Browse Topic: Architecture

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EV Propulsion Drive Converter SiC Power Device Over-Temperature Protection under Low Motor Speed Operation2026-01-0436To be published on 04/07/2026
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 widely used Si-IGBT. However, SiC MOSFET is a more expensive device, so the present technology trend is trying to reduce its chip size. 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
Thongam, Jogendra Singh, Ge, BAOMING, Bradford, Steven, Kulkarni, Milind
Predictive Maintenance of EV Drive Unit Oil Pumps via Machine Learning on Fleet Telemetry2026-01-0161To be published on 04/07/2026
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. However, direct physical sensors for real-time pump health monitoring are absent in current architectures, making early fault detection particularly challenging. 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. The core of the approach is 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 Error (MAPE) between predicted and actual currents, providing a continuous and interpretable measure of
Li, Jingman, Yao, Mengqi, Rahimi, Sahil, Lin, Joanne
Beyond PFAS: Unlocking the Potential of R290 and R744 for EV Efficiency2026-01-0127To be published on 04/07/2026
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. In AVL’s Refrigerant Cooling Project, direct CO₂ cooling battery plate simplified the thermal system architecture and provided adequate cooling capacity during high charging loads. 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
bires, Michael, Possegger, Jonathan
THE IN-VEHICLE CONNECTIVITY FOUNDATION POWERING AUTONOMOUS DRIVING2026-01-0009To be published on 04/07/2026
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 all 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
A System-Level Calibration Framework for Embedded Vision: Integration of Sensor, Firmware, and Software Enhancements2026-01-0013To be published on 04/07/2026
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
Target Cascading Kinematics and Compliance Tables to Suspension Design2026-01-0584To be published on 04/07/2026
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
Implementation and evaluation of a method to defend in-vehicle networks using SAE J1939-91C to authenticate network messages between multiple ECUs.2026-01-0092To be published on 04/07/2026
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. The evaluation results provide a benchmark for network system performance that can be used in a system test bench to assess compliance with the SAE J1939-91C standard and to test the standard's effectiveness in ambiguous edge cases. In the future, this benchmark could be utilized in "Plugtests" to assign test scores to non-uniform SAE J1939-91C
Zachos, Mark, Medam, Krishna Teja
Understanding Adversarial Transferability in Vision-Language Models for Autonomous Driving: A Cross-Architecture Analysis2026-01-0170To be published on 04/07/2026
Vision-language models (VLMs) offer a promising end-to-end approach for autonomous driving, but their robustness to physical adversarial attacks remains an unaddressed gap for safety-critical deployment. This paper introduces a systematic framework for evaluating these attacks using the Dolphins VLM and CARLA simulator. We employ Natural Evolution Strategies (NES), a black-box optimization method, to generate adversarial patches without internal model access. Our approach uses semantic similarity loss and Expectation over Transformation (EoT) to ensure physical realizability under varying viewing conditions. Patches are strategically placed on realistic advertising infrastructure (bus shelter panels and highway billboards), reflecting a plausible threat model. We establish two complementary scenarios: crosswalk pedestrian detection suppression and highway steering manipulation. Our evaluation reveals critical vulnerabilities. Adversarial patches achieved a 76.0% overall attack success
Fernandez, David, MohajerAnsari, Pedram, Salarpour, Amir, Pese, Mert D.
