Browse Topic: Vehicle integration
This paper presents the design, implementation, and validation of an aerial-launch FPV (First-Person View) drone system that was developed to provide a complex environment with flexible deployment and precise delivery capabilities. The integrated system is composed of a hybrid VTOL carrier aircraft, a number of FPV drones, and an aerial mounting / release equipment. Using the AYK-250 platform, the carrier has a vertical take-off and landing function and long-time endurance. In terms of the FPV drones, it is built upon the high performance MARK4 5-inch frame that has high agility and high payload. The release module uses a single-hook point structure with a limit stop. The FPV drones are released stably, and the separation is reliable in flight. Comprehensive flight tests proved all workflows completely, involving carrier take-off, cruise with drones mounted, sequential aerial launch, and subsequent autonomous attitude recovery and route tracking by the FPV drones. The test results confirm the system’s capability for reliable launch from an aerial platform coupled with precise guidance, establishing a credible technical solution for expanding the practical applications of FPV drones in distributed tasks. Results show that our system can be launched via an aerial platform with an accurate guide and is a viable technological solution to spread FPF Drones for operational strategies in a more distributed way.
Civil aircraft, as typical complex product systems, exhibit characteristics such as a high concentration of high-tech technologies, strong interdisciplinarity, a high level of system integration, long development cycles, substantial project investments, and complex management. During the R&D process of civil aircraft projects, there are often high risks in performance, cost, and schedule. Delays in the schedule can lead to losses in project manpower and material resources, as well as project failure. A mature objective criteria system for maturity assessment provides a reference basis for determining whether the project has reached its optimal state at a specific stage, thereby reducing project management risks and increasing the probability of project success. This research will adopt a research approach combining theoretical studies with practical case analysis. First, it will conduct extensive and in-depth investigations into various maturity models and their applications across the entire product lifecycle within relevant fields. A requirement maturity model and requirement maturity KPI (Key Performance Indicator) indicators will be established to clarify the maturity status of requirements at different development stages, enabling judgment of whether the project is ready to proceed to the next development phase. Concurrently, by developing a KPI statistical system platform integrating application servers and data processing tools, a scientific and quantitative inspection mechanism will be implemented to visualize project development progress, status, and risk data. This will provide actionable insights for project decision-making and achieve effective project management and control.
Building a trusted digital twin and decision-centric simulation ecosystem The automotive industry has been experiencing significant change and transformation. Electrification, software-defined vehicles, advanced driver assistance systems, and increasing electrical system integration are fundamentally reshaping how vehicles are designed and validated. As integration complexity continues to increase, the expectations for design cycle times are being compressed. Programs that once relied on extended validation timelines are now expected to deliver the same level of confidence in a fraction of the time. Traditional engineering workflows were built around sequential design phases, iterative simulations, and heavy reliance on physical validation. Design concepts were documented, prototypes were constructed, tests were performed, and results were compiled in reports and specifications that informed the next iteration. That approach worked well when systems were less complex and product life cycles were longer. In recent years, the volume of data, the speed of development, and the interconnected nature of modern vehicle architectures demand a different approach.
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 Ethernet, and standardized software architecture—are still maturing. To ensure safety, reliability, and cost-effectiveness, Original Equipment Manufacturers (OEMs) must therefore adopt carefully planned evolution strategy to progressively consolidate functions, realizing the zonal design step by step. This paper proposes a unified architectural framework that systematically maps the full spectrum of evolutionary paths toward zonal E/E architecture. The framework identifies major transition stages, key engineering activities, and alternative migration paths, including distributed and domain-based architectures, vertical and horizontal function integration, various domain fusion patterns, mixed E/E architecture, continuous function migration to CCP and zonal controllers, and ultimately, the full realization of zonal E/E architecture. By organizing and contrasting these evolutionary paths, the framework provides OEMs with architectural insight and practical guidance for planning low-risk, staged transition toward fully zonal E/E architecture capable of supporting next-generation Software-Defined Vehicles (SDVs).
