analysis, revealing a consistent distrust tendency where reliability exceeds user confidence and other observations. By quantifying trust through reliability, the study reframes it as a
Wen, He, Mounir, Adil
Sensitivity Analysis and Development Approach of HV Battery Lifetime for Complete Vehicle Integration2026-01-03954/7/2026
The lifetime and aging of the high voltage battery is one of the major discussion points for the end-customer to decide between buying a car with an electric powertrain or still using a conventional powertrain. Therefore, the provision of adequate vehicles to the end-customer, the aging of the high voltage battery become an important topic for the complete vehicle development. In addition, also legal regulations (e.g. EU7) will preset minimum requirements for the warranty of the high voltage battery. These circumstances define the lifetime / aging of the HV battery to be a complete vehicle development target, which needs to be developed. The paper will present a method for the development process of a lifetime target from complete vehicle perspective. The method is based on the generation of a representative monthly power profile and temperature profile. Depending on a monthly user routine, ambient temperature profile and charging behavior, the vehicle specific battery power profile
Martin, Michael
Thermal Characterization of an Automotive High Voltage Junction Box2026-01-03904/7/2026
The high voltage battery junction box (HVJB) controls and protects the high voltage connections of the battery pack to the traction, auxiliary, and charging systems. HVJBs are composed of busbars, contactors, fuses, and other protection systems. The health of the HVJB is paramount to ensure performance of electric vehicles. However, sensing and monitoring in the HVJB are often lacking due to packaging cost, causing limited capability of the vehicle controller to estimate the status and health of the unit. This publication focuses on the experimentation of an automotive HVJB to characterize the operation and build the foundation for the development of prognostic algorithms for HVJB. A production HVJB has been acquired and heavily instrumented. Extensive testings are performed in adiabatic and in ambient conditions at various current levels for various durations of operation. The testing setup was calibrated and iterated based on preliminary results, and the testing conditions were
Arigo, Sam, Borgerson, Joe, D'Arpino, Matilde, Zhu, Di, Zhang, Liwen
Innovative Smart Switch Architecture Catering to 12 Volts to 48 Volts Battery Bus Requirements2026-01-00644/7/2026
Electronics is entering rapidly into all automotive subsystems, performing control and monitoring tasks apart from making the entire vehicle intelligent. Interface with the external automotive eco-system needs careful attention during the system design. It defines how seamlessly the electronic unit interacts with rest of the vehicle. It needs to do so in an effective manner without compromising on cost and other automotive application constraints. This paper focusses on the “smart switch building block” that forms heart of an automotive output interface echo system.: Its importance stems from the fact that, a smart switch is an indispensable building block for any electronic control system driving external loads. As various novel electical and electronics architectures are entering various vehicle segments, the need for a single reusable solution that will cater to 12 Volts to 48 Volts battery buses is increasingly being felt. However, no prevelant solution meets this requirement. Even
Vaidya, Vishwas Manohar
Roundabout Dilemma Zone Data Mining and Forecasting with Trajectory Prediction and Graph Neural Networks2026-01-00294/7/2026
Traffic roundabouts, as complex and safety-critical road scenarios, present significant challenges for autonomous vehicles. In particular, predicting and managing dilemma zone (DZ) encounters at roundabout intersections remains a pivotal concern. This paper introduces an AI-driven system that leverages advanced trajectory forecasting to anticipate DZ events, specifically within traffic roundabouts. At the core of our framework is a modular, graph-structured recurrent architecture powered by graph neural networks (GNNs). By modeling agent interactions as a dynamic graph, our approach integrates heterogeneous data sources - including semantic maps - while capturing agent dynamics with high fidelity. This GNN-based forecasting model enables accurate prediction of DZ events and supports safer, data-driven traffic management decisions for both autonomous and human-driven vehicles. We validate our system on a real-world dataset of roundabout intersections, where it achieves high precision
Lu, Duo, Satish, Manthan, Farhadi, Mohammad, Chakravarthi, Bharatesh, Yang, Yezhou
Application of DFSS for Optimizing Thermal Protection in Electric Vehicles during Battery Thermal Runaway Events2026-01-01424/7/2026