Ultra-Low Latency Video Pipeline Optimization for Perception-Centric Autonomous Systems2026-01-0022To be published on 04/07/2026
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
CrashML Analyzer: A Data-Driven Approach for Investigation and Fault Assignment in Autonomous Driving Collisions2026-01-0160To be published on 04/07/2026
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 reveal that AVs were not at fault in 69.4% of cases, and were fully at fault for 22.6% of the 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
Mechanical Performance and Deformation Mechanisms of Hybrid Cylindrical TPMS Lattices with Gradient Wall Thickness2026-01-0484To be published on 04/07/2026
Lattice metamaterials possess lightweight, high strength, and excellent energy absorption, making them promising for engineering applications. Their mechanical properties can be tuned by cell topology and structural design. This study proposes a multi-degree-of-freedom hybrid strategy for cylindrical TPMS lattices. Three uniform types (Primitive, Gyroid, IWP) were fabricated and validated through experiments and simulations. Radial, axial, and circumferential hybridizations were then explored, revealing distinct deformation and energy absorption mechanisms: CMA structures showed multi-stage behavior, while CMR and CMC offered longer plateau stages and higher stresses. Inspired by biological architectures, gradient wall thickness designs were introduced, significantly improving performance. Notably, CMR-GPI213 and CMC-PIG312 increased specific energy absorption by 24% and 14.8%, and CMA-GIP312 reduced peak force by 37.7% without sacrificing absorption. These results demonstrate that
Liu, Zhe, Guo, Pengbo, Zhong, Gaoshuo, Lian, Yuehui, Li, Youguang
Study of Approaches for Enhanced Traction Control Response for Hybrid Powertrain Architectures2026-01-0415To be published on 04/07/2026
Traction Control functionality requires fast and accurate response by powertrain system for wheel slip control. Close loop control effort of wheel slip shall be greatly reduced with more accurate feedforward torque. This paper explores the method of achieving requested traction control torque with better accuracy for torque converter-based hybrid electric powertrain architectures using improved dynamic modeling of torque converter. This method uses input inertia compensation involving feedforward and closed loop control modeling of torque converter and includes conversion of traction control intervention request through appropriate domains. Commanded torques by hybrid controller at wheel with and without these approaches are compared in Model in the loop simulation environment to identify optimum approach.
Karogal, Indrasen, Ober, Mitchell, Banuso, Abdulquadri, Sharma, Ashay
Radar-Based Dynamic Pixel Activation in Camera Sensors for Optimized Image Processing, Power Efficiency, and Thermal Management2026-01-0047To be published on 04/07/2026
This paper presents a radar-based dynamic imager pixel activation method designed to optimize image processing efficiency, power consumption, and thermal management in automotive In-cabin monitoring systems (DMS and OMS). By integrating radar sensing with high-resolution cameras, the proposed approach selectively activates image sensor pixels only within radar-identified regions of interest, such as moving occupants or key cabin areas. This selective pixel activation substantially reduces the computational workload and data bandwidth required, as the camera transmits high-resolution data solely for relevant zones while maintaining lower resolution or deactivating pixels elsewhere. The fusion of radar and camera data enables adaptive sensor control that caters to distinct scenarios, including driver gaze tracking and passenger monitoring, thereby enhancing DMS and OMS functionality. The architecture supports mounting configurations such as headliner or rearview mirror installations
Kasarla, Nagender Reddy, Patel, Krunal, Balaraman, Nirmal Kumar
HaloBus: Edge Computing‑Enabled Real‑Time Boarding and Exit Detection for Enhanced Transportation Safety Using Lightweight AI2026-01-0555To be published on 04/07/2026
This paper proposes HaloBus, an innovative, edge‑computing solution designed to mitigate this risk by detecting student boarding and exiting in real time using lightweight AI based methods. A persistent challenge in elementary school transportation is the issue of missing students after they exit their buses, which disproportionately impacts low-income households. Current safety systems are forced upon the individual households with their own methods. Common methods include applications on a personal device or a small tracker. However, not everyone can afford these options, and every parent deserves to feel like their child is safe. That is why HaloBus was invented. The system employs YOLOv5u—an Ultralytics‑enhanced, anchor‑free, split‑head architecture that offers a superior accuracy–speed tradeoff. By providing real-time, on-device alerts, HaloBus enables immediate intervention to prevent a student from being left behind, thereby shifting the focus from reactive post-incident
Getz, Grayson, Zadeh, Mehrdad, TAN, Teik-Khoon
Design and Performance Evaluation of a Dust Contamination Simulation Test Apparatus for Automotive Cameras2026-01-0059To be published on 04/07/2026
In the era of Software-Defined Vehicles (SDVs), sensor integration has become increasingly prevalent, enabling advanced features such as autonomous driving and driver assistance systems. However, contamination from road dust and muddy water poses significant risks to the performance of vehicle camera sensors. Manual methods of applying dust for testing sensor durability are subject to inconsistencies and human error, potentially undermining the validity of test results. To address this, we developed an automated dust deposition apparatus specifically designed for automotive camera systems. This device enables precise and repeatable control over dust application, thereby enhancing the reliability and efficiency of the testing process. The dust contamination simulation apparatus leverages programmable parameters to consistently reproduce various environmental scenarios encountered in real-world driving conditions. Performance evaluations demonstrated that the system achieves uniform dust
Jinhyeok, Gong
A Shared Autonomy Architecture: Utilizing an Advanced Personalized Control System (APeCS) and Shared Autonomy Arbiter (SAB)2026-01-0147To be published on 04/07/2026
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.