This study presents an integrated vehicle dynamics framework combining a 12-degree-of-freedom full vehicle model with advanced control strategies to enhance both ride comfort and handling stability. Unlike simplified models, it incorporates linear and nonlinear tire characteristics to simulate real-world dynamic behavior with higher accuracy. An active roll control system using rear suspension actuators is developed to mitigate excessive body roll and yaw instability during cornering and maneuvers. A co-simulation environment is established by coupling MATLAB/Simulink-based control algorithms with high-fidelity multibody dynamics modeled in ADAMS Car, enabling precise, real-time interaction between control logic and vehicle response. The model is calibrated and validated against data from an instrumented test vehicle, ensuring practical relevance. Simulation results show significant reductions in roll angle, yaw rate deviation, and lateral acceleration, highlighting the effectiveness of the proposed approach. Overall, the framework offers a scalable and robust foundation for developing adaptive stability control systems in modern four-wheeled vehicles
Nowadays, digital instrument clusters and modern infotainment systems are crucial parts of cars that improve the user experience and offer vital information. It is essential to guarantee the quality and dependability of these systems, particularly in light of safety regulations such as ISO 26262. Nevertheless, current testing approaches frequently depend on manual labor, which is laborious, prone to mistakes, and challenging to scale, particularly in agile development settings. This study presents a two-phase framework that uses machine learning (ML), computer vision (CV), and image processing techniques to automate the testing of infotainment and digital cluster systems. The NVIDIA Jetson Orin Nano Developer Kit and high-resolution cameras are used in Phase 1's open loop testing setup to record visual data from infotainment and instrument cluster displays. Without requiring input from the system being tested, this phase concentrates on both static and dynamic user interface analysis, including screen transitions, animations, and error messages. Among the methods used are optical character recognition (OCR) for on-screen text validation, convolutional neural networks (CNNs) for screen classification, and object detection for user interface verification. Automated anomaly detection and interface behavior evaluation are made easier with this method. Phase 2 suggests integrating a Hardware-in-the-Loop (HIL) simulator to transform the system into a closed-loop testing environment. The vision-based system will assess system responsiveness and end-to-end behavior, while the HIL setup will produce simulated user inputs and vehicle network data (such as CAN, Ethernet). This thorough framework tackles important issues like complex system integration, multimodal interaction testing, and managing cognitive load. In order to support the creation of safer, more user-friendly infotainment and digital cluster systems that are in line with Advanced Driver Assistance Systems (ADAS) standards, it seeks to decrease the amount of manual testing effort, increase test coverage, and improve consistency.
The integration of Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) has transformed various industries, offering substantial benefits. The application of these technologies in engine reliability testing has immense potential as they offer real-time monitoring and analysis of engine performance parameters. Engine reliability testing is vital for ensuring the safety, efficiency, and longevity of engines. Traditional methods are time consuming, expensive, and rely heavily on manual inspection and data analysis. This paper shows how IoT and ML technologies can enhance the efficiency of engine reliability testing. The paper includes the following case studies:
Modern vehicle integration has become exponentially more difficult due to the complicated structure of designing wiring harnesses for multiple variants that have diverse design iterations and requirements. This paper proposes an AI-driven solution for addressing variant complexity. By using Convolutional Networks and Deep Neural Networks (CNN & DNN) to generate harness routing using defined specifications and constraints, the proposed solution uses minimal human intervention, substantially less time, and enables less complexity in designing. AI trained modelled systems can generally even predict failures in production methods which also reduces downtime and increases productivity. The new AI system automatically converts design specifications to manufacturable design specifications to avoid confusion with design parameters, by optimizing concepts with connector placements, grommet fittings, clip alignments, and other tasks. The solution coping with the inherent dynamic complexity of variant design, is developed to learn the unique design constraints and updates in real-time detailed in a new framework. As opposed to another static master/slave co-ordinate system, this dynamic AI system takes input parameters like but not limited to; the routing through the shortest spline path of an area with geometry and takes that information to automatically develop a harness network based on practical, and most simply possible design. The learning algorithms allows for intelligently scalable designs through truck variant capability optimization. Continual integration occurs at order booking which allows specific order requirements to automatically integrate into the designs. The system continues to manage the process to ensure the design performs optimally. By removing manual intervention and allowing to automatically adapt to variant configurations, this AI system transforms the wiring harness design process and enhances the scalability of production processes. This research proposes a novel solution for reductions in variant complexity, in a scalable developed from the time being reasonable and accurate harness design approach to the wiring harness for modern trucks.