Battery thermal runaway is a major safety concern in electric vehicles because of the extreme heat and hazardous gases released during cell failure. These venting events can quickly raise the temperature of the battery enclosure and cabin floor, threatening occupant safety. To address this challenge, this study employs the Design for Six Sigma (DFSS) methodology to design and optimize a thermal protection system that delays and limits heat transfer to the cabin. A physics-based transient heat-transfer model was combined with DFSS principles to systematically evaluate insulation materials, shield layouts, surface emissivity, and layer geometry. An L-18 orthogonal array was used to identify key parameters and quantify their influence on thermal robustness. The optimized architecture reduced cabin-floor temperature rise under severe runaway conditions (600–900 °C vent gas), meeting occupant-egress safety requirements. Findings confirm DFSS as an effective framework for developing high
El-Sharkawy, Alaa, Asar, Mona, Taha, Nahla, Sheta, Mai
Low-Voltage Electric Vehicle with Increased Electrical Safety and Distributed Charging System2026-01-03964/7/2026
Reducing the high-voltage BEV to a household level of 120-240 volts is considered in the paper as an effective means of solving the problems of electrical safety, maintenance and minor repairs of an electric vehicle in household conditions, and distributed power supply of BEV within walking distance for the driver. The analysis of the low-voltage electric drive is performed under the assumption that the battery has a nominal voltage of 200 volts. The issues of transforming a high-voltage machine (400 volts) into a low-voltage one (200 volts) by switching the stator phase sections from serial to parallel connection without changing the overall and energy characteristics are considered. It is shown that a two-motor unit with induction machines with a capacity of 50 kilowatts can provide 100 kilowatts in long-term and up to 200 kilowatts in peak modes. The paper considers the issues of implementing a low-voltage inverter and modern trends in distributed power supply for BEVs based on low
Smolin, Victor, Sobolevskiy, Anatoliy, Volovich, Georgy
Modular Smart Corner Systems for Next-Generation Electric Vehicle Architecture2026-01-06394/7/2026
The transition to software-defined vehicles (SDVs) necessitates a paradigm shift in both control strategies and vehicle architecture. The EU-funded R&D project SmartCorners addresses this challenge by developing integrated, modular, and scalable smart corner systems (SCS) that combine in-wheel motor (IWM)-based propulsion, brake blending, active suspension system, and steer-by-wire functionality in one module. These SCS can be retrofit or smoothly integrated into the highly adaptable skateboard chassis architecture of modern electric vehicles (EVs), enabling scalable deployment across diverse vehicle types. The central approach of this paper is the utilization of artificial intelligence (AI) and machine learning (ML) to implement multi-layer, data-driven control strategies, facilitating real-time actuation, fault mitigation, and user-centric EV architecture. The SmartCorners project strives to demonstrate significant enhancements, including improved real-world driving range due to
Ratz, Florian, Armengaud, Eric, Formento, Cecilia, Moscone, Giulia, Sorrentino, Gennaro, Bisciaio, Giorgio, Sorniotti, Aldo, Amati, Nicola, Braun, Daniel, Deibler, Bernd, Boxberger, Valerius, Sottile, Salvatore, Ivanov, Valentin, Fuse, Hiroyuki, Kompara, Tomaž
Dual Battery Pack System for Multi-Voltage Electric Vehicle Operation2026-01-03924/7/2026
Electric Vehicles (EV) have become a major focus in the automotive industry. This paper introduces a propulsion system design, which supports the Wide Torque Band (WTB) concept to boost the power density of PM (permanent magnet) motors in EV Trucks resulting in performance, efficiency, and cost benefits. A selectable 400V/800V battery system has been developed to support the WTB concept and enhance the power density of permanent-magnet motors in electric vehicles. The RESS comprises two 400V battery packs that can be charged at 400V in parallel or at 800V in series via a DC fast-charging (DCFC) connection. In this study, an 800V driving mode was additionally implemented. A prototype battery management system (BMS) along with existing production voltage, current and temperature measurement block hardware are applied to perform mode switching, safety, and cell balancing. The success of this dual pack hardware enables high voltage dynamometer testing of a new 800V DU (Drive Unit) and
Zhu, Yongjie, Lee, Chunhao, Gopalakrishnan, Suresh, Namuduri, Chandra
Design and Performance Evaluation of a Dust Contamination Simulation Test Apparatus for Automotive Cameras2026-01-00594/7/2026