Backward Fuzzy Driving Control for 6×4 Off-Road Vehicles2026-01-0148To be published on 04/07/2026
Offroad 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 6x4 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 6x4 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 6x4 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, Sam, Patterson, Albert, Sreenivas PhD, Ramavarapu, Norris, William, Soylemezoglu PhD, Ahmet, Nottage PhD, Dustin
Designing an e-SUV to Meet Global Front Crash Safety Standards2026-01-0580To be published on 04/07/2026
Vehicle crashworthiness has improved tremendously over the past 20 years with increasing scenarios being defined and assessed by governments and nonprofits every few years. Bodies such as NTHSA, IIHS and EuroNCAP created safety ratings to distinguish objectively which vehicles are the safest for consumers. In doing so, automotive OEMs must spend more resources and design additional crash safety features onto their vehicles, often making them heavier in the process. As a result, negatively impacting the driving range of an electric vehicle. At Lucid Motors, through a holistic and first-principles approach, CAE simulations were used to guide the design of the Lucid Gravity to pursue highest possible crash safety ratings and battery protection while minimizing weight on the vehicle structure. By keeping the weight low, the Lucid Gravity Grand Touring was able to achieve an estimated EPA-rated range of 450 miles, which is groundbreaking for a full size 3-row electric SUV. This paper
Ng, Meng
Accelerating Electrification Through Modular Propulsion: GM’s Strategy for BEV and BET Platforms2026-01-0410To be published on 04/07/2026
General Motors 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, 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 rationale behind
Liu, Jinming, Sevel, Kris, Anwar, Mohammad, Oury, Andrew, Welchko, Brian, Gagas, Brent
Efficient Embedded Control of Nonlinear Systems for Software-Defined Vehicles: A Case Study Using the Inverted Pendulum2026-01-0075To be published on 04/07/2026
Software-defined vehicles (SDVs) are reshaping automotive control architectures by shifting intelligence to embedded systems, where computational efficiency is paramount. This paper presents the implementation of multiple control strategies such as PID, LQR, and MPC for the inverted pendulum on a cart problem, a canonical example of nonlinear and unstable system control. The objective is to demonstrate how sophisticated control algorithms can be optimized for execution on microcontrollers with limited processing power and memory, reflecting the constraints typical of embedded platforms in SDVs. Each control strategy is implemented with careful consideration of algorithmic complexity, real-time responsiveness, and resource utilization. Performance is evaluated across key metrics including stability, convergence time, and computational load, enabling a comparative analysis that highlights trade-offs between control fidelity and hardware efficiency. The results provide actionable insights
Vupparige, Varun, Pandya, Vidit
Vehicle Health Monitoring and Failure Prognosis through Edge-Based Component Wear Tracking2026-01-0094To be published on 04/07/2026
Traditional vehicle maintenance has relied primarily on mileage-based schedules and periodic visual inspections, approaches that often fail to account for variations in driving conditions, component quality, and usage patterns. These conventional methods typically result in either unnecessary premature replacements or unexpected component failures, leading to increased operating costs and unplanned downtime. Reactive maintenance strategies prove particularly problematic for critical wear components like clutch assemblies, brake systems, and suspension parts where undetected degradation can cause secondary damage. This paper presents an advanced monitoring system that shifts from scheduled maintenance to actual condition-based servicing through real-time wear measurement. The solution implements a network of distributed sensors that directly track critical parameters: ultrasonic measurement of clutch plate thickness, resistive wear sensors for brake pads, linear potentiometers for
Saraswat, Shubham, Vishe, Prashant
Enhanced Catalyst Heating Control Strategy within Hybrid Electric Powertrain Architectures2026-01-0414To be published on 04/07/2026
Kudupley, Harshal, Pooyafar, Payam, Karogal, Indrasen, Patel, Nadirsh
Leveraging Large Language Models for CAD Automation and Multi-Objective Optimization of Automotive Heat Exchangers2026-01-0506To be published on 04/07/2026
