Browse Topic: Hardware
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
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
Model Based Design (MBD) uses mathematical modelling to create, test and refine systems in simulated environment, primarily applied in control system development. This paper discusses an approach to control gear shifting using shift logic on vehicle level for twin clutch transmission using prototype controller. Twin clutch transmission is a concept with two clutches, one at input end of the transmission called primary clutch and the other at output end of the transmission called secondary clutch. This concept is proposed to counter the challenges with conventional transmission which include increased gear shift time and effort in lower gears, potential rollback of vehicle in uphill condition and chance of missed shifts. The advantages of this concept include reduced gear shift effort and improved synchronizer life with potential for reducing the size of the synchro pack. This paper proposes a methodology to develop shift logic, integrate hardware with software, flashing and calibration
This paper addresses the challenge of increasing hardware complexity, long development cycles and high costs associated with integrating multiple systems. The research explores the potential of Large Language Models (LLMs) when applied as chatbots to revolutionize the design and development of automotive electrical hardware systems, encompassing areas such as convenience features, safety systems, advanced lighting, vehicle body control and modular electronic control units. A key focus is on how LLMs can automate cost-reduction design tasks, including design optimization, requirements verification and component validation, ultimately driving down expenses without compromising performance or reliability. Furthermore, the research investigates how LLMs can assist in decision-making by providing data-driven insights that inform critical design choices and facilitate enhanced team collaboration, leading to improved productivity through innovative tools and streamlined workflows. In that
Modern warfare is defined as much by data dominance as by maneuver. From satellite-based intelligence, surveillance, and reconnaissance (ISR) platforms to dismounted soldiers' handheld radios, operational success depends on the ability to move, process, and act on digital information in real time. Yet this dependence introduces a critical vulnerability: as the force becomes more data-centric, it becomes more susceptible to disconnection, jamming, and cyber denial. In disconnected, intermittent, and limited (DIL) environments - where communications are degraded by terrain, adversarial interference, or limited infrastructure - traditional network architectures falter. Centralized command nodes and linear data pipelines cannot sustain the agility or resilience required at the tactical edge. The solution is a new design paradigm - one that integrates ruggedized hardware, edge computing, artificial intelligence (AI), and hybrid tactical-cloud architectures into a distributed, adaptive
In the electrical machines, detrimental effects resulted often due to the overheating, such as insulation material degradation, demagnetization of the magnet and increased Joule losses which result in decreased lifetime, and reduced efficiency of the motor. Hence, by effective cooling methods, it is vital to optimize the reliability and performance of the electric motors and to reduce the maintenance and operating costs. This study brings the analysis capability of CFD for the air-cooling of an Electric-Motor (E-Motor) powering on Deere Equipment's. With the aggressive focus on electrification in agriculture domain and based on industry needs of tackling rising global warming, there is an increasing need of CFD modeling to perform virtual simulations of the E-Motors to determine the viability of the designs and their performance capabilities. The thermal predictions are extremely vital as they have tremendous impact on the design, spacing and sizes of these motors.
As mission-critical systems demand more processing power, real-time data movement, and multi-domain interoperability, rugged embedded systems are being transformed. Today's military and aerospace applications increasingly demand the merging of AI computing, enhanced sensor interfaces, and cybersecurity - all under harsh environmental conditions. At the heart of this evolution is the 3U OpenVPX form factor, a modular, compact, and ruggedized hardware standard and increasingly the SOSA aligned subset of the architecture. However, next-generation systems need to go further: supporting higher bandwidth, better thermal efficiency, improved security, while maintaining multi-vendor interoperability and long-term sustainability. We'll discuss some of today's enclosure solutions as well as emerging technologies.
Object detection has many different uses in Command and Control (C2) systems such as autonomous control, target tracking, threat detection, and general surveillance. Graphics Processing Units (GPUs) are the de-facto standard hardware for these types of workloads in datacenter environments. Still, when deploying to an edge environment many considerations are required to ensure an optimized deployment. This paper provides a general overview of how to utilize GPUs for AI inference for object detection at the edge using NVIDIA® HoloScan as well as an overview of the many considerations to account for when selecting the most optimal GPU for any specific ground vehicle solution.
As SAE standard J3400 (also known as NACS, or North American Charging Standard) is being adopted by automakers and deployed on the latest EVs, the standard itself is still evolving. That latest evolution is SAE J3400/2. That extra 2 will make charging quicker, thanks to hardware updates to the port and inlet. As standards are announced, there are elements that result in a standard within the standard. Essentially, J3400 is more of a family of standards that handle everything from the internal technology that allows for compatibility, the hardware specs and testing of adapters that have and will be deployed and, in the case of J3400/2, the hardware itself.
ABSTRACT This paper will present the use of a licensed open-source software application based on commercially available off-the-shelf hardware for the control and data acquisition of aerospace system integration test rigs. System integration test rigs are complex systems requiring real-time deterministic control and high-speed data acquisition. Various aircraft flight systems and subsystems can be tested to see if they interact as they would on the aircraft without an airframe. These systems are critical to ensure interoperability during the development phase and facilitate the interchangeability of actual flight hardware, prototypes, and simulation models throughout the development cycle. Deploying open, flexible, and highly configurable real-time control and data acquisition systems ensures that development milestones will be achieved cost-effectively, whether using actual flight hardware or working with a simulation. This is because, as the prototype hardware is developed, the
This SAE Aerospace Standard (AS) establishes minimum requirements for eddy current inspection of circular holes in nonferrous, metallic, low conductivity (less than 5% IACS) aircraft engine hardware with fasteners removed. The inspection is intended to be performed at maintenance and overhaul facilities on engine run hardware.