This study presents the development and validation of a muddy water spray apparatus designed to simulate dust contamination on vehicle sensors for sensor cleaning system testing. It is important to have a constant and quantifiable test environment for the vehicle development process. For verifying the apparatus, muddy water, prepared by mixing standardized dust powder, salt, and water to maintain constant contamination test conditions, was sprayed onto glass specimens to evaluate equipment consistency. Deposited dust weight and thickness were measured across multiple spray cycles, with statistical analyses confirming consistent and reliable deposition. Paired t-tests indicated no significant difference between sample positions, demonstrating uniform spray distribution. The apparatus was further applied to individual infrared (IR) cameras to observe performance degradation under dry and wet contamination conditions showing statistically consistent increases in contamination levels
Jinhyeok, Gong
Radar-Based Dynamic Pixel Activation in Camera Sensors for Optimized Image Processing, Power Efficiency, and Thermal Management2026-01-00474/7/2026
The increased integration of radar and vision sensors in modern vehicles has significantly improved environmental perception, safety, and automation. Nevertheless, conventional camera modules capture images in fixed, continuous frames, leading to unnecessary data processing, power consumption, and heat generation in the limited space of small sensors. The paper discusses the technology of Radar Based Dynamic Pixel Activation (RDPA); whereby radar data can be used to dynamically activate specific pixels on the camera sensor, optimizing image capture and processing. Through a systematic literature review of peer-reviewed articles published between 2021 and 2025, we examined the literature on radar-camera fusion, adaptive imaging, and sensor design that is efficient in power consumption. The review indicates a research gap that there is no current paradigm that dynamically activates sensor pixels at the hardware level using radar data. We aggregated ten topical studies and proposed a
Kasarla, Nagender Reddy
Curvature-Based End-to-End Autonomous Vehicle Path Planning at Intersections2026-01-00454/7/2026
Autonomous vehicle navigation requires accurate prediction of driving path curvature to ensure smooth and safe trajectory planning. This paper presents a novel approach to curvature prediction using deep neural networks trained on GPS-derived ground truth data, rather than model predictions, providing a more accurate training signal that reflects actual vehicle motion. We develop a multi-modal neural network architecture with temporal GRU encoders that processes vision features, driver intent signals, historical curvature, and vehicle state parameters to predict curvature. A key innovation is the use of GPS-based actual curvature measurements computed from vehicle motion data (κ = ωz/v) as training supervision, enabling the model to learn from real-world driving patterns. The model is trained on 5,322 samples from real-world driving data collected on The University of Oklahoma’s Norman Campus using a Comma 3X device and a 2025 Nissan Leaf electric vehicle. Experimental results
Hajnorouzali, Yasaman, Wang, Hanchen, Li, Taozhe, Burch, Collin, Lee, Victoria, Tan, Lin, Arjmandzadeh, Ziba, Xu, Bin
A System-Level Calibration Framework for Embedded Vision: Integration of Sensor, Firmware, and Software Enhancements2026-01-00134/7/2026
Embedded vision systems are essential for contemporary applications, including robotics, advanced driver assistance systems (ADAS), and intelligent surveillance; yet they frequently experience diminished image quality due to resource constraints, environmental variability, and inconsistent illumination conditions. Such degradations impact multiple visual attributes—sharpness, contrast, color accuracy, noise levels, and structural similarity—that are critical for reliable perception in safety- and performance-driven domains. This study introduces a comprehensive system-level calibration architecture that integrates three coordinated layers: sensor-level adjustment, firmware optimization, and adaptive software enhancements. At the sensor level, exposure control, gain tuning, and white balance adjustments mitigate luminance imbalance and color shifts under changing light conditions. Firmware optimization leverages image signal processor (ISP) parameters to reduce temporal and spatial
Indrakanti, Rama Kiran Kumar, Vishnoi, Nitin, Kamadi, Venkata
Designing Military Vehicles for a Battlefield Characterized by Drones2026-01-02624/7/2026
The modern battlefield is increasingly characterized by the use of small drones. As such, military vehicles must now be designed to account for this threat. This paper presents a model-based systems engineering approach to identify vehicle vulnerabilities and generate new vehicle requirements to mitigate them. This approach uses a standard set of System Modeling Language diagrams. A vehicle’s primary roles are captured in a series of use cases. Each use case is characterized by a sequence of activities performed by the vehicle. These activity sequences are captured in an activity diagram, which are used to wargame how a drone can exploit the vehicle at each phase. Each potential exploitation is assigned likelihood and severity scores, which feed into a risk index. This risk index is then used to prioritize each vulnerability. From these vulnerabilities, a set of operational requirements are derived, which then informs the development of system requirements. As the system matures, the