The design of automotive heat exchangers is a critical challenge, requiring the careful balance of thermal performance, aerodynamic efficiency, structural integrity, and manufacturability. Traditional design processes rely heavily on manual CAD modeling and iterative simulations, which are both time-intensive and computationally demanding. Recent advances in artificial intelligence, particularly in Large Language Models (LLMs) and Transformer-based architectures, open new possibilities for automating these tasks. This work introduces a novel framework that integrates LLMs with CAD kernels to generate parametric heat exchanger geometries directly from natural language descriptions. The models are automatically exported into standard formats for computational fluid dynamics simulations and seamlessly incorporated within a multi-objective optimization pipeline. By coupling natural language-driven CAD automation with advanced optimization techniques, the framework enables rapid exploration
Chaudhari, Prathamesh, Tovar, Andres
Dual Battery Pack System for Multi-Voltage Electric Vehicle Operation2026-01-0392To be published on 04/07/2026
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. The WTB drive requires an 800V battery for vehicle operation. The chosen solution combines two 400V battery packs to create a selectable 400V/800V DC bus. The electrical hardware and controls to manage the dual battery packs in either series (800V) or parallel (400V) mode are presented in this paper. A prototype battery management system (BMS) along with existing production Voltage, Current, Temperature Monitoring (VITM) 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 inverters for EVs. The flexibility of switching between two voltage levels (400
Zhu, Yongjie, Lee, Chunhao, Gopalakrishnan, Suresh, Namuduri, Chandra
Designing Military Vehicles For A Battlefield Characterized by Drones2026-01-0262To be published on 04/07/2026
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 SysML 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 can be used to wargame how a drone could exploit the vehicle at each phase. Each exploitation is assigned a likelihood and severity, which feed into a risk index. This risk index is then used to prioritize each vulnerability. From these vulnerabilities, a set of operational requirements can be derived, which then informs the development of system requirements. As the system matures, the physical system
Ells, Alec, Werntz, Bryson, Saulsberry, Taylor, Wilkinson, Cooper, Mittal, Vikram
Protection of Megawatt-Scale DC Charging Infrastructure for Heavy Duty Vehicles: A Review of Current Technologies and Future Roadmap2026-01-0400To be published on 04/07/2026
Abstract Direct Current (DC) fast charging enables the direct delivery of megawatt (MW) scale power to the large battery systems of heavy-duty electric vehicles (EVs), such as trucks, buses, and construction machinery. This contrasts with Alternating Current (AC) charging, which is constrained by the limited capacity of onboard chargers. The feasibility of ultra-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 ultra-fast DC charging. Key benefits include significantly higher charging efficiency, drastically reduced charging times, and lower driver fatigue. However, unlike AC systems, DC charging infrastructure presents unique protection challenges. These challenges arise from the rapid rise of DC fault
Rahman, Md Rakib-Ur, Dobrzynski, Daniel
Topology Optimization of Multibody Structures Considering Inertial Loads2026-01-0501To be published on 04/07/2026
Topology optimization (TO) of dynamic structures has traditionally focused on single-body components subject to harmonic and impact loads, with limited extension to multibody dynamics. This work presents a novel framework for topology optimization of multibody dynamic systems considering inertial loads. The method integrates density-based topology optimization with nonlinear multibody dynamics to capture the distribution of mass, stiffness, and inertia across interconnected rigid and flexible components. Inertial forces, arising from accelerations and joint interactions, are explicitly incorporated into the optimization formulation to ensure dynamically consistent designs. Numerical examples demonstrate how accounting for inertial effects leads to lightweight, dynamically efficient multibody structures that outperform conventional static- or harmonic-based designs. The proposed approach establishes a foundation for inertia-aware topology optimization of multibody systems, with
Gupta, Aakash, Tovar, Andres
Research on the control of magnetic weakening scheme for the voltage utilization rate of hybrid transmission2026-01-0447To be published on 04/07/2026
This paper proposes a weak magnetic control method for PMSM current regulators based on the ratio of pressure to frequency and voltage utilization rate when the motor speed exceeds the weak magnetic speed. That is, the direct-axis offset current id is generated through the action of weak magnetic feedforward and weak magnetic I regulator, and the control of weak magnetic speed can directly adjust the demagnetization effect of id to achieve weak magnetic control of PMSM. The weak magnetic control method of PMSM current regulators with the outer loop of voltage utilization rate is adopted. It is proposed to use the phase-locked loop control method to decode the motor position signal in a closed loop and calculate the motor speed. The fluctuation of the position signal is reduced and the signal is more stable. This paper realizes constant power speed regulation based on high voltage utilization rate and weak magnetic control method at high speed operation above the base speed, which