Artificial intelligence (AI) systems promise transformative advancements, yet their growth has been limited by energy inefficiencies and bottlenecks in data transfer. Researchers at Columbia Engineering have unveiled a groundbreaking solution: a 3D photonic-electronic platform that achieves unprecedented energy efficiency and bandwidth density, paving the way for next-generation AI hardware.
The effect of seat belt misuse and/or misrouting is important to consider because it can influence occupant kinematics, reduce restraint effectiveness, and increase injury risk. As new seatbelt technologies are introduced, it is important to understand the prevalence of seatbelt misuse. This type of information is scarce due to limitations in available field data coding, such as in NASS-CDS and FARS. One explanation may be partially due to assessment complexity in identifying misuse and/or misrouting. An objective of this study was to first identify types of lap-shoulder belt misuse/misrouting and associated injury patterns from a literature review. Nine belt misuse/misrouting scenarios were identified including shoulder belt only, lap belt only, or shoulder belt under the arm, for example, while belt misrouting included lap belt on the abdomen, shoulder belt above the breasts, or shoulder belt on the neck. Next, the literature review identified various methods used to assess misuse
Camera-based mirror systems (CBMS) are being adopted by commercial fleets based on the potential improvements to operational efficiency through improved aerodynamics, resulting in better fuel economy, improved maneuverability, and the potential improvement for overall safety. Until CBMS are widely adopted it will be expected that drivers will be required to adapt to both conventional glass mirrors and CBMS which could have potential impact on the safety and performance of the driver when moving between vehicles with and without CBMS. To understand the potential impact to driver perception and safety, along with other human factors related to CBMS, laboratory testing was performed to understand the impact of CBMS and conventional glass mirrors. Drivers were subjected to various, nominal driving scenarios using a truck equipped with conventional glass mirrors, CBMS, and both glass mirrors and CBMS, to observe the differences in metrics such as head and eye movement, reaction time, and
Researchers have combined miniaturized hardware and intelligent algorithms to create a cost-effective, compact powerful tool capable of solving real-world problems in areas like healthcare.
To meet the requirements of high-precision and stable positioning for autonomous driving vehicles in complex urban environments, this paper designs and develops a multi-sensor fusion intelligent driving hardware and software system based on BDS, IMU, and LiDAR. This system aims to fill the current gap in hardware platform construction and practical verification within multi-sensor fusion technology. Although multi-sensor fusion positioning algorithms have made significant progress in recent years, their application and validation on real hardware platforms remain limited. To address this issue, the system integrates BDS dual antennas, IMU, and LiDAR sensors, enhancing signal reception stability through an optimized layout design and improving hardware structure to accommodate real-time data acquisition and processing in complex environments. The system’s software design is based on factor graph optimization algorithms, which use the global positioning data provided by BDS to constrain
In an era where technological advancements are rapid and constant, the U.S. Army will need a more agile and efficient approach to modernizing systems on succeeding generations of Army vehicles. Legacy platforms like Abrams, Stryker, and Bradley vehicles use multiple mission computers tied to individual sensors that often required the addition of “boxes” to accommodate new capabilities, which could take years to deploy and drove sustainment costs up due to vendor lock. In addition, this antiquated approach doesn't leverage data to converge effects across the formation in a multi-domain environment. Centralized, common computing as detailed in GCIA would help solve this problem, potentially linking all major subsystems and providing higher-speed processing to assess large datasets in real time with AI and ML algorithms. By using a common, open architecture computer, the Army will be able to rapidly integrate new capabilities inside one box, versus adding multiple boxes. This pivotal
In an era where technological advancements are rapid and constant, the U.S. Army will need a more agile and efficient approach to modernizing systems on succeeding generations of Army vehicles. Legacy platforms like Abrams, Stryker, and Bradley vehicles use multiple mission computers tied to individual sensors that often required the addition of “boxes” to accommodate new capabilities, which could take years to deploy and drove sustainment costs up due to vendor lock. In addition, this antiquated approach doesn’t leverage data to converge effects across the formation in a multi-domain environment. Centralized, common computing as detailed in GCIA would help solve this problem, potentially linking all major subsystems and providing higher-speed processing to assess large datasets in real time with AI and ML algorithms. By using a common, open architecture computer, the Army will be able to rapidly integrate new capabilities inside one box, versus adding multiple boxes. This pivotal
LIDAR-based autonomous mobile robots (AMRs) are gradually being used for gas detection in industries. They detect tiny changes in the composition of the environment in indoor areas that is too risky for humans, making it ideal for the detection of gases. This current work focusses on the basic aspect of gas detection and avoiding unwanted accidents in industrial sectors by using an AMR with LIDAR sensor capable of autonomous navigation and MQ2 a gas detection sensor for identifying the leakages including toxic and explosive gases, and can alert the necessary personnel in real-time by using simultaneous localization and mapping (SLAM) algorithm and gas distribution mapping (GDM). GDM in accordance with SLAM algorithm directs the robot towards the leakage point immediately thereby avoiding accidents. Raspberry Pi 4 is used for efficient data processing and hardware part accomplished with PGM45775 DC motor for movements with 2D LIDAR allowing 360° mapping. The adoption of LIDAR-based AMRs
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