Ells, Alec, Werntz, Bryson, Saulsberry, Taylor, Wilkinson, Cooper, Mittal, Vikram
EV Propulsion Drive Converter SiC Power Device Over-Temperature Protection under Low Motor Speed Operation2026-01-04364/7/2026
Monitoring power device temperature in an electric vehicle propulsion drive converter is extremely important to achieve full power delivery within the maximum power capability envelope. Usually, on-die temperature sensors are installed on Si-IGBT power devices in electric vehicle propulsion drive converters to enable monitoring device temperature and achieve over-temperature protection. Currently, SiC MOSFET is a promising power device in power converters of electric drives because of its lower loss, higher switching speed, higher voltage capability, and higher junction temperature limit in comparison with the widely used Si-IGBT. However, SiC MOSFET is a more expensive device, installation of an on-die temperature sensor on SiC MOSFET will significantly increase its cost and complexity. So presently, there is no junction temperature sensor installed in SiC MOSFET due to which there is great difficulty protecting SiC MOSFET from over temperature. When a junction temperature estimation
Thongam, Jogendra Singh, Ge, Baoming, Bradford, Steven, Kulkarni, Milind
Protection of Megawatt-Scale DC Fast Charging Infrastructure for Heavy-Duty Vehicles: Ultra-Fast DC Breaker Development, Testing, and Future Roadmap2026-01-04004/7/2026
Direct Current (DC) fast charging enables supply of megawatt (MW) scale DC power to the large battery systems of Heavy-Duty Electric Vehicles (HDEVs), such as electric trucks, buses, ferry and construction machinery. This contrasts with Alternating Current (AC) charging, which is limited by the capacity of the On-Board Charger (OBC) that converts AC to DC to charge the battery. In DC fast charging, however, the Electric Vehicle Supply Equipment (EVSE) delivers DC power directly to the HDEVs, bypassing the OBC. The feasibility of fast DC charging has been driven by advancements in semiconductor technology offering higher voltage and current handling capabilities as well as improvements in battery energy density. Ongoing research indicates continued growth in both semiconductor power handling and battery storage capacity, further strengthening the case for fast DC charging. Key benefits include significantly higher charging efficiency, drastically reduced charging times, and lower driver
Rahman, Md Rakib-Ur, Dobrzynski, Daniel
Topology Optimization of Multibody Structures Considering Inertial Loads Using an Equivalent Static Displacement Framework2026-01-05014/7/2026
Topology optimization (TO) of dynamic structures has traditionally been constrained to single-body components and simplified harmonic load assumptions. Extending TO to multibody dynamic systems (MBS) remains challenging due to complex coupling between inertia, mass distribution, and joint constraints. This paper presents an inertia-aware topology optimization framework that integrates mass moment of inertia (MMI) constraints within an enhanced Equivalent Static Displacement (ESD) methodology. Building upon the authors’ previously developed ESD framework, the proposed approach — termed Inertia-Augmented Equivalent Static Displacement (IA-ESD) — explicitly incorporates inertial effects arising from accelerations and joint interactions. The approach enables dynamically consistent optimization by coupling design-dependent inertia tensors with equivalent static displacements derived from nonlinear multibody dynamics. Case studies involving an MBB beam and a piston–connecting rod assembly
Gupta, Aakash, Tovar, Andres
Predictive Maintenance of EV Drive Unit Oil Pumps through Machine Learning on Fleet Telemetry Data2026-01-01614/7/2026
The reliability of Drive Unit (DU) oil pumps is critical to the performance and safety of electric vehicles, as these pumps provide essential lubrication and thermal management. In modern EV architectures, real-time health monitoring of these pumps typically relies on indirect signals than dedicated sensing hardware, a design choice optimized for cost, weight, and system complexity. This makes early fault detection a non-trivial challenge. To address this limitation, we present a novel, data-driven anomaly detection framework that leverages large-scale customer fleet telemetry and advanced machine learning to identify incipient pump degradation that traditional diagnostic methods often fail to capture. Specifically, we develop an XGBoost regression model trained on time-series features—including commanded pump speed, oil temperature, and historical pump current—to predict expected current behavior under nominal conditions. Deviations are quantified using the Mean Absolute Percentage