Jing, Junchao, Yan, Xiaoqian, Liu, Yiqiang, Sun, Fengjiao, Dai, Zhengxing
Curvature-Based End-to-End Autonomous Vehicle Path Planning at Intersections2026-01-0045To be published on 04/07/2026
Autonomous vehicle navigation requires accurate prediction of driving path curvature to ensure smooth and safe trajectory planning. This paper presents an end-to-end approach to curvature prediction using deep neural networks trained on real-world driving data. We develop a comprehensive neural network architecture that directly maps high-dimensional sensor inputs to curvature predictions, eliminating the need for intermediate feature engineering steps. The model processes multi-modal inputs including vision features, vehicle state parameters, and environmental context through a deep learning pipeline. Our end-to-end approach demonstrates the feasibility of learning complex driving behaviors directly from raw sensor data, providing a more robust and generalization solution compared to traditional rule-based methods. The neural network architecture incorporates dropout regularization and adaptive learning rate scheduling to ensure stable training and optimal performance. Results show
Hajnorouzali, Yasaman, Wang, Hanchen, Li, Taozhe, Burch, Collin, Lee, Victoria, Tan, Lin, Arjmandzadeh, Ziba, Xu, Bin
Roundabout Dilemma Zone Data Mining and Forecasting with Trajectory Prediction and Graph Neural Networks2026-01-0029To be published on 04/07/2026
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 with
Lu, Duo, Farhadi, Mohammad, Satish, Manthan, Yang, Yezhou, Chakravarthi, Bharatesh
Predictive Modeling and Sensitivity Analysis of Thermal Runaway in 800V Lithium-Ion Battery Pack Module Using DFSS Methodology2026-01-0124To be published on 04/07/2026
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 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 the
Dixit, Manish, Raja, Vinayak, Gudiyella, Soumya
Evolutionary Path to Vehicle Zonal E/E Architecture2026-01-0071To be published on 04/07/2026
The automotive industry is undergoing a major transition in Electrical/Electronic (E/E) architecture, evolving from traditional distributed and domain-based designs to zonal configuration. The rapid growth of software-driven functionality, cross domain function integration, and demands for centralized management have exposed the limitations of distributed and domain-based designs in complexity, scalability, and cost. Zonal E/E architecture addresses these challenges by consolidating computing and I/O resources into high-performance controllers distributed across physical zones of the vehicle, greatly reducing wiring complexity, improving modularity, and enabling scalable, software-defined vehicles. However, this transition cannot be immediate because vehicle design and electrical system solutions have been built on decades of distributed and domain-based development. Moreover, the enabling technologies for realizing zonal E/E architecture, such as high-speed automotive Ethernet, zonal
Jiang, Shugang
Model-Based Development of the “Big 5” in Fuel Cell Systems2026-01-0072To be published on 04/07/2026
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
Method of Estimating Electric Vehicle Charge Time Based on Neural Networks2026-01-0382To be published on 04/07/2026
Accurate prediction of electric vehicle charging time is critically hindered by dynamic, non-linear factors including battery aging which is indicated by the State of Health (SOH), substantial power diversion to thermal management systems in extreme temperatures, fluctuating user-defined accessory loads, and hardware limitations of the charging infrastructure. Traditional estimation methods, reliant on static models or predefined calibrations, fail to adapt to these real-world variables, leading to inaccurate predictions and user dissatisfaction. This paper presents a novel data-driven estimation framework utilizing a tailored feedforward neural network architecture specifically designed for this complex task. The model processes a sensitive set of inputs—including initial State of Charge (SOC), SOH, battery temperature, charging station power level and user-selected target SOC—to effectively capture the intricate, non-linear interdependencies governing the charging process. The
Xie, Zhentao, shojaei, Sina, Weslati, Feisel
Securing the AI-Driven Vehicle: A Hardware-Based Security Framework for Scalable, Trustworthy Autonomy2026-01-0085To be published on 04/07/2026