Thermal Management System (TMS) for Battery Electric Vehicles (BEV) incorporates maintaining optimum temperature for cabin, battery and e-powertrain subsystems under different charging and discharging conditions at various ambient temperatures. Current methods of thermal management are inefficient, complex and lead to wastage of energy and battery capacity loss due to inability of energy transfer between subsystems. In this paper, the energy consumption of an electric vehicle's thermal management system is reduced by a novel approach for integration of various subsystems. Integrated Thermal Management System (ITMS) integrates air conditioning system, battery thermal management and e-powertrain system. Characteristics of existing integration strategies are studied, compared, and classified based on their energy efficiency for different operating conditions. A new integrated system is proposed with a heat pump system for cabin and waste heat recovery from e-powertrain. Various cooling and heating strategies for battery are identified for different ambient temperatures. An ITMS valve functioning is explained for each scenario depending on vehicle operating condition and ambient temperature.
In both internal combustion engine (ICE) and electric vehicles, Heating, Ventilation, and Air Conditioning (HVAC) systems have become significant contributors to in-cabin noise. Although significant efforts have been made across the industry to reduce noise from airflow handling systems, especially blower noise. Nowadays, original equipment manufacture’s (OEMs) are increasingly focusing on mitigating noise generated by refrigeration handling systems. Since the integration of refrigeration components is vital for the overall Noise Vibrations and Harshness (NVH) refinement of a vehicle, analysing the impact of each HVAC component during vehicle-level integration is essential. This study focused on optimizing the NVH performance of key refrigeration components, including the AC compressor, thermal expansion valve (TXV), suction pipe, and discharge line. The research began with a theoretical investigation of the primary noise and vibration sources, particularly the compressor and TXV, followed by an analysis of vibration transmission paths through the refrigerant lines. To ensure an optimal acoustic and thermal balance among these four components, both design parameters and dynamic operating characteristics were studied for their impact on thermoacoustic performance inside the vehicle. The compressor was identified as a major source of low and mid frequency noise and vibration, while pressure pulsations in the refrigerant lines contributed to structure-borne and airborne noise. These issues were addressed by developing new design guidelines aimed at improving isolation and damping characteristics. Specific efforts included designing stair-gated modal decoupling strategies to avoid resonance between the compressor bracket and engine or aggregate excitation frequencies. In addition, the standing wave behaviour in the suction and discharge lines was analysed to identify and control resonant modes that amplified NVH issues. The TXV was also studied in detail, with a particular focus on mid-frequency noise caused by its internal dynamics. Parameters such as spring stiffness, natural frequency, and superheat setting behaviour were optimized to improve cabin acoustic comfort. The outcome of this paper is a comprehensive component-level NVH validation combined with practical design guidelines for minimizing integration-related noise and vibration issues in HVAC systems. These findings provide a robust framework for engineers to enhance both thermal performance and in-cabin acoustic refinement, ensuring superior comfort in modern vehicles.
