Browse Topic: Vehicle integration
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
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
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
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
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
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
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
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
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.
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
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
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
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
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
Today, the battery development process for automotive applications is relatively decoupled from the vehicle integration and system validation phase. Battery pack design targets are often disregarded at very early development phases even though they are thoroughly linked to the vehicle-level requirements such as performance, lifetime and cost. Here, AVL proposes a methodology guided by virtual testing techniques to frontload vehicle-level validation tasks in the earlier phase of battery pack testing. This paper focuses on the benefits of the methodology for both battery suppliers and automotive OEMs. Applications will be explained, based on a modular virtual testing toolchain, which involves the simulation platform and models as well as the generation of model parameters and test cases.
This standard only defines interconnect, electrical and logical (functional) requirements for the interface between a Micro Munition and the Host. The physical and mechanical interface between the Micro Munition and Host is undefined. Individual programs will define the relevant requirements for physical and mechanical interfaces in the Interface Control Document (ICD) or system specifications. It is acknowledged that this does not guarantee full interoperability of Interface for Micro Munitions (IMM) interfaces until further standardization is achieved.
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