Autonomous driving technology in modern vehicles marks a pivotal evolution in transportation, but it may also introduce system-level vulnerabilities that span from tampering with sensors and interfaces to compromising compute units and communication links. This paper proposes a unified, layered hardware security architecture for AI-equipped automated vehicles. Based on current automotive Ethernet and zonal architectures, it provides end-to-end trust using hardware interface security, accelerated-cryptography, and SRAM PUF-based key provisioning. All security primitives are anchored to a hardware root of trust, delivering cryptographic identity, secure boot enforcement, and trusted key storage across the entire vehicle lifecycle. Our architectural assumptions follow the direction ISO 26262, ISO/SAE 21434 and AI development mandates for next-generation SAE Level 3+ vehicles using zonal Ethernet backbones, Time-Sensitive Networking (TSN), and heterogeneous compute islands (e.g., vision
C Suriyanarayanan, Pav, Iacob, Radu
Innovative Smart Switch Architecture Catering to 12 Volts to 48 Volts Battery Bus Requirements2026-01-0064To be published on 04/07/2026
Electronics is rapidly entering all automotive subsystems, performing control and monitoring tasks apart from making the entire vehicle intelligent.. Interface with the external automotive ecosystem needs careful attention during the system design. It defines how seamlessly the electronic unit interacts with the rest of the vehicle. It needs to do so in an effective manner without compromising on cost and other automotive application constraints. This paper focuses on the “ smart switch building block” that forms the 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 electrical and electronics architectures are entering various vehicle segments need for a single reusable solution that will cater to 12 Volts to 48 Volts battery buses is increasingly being felt. However, no prevalent solution meets this requirement. Even
Vaidya, Vishwas Manohar
Sensitivity analysis and development approach of HV battery lifetime for complete vehicle integration2026-01-0395To be published on 04/07/2026
The lifetime and aging of the high-voltage battery are among the key factors influencing the end customer’s decision to choose an electric powertrain over a conventional one. Consequently, battery aging has become a critical aspect of overall vehicle development to ensure that vehicles meet customer expectations, for example risk of repair costs or resale value. Additionally, legal regulations (e.g. EU7) will define minimum warranty requirements for high voltage batteries. These circumstances establish battery lifetime and aging as an essential development target at complete vehicle level. The paper presents a method for the development process of a lifetime target from complete vehicle perspective. The approach involves the generation of a representative monthly electrical power and temperature profile for the high voltage battery. Based on a monthly user routine, ambient temperature profile and charging behavior, a vehicle-specific battery power profile is created using energy flow
Martin, Michael
Thermal Characterization of an Automotive High Voltage Junction Box2026-01-0390To be published on 04/07/2026
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 and reliability of electric vehicles. However sensing and monitoring in the HVJB is often lacking due to 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 testing 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
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
Balanced Cathode and Anode Interfaces Formation Performance of Sodium Difluoro(oxalato)borate Additive Enables High-Voltage Sodium-Ion Batteries2026-01-70342/27/2026
Sodium-ion batteries (SIBs) are becoming a strong candidate for large-scale energy storage applications due to their cost-effectiveness and abundant sodium resource reserves. Ether solvents have advantages such as excellent low-temperature performance and good reduction stability. However, poor oxidation stability limits the use of ether-based electrolytes, which need to be addressed urgently. In this study, 1 M sodium tetrafluoroborate (NaBF4) and 0.05 M sodium difluoro(oxalato)borate (NaDFOB) were added in tetraethylene glycol dimethyl ether (G4), which is named “BDG4”. BDG4 electrolyte can promote the formation of cathode electrolyte interface (CEI) layers containing NaF and B─O/B─Na inorganic components on the surface of the cathode. The dense CEI layers can prevent the solvent from undergoing oxidation reactions. Therefore, thanks to the lower highest occupied molecular orbital (HOMO) energy level of G4 and its close coordination structure with Na+, the electrolyte has a high
Bai, Zheng, Mai, Xinyu, Dou, Xin, Chen, Zixin, Song, Zhen, Chen, Long, Li, Chunzhong
Analysis of Network V2X-Based Services and Opportunities for StandardizationJ3297_202602 (Current)2/25/2026
This report analyzes the characteristics of mobile network communication and highlights the technical aspects of using mobile networks to implement V2X applications. This report provides a high-level analysis of architecture, protocols, and performance and is intended to support future implementation guidance and standardization for providing V2X services over mobile networks, also referred to as network V2X.