This research is dedicated to exploring the application of large language models in the Beijing Subway scientific research project management platform. It conducts a thorough analysis of many key elements, including the application background, technical support, practical achievements, and future development paths. With the continuous development of the Beijing Subway construction scale, the number and complexity of scientific research projects have been gradually increasing. Traditional management models are getting more and more insufficient in dealing large amounts of data, complicated processes, and precise decision-making requirements. By using natural language processing, machine learning, knowledge graph pedigreestechnological and technical model related technologies, which are very different from the one of the most inventive ones, are presented. The objective of intelligence is to solve this model by automatically analyzing papers with a logical and scientific approach and logically forecast project development and cost. This not only greatly increases managerial efficiency; it also puts more and more rationalization into the industry, which is why the intelligent development of the business is encouraged to some extent and makes decision-making in a way that is both more sensible. But there are still some problems that the use of these kinds of models has to be done. Issues concerning the quality of data; these included data that was either inaccurate; these had an effect on the models’ repeating. Numerous research organizations are also severely financially dependent because of the volume of data that is used; these problems are made worse by technical difficulties. Furthermore, because of the variations between the various types of data and the incompatible interfaces, the system integration operation is very complicated. These barriers are predicted to be resolved by huge language models in the future. They might be more intelligent and include many kinds of data in their integration. The rail transportation industry is going to see a greater use of this development in research and development, which will give the rail transit business a more complete view and more knowledgeable decision-making.
Ground vehicle software continues to increase in cost and complexity, in part driven by tightly integrated systems and vendor lock-in. One method of reducing costs is reuse and portability, encouraged by the Modular Open Systems Approach and the Future Airborne Capability Environment (FACE) architecture. While FACE provides a Conformance Testing Suite to ensure portability between compliant systems, it does not verify that components correctly implement standard interfaces and desired functionality. This paper presents a layered test methodology designed to ensure that a FACE component correctly implements working communication interfaces, correctly handles the full range of data the component is expected to manage, and correctly performs all of the functionality the component is required to perform. This testing methodology includes unit testing of individual components, integration testing across multiple units, and full hardware in the loop system integration testing, offering a structured approach to validating FACE conformant components beyond formal conformance.
This paper presents a model-based systems engineering (MBSE) and digital twin approach for a military 6T battery tester. A digital twin architecture (encompassing product, process, and equipment twins) is integrated with AI-driven analytics to enhance battery defect detection, provide predictive diagnostics, and improve testing efficiency. The 6T battery tester’s MBSE design employs comprehensive SysML models to ensure traceability and robust system integration. Initial key contributions include early identification of battery faults via impedance-based sensing and machine learning, real-time state-of-health tracking through a synchronized virtual battery model, and streamlined test automation. Results indicate the proposed MBSE/digital twin solution can detect degradation indicators (e.g. capacity fade, rising internal impedance) earlier than traditional methods, enabling proactive maintenance and improved operational readiness. This approach offers a reliable, efficient testing framework aligning with military requirements for safety and performance in 6T battery sustainment.
Engineering precision is an art of nuance — especially when it comes to selecting the right bearing for medical devices. What begins as a straightforward specification process quickly becomes a complex yet familiar puzzle of competing requirements. Oftentimes, engineers discover that a bearing’s performance extends beyond its basic dimensional specs, involving considerations of material properties, system integration and supply chain dynamics.
U.S. Army Combat Capabilities Development Command (DEVCOM), Aviation & Missile Center (AvMC) developed a Digital Backbone for the Rotorcraft Applied Systems Concepts Airborne Lab (RASCAL-X) UH-60M for rapid Modular Open Systems Approach (MOSA) mission system integrations. The RASCAL-X Digital Backbone is the cornerstone of a unique experimental flight test capability connecting the experimental research flight control system with the Mission Systems Flying Testbed (MSFTB) and other mission system components. The Digital Backbone with MSFTB provides a suite of capabilities to integrate, assess, and flight test Mission Systems Under Test. The RASCAL-X Digital Backbone supports many of the physical aspects of mission system integration by providing Nodal Points with provisioning for power, data, and connectivity. Numerous challenges in Digital Backbone design, fabrication and installation were successfully addressed and solved during the development effort. The RASCAL-X Digital Backbone installation was completed in February 2025.
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 remaining aircraft systems can still be tested by interacting with the model.