Li, Jingman, Yao, Mengqi, Rahimi, Sahil, Lin, Joanne
CrashML Analyzer: A Data-Driven Approach for Investigation and Fault Assignment in Autonomous Driving Collisions2026-01-01604/7/2026
This study investigates factors contributing to autonomous vehicle (AV) accidents and proposes an automated fault determination framework. A total of 563 accident reports from the State of California Department of Motor Vehicles spanning from 2019 to 2024 were analyzed by converting unstructured standardized reports into structured data using custom extraction tools. Analysis of these reports reveals that AVs were not at fault in 69.4% of cases and were fully at fault for 22.6% of cases. The proposed method uses these reports to provide an early indicator of fault likelihood and potentially replaces tedious manual review. Machine Learning (ML) and Natural Language Processing techniques were used to replicate the reported faults, achieving 96% average accuracy across three models: Gradient Boosting, Linear Regression, and Random Forest. Through feature engineering techniques in semantic feature extraction from narrative accident descriptions, quantifiable variables were obtained and
Rwejuna, Florida Perfect, Majid, Nishatul, Goutham, Mithun, Loukili, Alae
Automotive Engineering: April 202626AUTP044/2/2026
WCX 2026: Honda's crowdsourced PRMS can improve roads, drivers Proactive Roadway Maintenance System could tap into humanity's altruistic mode. Sue Bai's done the research and will deliver a keynote speech. Precise fit Why precision engineering is defining confidence in next-generation internal combustion engines. How precise GNSS unlocks scalable, cost-effective mapping for ADAS and autonomy The benefits of using the Global Navigation Satellite System and SD maps to keep ADAS/AVs up-to-date. Editorial It's war o'clock, again Supplier Eye Goodbye: A look at the past and thoughts on the future Caterham Project V EV on track for 2027 Joby looks to Toyota for investment and manufacturing Tensor goes with 433 Arms in upcoming Robocar Scout CEO: 160K reservations, no unsolvable Problems Ford announces 48-volt architecture for future electric truck BorgWarner to supply integrated drive and generator for EREV trucks The electric Mercedes G-Wagon: A pricey joy 2026 Audi A6: A sensible but fun
Ford announces 48-volt architecture for future electric truck26AUTP04_104/1/2026
Ford is seeding bits of information about its electric mid-size pickup that is slated to land in 2027. The vehicle is the brainchild of the company's skunkworks division and is set to become the standard by which other new electric vehicles from the blue oval are constructed. The underlying UEV (Universal Electric Vehicle) platform is meant to reduce the cost of EVs so they are comparable with gas vehicles. During a presentation focused on efficiency and how Ford plans to eke every mile it can out of the upcoming vehicle, the automaker shared that the vehicle would have a 48-volt architecture instead of the traditional 12-volt system via a DC-to-DC converter. The converter will step down the power from the 400-volt battery system to 48 volts to power ancillary items in the vehicle.
Baldwin, Roberto
On-Board System Requirements for V2V Safety CommunicationsJ2945/1_202603 (Current)3/4/2026
This standard specifies the system requirements for an on-board vehicle-to-vehicle (V2V) safety communications system for light vehicles1, including standards profiles, functional requirements, and performance requirements. The system is capable of transmitting and receiving the SAE J2735-defined basic safety message (BSM) [1] over a dedicated short range communications (DSRC) wireless communications link as defined in the Institute of Electrical and Electronics Engineers (IEEE) 1609 suite and IEEE 802.11 standards [2] to [6].
V2X Core Technical Committee
Vehicle Level Validation Test Procedures for V2V Safety CommunicationsJ2945/1A_202603 (Current)3/3/2026
This document provides vehicle-level data collection, data analysis, and data verification procedures that may be used to verify that an instrument under test (IUT) satisfies the vehicle-level requirements specified in the SAE International (SAE) J2945/1 standard. For the purposes of this recommended practice, “vehicle-level requirements” primarily consist of those requirements which can be verified external to the vehicle. The IUT for these procedures is a configured dedicated short range communications (DSRC) vehicle-to-vehicle (V2V) device as defined in SAE J2945/1 and is installed on a light vehicle. While the IUT is conceptually separated from the vehicle it is installed on, the tests outlined in this document are primarily vehicle-level so the terms “vehicle” and “IUT” can generally be considered interchangeable. Additionally, non-vehicle-level complementary tests, not included in this document, are required to verify that the entire set of requirements specified in SAE J2945/1
V2X Core Technical Committee
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