V2X Core Technical Committee
Airport Electrical Power System HarmonicsAIR5387 (Current)2/17/2026
This SAE Aerospace Information Report (AIR) is intended to cover all airport 50 or 60 Hz electrical systems as well as all electrical utilization equipment that is attached to those systems.
AGE-3 Aircraft Ground Support Equipment Committee
A Nature-Inspired Optimization Approach for Vienna Rectifier Control in EV Fast Charging Applications2026-28-01122/12/2026
The growing adoption of electric vehicles (EVs), particularly those utilizing High-Voltage battery systems, demands fast-charging infrastructure that ensures high efficiency and power quality. The proposed GJO algorithm is employed to optimize the control and switching parameters of the Vienna rectifier, thereby improving harmonic performance and conversion efficiency without altering the converter hardware. This paper focuses solely on control optimization of the Vienna rectifier topology and does not include DC–DC isolation or galvanic separation. Filter components are modeled with equivalent series resistance (ESR) to account for incremental losses. Simulation results demonstrate that the Golden Jackal optimization (GJO) based control reduces input current THD to 2.09%, has a power factor of 0.998, and achieves an efficiency of 98.53%, representing a fractional but consistent improvement over conventional control methods such as SSA, ALO, and PSO. These findings highlight the
R, Mohammed Abdullah, N, Kalaiarasi
SAE Truck & Off-Highway Engineering: February 202626TOFHP022/12/2026
AI influence on ICE design Experts offer their outlook on the future of heavy-duty internal combustion engines, and of course artificial intelligence will play a role. Accelerating down the road to autonomy Commercial success of the autonomous truck may be closer than we think. Digital tech a Las Vegas headliner With the rise of AI and other new digital technologies on the horizon, ACT Expo 2026 will be a crucial intersection for industry leaders to map out the route ahead. Editorial CONEXPO debuts - from micro to massive Rolls-Royce pursues pure methanol for marine engines Integrated architectures reshape the mobile-hydraulic ecosystem A proactive approach to hydraulic health in Class 8 trucks Precast concrete bases pave the way for rapid EV charger deployment CONEXPO 2026: Volvo CE to reveal new articulated hauler and short-swing, compact and wheeled excavators Product Briefs SPOTLIGHTS: Imaging/Cameras; Simulation/Analysis Software Q&A Bosch Rexroth: Hydraulics remain relevant in
Quantum Meets Agile: Crypto-Agility for a Post-Quantum World2026-28-01272/12/2026
As the automotive industry transitions toward software-defined vehicles and highly connected ecosystems, cybersecurity is becoming a foundational design requirement. A challenge arises with the advent of quantum computing, which threatens the security of widely deployed cryptographic standards such as RSA and ECC. This paper addresses the need for quantum-resilient security architectures in the automotive domain by introducing a combined approach that leverages Post-Quantum Cryptography (PQC) and crypto-agility. Unlike conventional static cryptographic systems, our approach enables seamless integration and substitution of cryptographic algorithms as standards evolve. Central to this work is the role of Hardware Security Modules (HSMs), which provide secure, tamper-resistant environments for cryptographic operations within vehicles. We present how HSMs can evolve into crypto-agile, quantum-safe platforms capable of supporting both hybrid (RSA/ECC + PQC) and fully post-quantum
Kuntegowda, Jyothi
Achieving Reliability, Safety & Security in SDV OS Architecture2026-28-01222/12/2026
Software-defined vehicles are those whose functionalities and features are primarily governed by software, thus allowing continuous updates, upgrades, and the introduction of new capabilities throughout their lifecycle. This shift from hardware-centric to software-driven architectures is a major transformation that reshapes not only product development and operational strategies but also business models in the automotive industry. An SDV operating system provides the base platform to manage vehicle software and enable those advanced functionalities. Unlike traditional embedded or general-purpose operating systems, it is designed to meet the particular demands of modern automotive architectures. Reliability, safety, and security become crucial because even minor faults may have serious consequences. Key challenges to be handled by the SDV OS include how to handle software bugs, perform real-time processing, address functional safety and SOTIF compliance, adhere to regulations, minimize
Khan, Misbah Ullah, Gupta, Vishal