Model-Based Systems Engineering (MBSE) enables requirements, design, analysis, verification, and validation associated with the development of complex systems. Obtaining data for such systems is dependent on multiple stakeholders and has issues related to communication, data loss, accuracy, and traceability which results in time delays. This paper presents the development of a new process for requirement verification by connecting System Architecture Model (SAM) with multi-fidelity, multi-disciplinary analytical models. Stakeholders can explore design alternatives at a conceptual stage, validate performance, refine system models, and take better informed decisions. The use-case of connecting system requirements to engineering analysis is implemented through ANSYS ModelCenter which integrates MBSE tool CAMEO with simulation tools Motor-CAD and Twin Builder. This automated workflow translates requirements to engineering simulations, captures output and performs validations. System Architecture Model is created in CAMEO with requirements and structure diagram. Motor-CAD is used to calculate motor performance and efficiency map. Twin Builder is used to develop an integrated system (EV) model and calculate vehicle level performance characteristics such as vehicle range, acceleration and gradeability etc. Trade studies are performed to evaluate design alternatives. Ansys ModelCenter empowers engineers and decision makers by providing early requirement verification capabilities thereby reducing re-work and enhancing efficiency in product development.
Over the decades, robotics deployments have been driven by the rapid in-parallel research advances in sensing, actuation, simulation, algorithmic control, communication, and high-performance computing among others. Collectively, their integration within a cyber-physical-systems framework has supercharged the increasingly complex realization of the real-time ‘sense-think-act’ robotics paradigm. Successful functioning of modern-day robots relies on seamless integration of increasingly complex systems (coming together at the component-, subsystem-, system- and system-of-system levels) as well as their systematic treatment throughout the life-cycle (from cradle to grave). As a consequence, ‘dependency management’ between the physical/algorithmic inter-dependencies of the multiple system elements is crucial for enabling synergistic (or managing adversarial) outcomes. Furthermore, the steep learning curve for customizing the technology for platform specific deployment discourages domain experts from rapid prototyping and validation of the technological piece. This creates a need for frameworks that can provide adequate compartmentalization for domain experts (to carry out platform agnostic research) and yet permit flexible encapsulation of multiple robotic code deployment architectures (legacy or otherwise). In this work, we explore various facets of these challenges for autonomous operations with a simulated/physical Clearpath Husky robot by developing Robot Operating System (ROS) based Docker containers, that isolate different functions of the robot operations and yet interact with each other in real-time for a synergistic deployment.
Automotive chassis components are considered as safety critical components and must meet the durability and strength requirements of customer usage. The cases such as the vehicle driving through a pothole or sliding into a curb make the design (mass efficient chassis components) challenging in terms of the physical testing and virtual simulation. Due to the cost and short vehicle development time requirement, it is impractical to conduct physical tests during the early stages of development. Therefore, virtual simulation plays the critical role in the vehicle development process. This paper focuses on virtual co-simulation of vehicle chassis components. Traditional virtual simulation of the chassis components is performed by applying the loads that are recovered from multi-body simulation (MBD) to the Finite Element (FE) models at some of the attachment locations and then apply constraints at other selected attachment locations. In this approach, the chassis components are assessed separately from the vehicle environment. The MBD model predicts the dynamic behavior of the motions of the flexible bodies (subframe, control arms, knuckle, wheel, yoke, tie rods, etc.) that are connected to each other through kinematic constraints / joints / contacts. The loads from MBD model do not consider the energy loss due to plastic deformation of the chassis components when the vehicle goes through a pothole or slides to a curb. To accurately predict chassis component performance, an integrated vehicle system model is needed. An FE-based full vehicle model has its challenges: (1) time consuming to build, (2) model is too large if all kinematic constraints / joints / contacts / tires are considered, or (3) cannot “drive” through the desired road. A tightly coupled co-simulation between MBD and FE model can overcome these inherent challenges. Co-simulation using Simpack and Abaqus is an ideal combination of solvers which combines the benefits of a high fidelity, detailed system level response and highly accurate Abaqus non-linear solution using plasticity and damage material models. This paper depicts case studies of Simpack-Abaqus co-simulation for chassis components under various extreme loading events performed.