Virtual Development Kit (VDK) for Software Defined Vehicle2026-28-01202/12/2026
This paper examines the technological and architectural transformations critical for advancing Software-Defined Vehicles (SDVs), emphasizing the decoupling of hardware from software. It highlights the limitations of traditional development models and proposes modern architectural approaches, including MPU-based designs and virtualization techniques, to foster flexible and scalable software ecosystems. Central to this vision is the concept of a Virtual Development Kit (VDK), which enables the design, validation, and scaling of SDVs even before physical hardware is available. The VDK integrates hardware platform emulators, operating systems, software stacks, and middleware optimized for high-performance computing (HPC) environments, providing developers with tools for early-stage testing, debugging, and integration while minimizing dependence on physical prototypes. As the automotive industry increasingly relies on software-defined features as primary drivers of innovation and
Khan, Misbah Ullah, Gupta, Vishal
Automotive Ethernet Proxy-Based Security Architecture in Vehicle Computers2026-28-01212/12/2026
Modern vehicles require sophisticated, secure communication systems to handle the growing complexity of automotive technology. As in-vehicle networks become more integrated with external wireless services, they face increasing cybersecurity vulnerabilities. This paper introduces a specialized Proxy based security architecture designed specifically for Internet Protocol (IP) based communication within vehicles. The framework utilizes proxy servers as security gatekeepers that mediate data exchanges between Electronic Control Units (ECUs) and outside networks. At its foundation, this architecture implements comprehensive traffic management capabilities including filtering, validation, and encryption to ensure only legitimate data traverses the vehicle's internal systems. By embedding proxies within the automotive middleware layer, the framework enables advanced protective measures such as intrusion detection systems, granular access controls, and protected over-the-air (OTA) update
M, Arvind, Praneetha, Appana Durga, Remalli, Ravi Teja
IoT-Enabled Cloud-Integrated Smart Parking System with Real-Time Monitoring and AI-Based Space Optimization for Next-Gen Mobility2026-28-01132/12/2026
This study presents the design and implementation of an advanced IoT-enabled, cloud-integrated smart parking system, engineered to address the critical challenges of urban parking management and next-generation mobility. The proposed architecture utilizes a distributed network of ultrasonic and infrared occupancy sensors, each interfaced with a NodeMCU ESP8266 microcontroller, to enable precise, real-time monitoring of individual parking spaces. Sensor data is transmitted via secure MQTT protocol to a centralized cloud platform (AWS IoT Core), where it is aggregated, timestamped, and stored in a NoSQL database for scalable, low-latency access. A key innovation of this system is the integration of artificial intelligence (AI)-based space optimization algorithms, leveraging historical occupancy patterns and predictive analytics (using LSTM neural networks) to dynamically allocate parking spaces and forecast demand. The cloud platform exposes RESTful APIs, facilitating seamless
Deepan Kumar, Sadhasivam, S, Balakrishnan, Dhayaneethi, Sivaji, Boobalan, Saravanan, Abdul Rahim, Mohamed Arshad, S, Manikandan, R, Jamuna, L, Rishi Kannan
Research on Online Learning Architecture for Engine Combustion Prediction on Multi-Platform Simulation03-19-01-00052/11/2026
To address the limitations of conventional offline data-driven models for engine parameter prediction in HIL testing, including poor generalization and inefficient use of supplementary data, this study develops an innovative cross-platform online learning architecture that integrates a pre-trained Python-based Wiebe parameter prediction model with high-fidelity MATLAB/Simulink engine simulation. The proposed framework incorporates five key functional modules (real-time data processing, online regression prediction, performance evaluation, incremental learning optimization, and engine simulation) to enable dynamic adaptation to varying engine conditions through seamless integration of Python’s incremental learning algorithms with Simulink’s simulation environment. By implementing a kth order polynomial decay learning rate strategy, the architecture significantly improves model convergence under limited training conditions while enhancing real-time performance and reliability in HIL
Wei, Mingxin, Shuai, Xiuyun, Wang, Zhaoyu, Zhao, Feiyang, Yu, Wenbin
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