The automotive subframe, also referred to as a cradle, is a critical chassis structure that supports the engine/electric motor, transmission system, and suspension components. The design of a subframe requires specialized expertise and a thorough evaluation of performance, vehicle integration, mass, and manufacturability. Suspension attachments on the subframe are integral, linking the subframe to the wheels via suspension links, thus demanding high performance standards. The complexity of subframe design constraints presents considerable challenges in developing optimal concepts within compressed timelines. With the automotive industry shifting towards electric vehicles, development cycles have shortened significantly, necessitating the exploration of innovative methods to accelerate the design process. Consequently, AI-driven design tools have gained traction. This study introduces a novel AI model capable of swiftly redesigning subframe concepts based on user-defined raw concepts. By leveraging design data from previous subframe projects, this model enhances the manufacturability and performance of user input designs by integrating validated features from past concepts. The implementation of this AI model results in significant reductions in design development time, thereby improving efficiency. Additionally, this paper provides a detailed analysis of the time and cost savings achieved through the adoption of this AI model throughout the design development phase.
Many eVTOL and electric aircraft systems are highly sensitive to battery performance, states, algorithms and behavior, which necessitates thorough testing. However, lab testing with real battery packs is not practical or desirable in most cases due to potential issues with safety, availability, cost, energy, and test coverage. A battery pack surrogate hard-ware-in-the-loop (HIL) test system may be used in place of the battery in order to safely and efficiently test many battery-sensitive aircraft systems across an extended range of battery conditions. This paper describes an 800V battery pack surrogate comprised of commercially available components including battery cell simulators providing over 200 cells of simulation, as well as signal and power IO, communications, cell models, a real-time controller, and battery management system (BMS). Important design considerations including safety, isolation, topologies, and interconnections are addressed, and applications for systems integration lab (SIL) and iron bird testing are presented.
Automotive radar plays a crucial role in object detection and tracking. While a standalone radar possesses ideal characteristics, integrating it within a vehicle introduces challenges. The presence of vehicle body, bumper, chassis, and cables in proximity influences the electromagnetic waves emitted by the radar, thereby impacting its performance. To address these challenges, electromagnetic simulations can guide early-stage design modifications. However, operating at very high frequencies around 77GHz and dealing with the large electrical size of complex structures demand specialized simulation techniques to optimize radar integration scenarios. Thus, the primary challenge lies in achieving an optimal balance between accuracy and computational resources/simulation time. This paper outlines the process of radar vehicle integration from an electromagnetic perspective and demonstrates the derivation of optimal solutions through RF simulation.
The once rarified field of Artificial Intelligence, and its subset field of Machine Learning have very much permeated most major areas of engineering as well as everyday life. It is already likely that few if any days go by for the average person without some form of interaction with Artificial Intelligence. Inexpensive, fast computers, vast collections of data, and powerful, versatile software tools have transitioned AI and ML models from the exotic to the mainstream for solving a wide variety of engineering problems. In the field of braking, one particularly challenging problem is how to represent tribological behavior of the brake, such as friction and wear, and a closely related behavior, fluid consumption (or piston travel in the case of mechatronic brakes), in a model. This problem has been put in the forefront by the sharply crescendo-ing push for fast vehicle development times, doing high quality system integration work early on, and the starring role of analysis-based tools in enabling this strategy. Focusing even further, brake corner systems under duress – such as high temperatures, and high braking power, can exhibit highly non-linear and in-stop varying behavior that can be exceedingly difficult to model accurately. The present work chronicles efforts by the author and colleagues to develop machine learning models that capture this complex behavior and generalize sufficiently well to continue representing the performance of the brake under high energy driving conditions, even as the models are presented with new braking conditions that were not part of the training of the models. The utility of the models in the prediction of system-level performance is demonstrated through a case study application to calibrating a fade warning feature. The present work is shown from the perspective of a practicing engineer, not a data scientist, with some details that may prove mundane to the latter – but a strong motivation behind this work is to share the experience of getting started and some practical lessons learned towards the use of these powerful machine learning tools to solving practical problems in the field of brake engineering.
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