Browse Topic: Product development
As global warming and environmental problems are becoming more serious, tires are required to achieve a high level of performance trade-offs, such as low rolling resistance, wet braking performance, driving stability, and ride comfort, while minimizing wear, noise, and weight. However, predicting tire wear life, which is influenced by both vehicle and tire characteristics, is technically challenging so practical prediction method has long been awaited. Therefore, we propose an experimental-based tire wear life prediction method using measured tire characteristics and the wear volume formula of polymer materials. This method achieves practical accuracy for use in the early stages of vehicle development without the need for time-consuming and costly real vehicle tests. However, the need for improved quietness and compliance with dust regulations due to vehicle electrification requires more accuracy, leading to an increase in cases requiring judgment through real vehicle tests. To address
This paper presents a Digital Twin approach based on Machine Learning (ML), aimed at creating software-based sensors to reduce the auxiliary devices of the vehicle and enabling predictive maintenance, thus reducing carbon footprint. The solution is applied to the electric Lubrication Oil Pump (eLOP), a crucial component within a vehicle's powertrain system. The proposed eLOP Digital Twin integrates ML-based sensors to estimate critical parameters such as temperature, pressure and flow rate, reducing the reliance on physical sensors and associated hardware. This approach minimizes manufacturing complexity and cost, enhancing energy efficiency during both production and operation. Furthermore, the Digital Twin facilitates predictive maintenance by continuously monitoring the component's performance, enabling early detection of potential failures and optimizing maintenance schedules. This leads to lower energy consumption and reduced emissions throughout the component's lifecycle. The
In the modern automotive industry, improving fuel efficiency while reducing carbon emissions is a critical challenge. To address this challenge, accurately measuring a vehicle’s road load is essential. The current methodology, widely adopted by national guidelines, follows the coastdown test procedure. However, coastdown tests are highly sensitive to environmental conditions, which can lead to inconsistencies across test runs. Previous studies have mainly focused on the impact of independent variables on coastdown results, with less emphasis on a data-driven approach due to the difficulty of obtaining large volumes of test data in a short period, both in terms of time and cost. This paper presents a road load energy prediction model for vehicles using the XGBoost machine learning technique, demonstrating its ability to predict road load coefficients. The model features 27 factors, including rolling, aerodynamic, inertial resistance, and various atmospheric conditions, gathered from a
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
Interest in Battery-Driven Electric Vehicles (EVs) has significantly grown in recent years due to the decline of traditional Internal Combustion Engines (ICEs). However, malfunctions in Lithium-Ion Batteries (LIBs) can lead to catastrophic results such as Thermal Runaway (TR), posing serious safety concerns due to their high energy release and the emission of flammable gases. Understanding this phenomenon is essential for reducing risks and mitigating its effects. In this study, a digital twin of an Accelerated Rate Calorimeter (ARC) under a Heat-Wait-and-Seek (HWS) procedure is developed using a Computational Fluid Dynamics (CFD) framework. The CFD model simulates the heating of the cell during the HWS procedure, pressure build-up within the LIB, gas venting phenomena, and the exothermic processes within the LIB due to the degradation of internal components. The model is validated against experimental results for an NCA 18650 LIB under similar conditions, focusing on LIB temperature
Electric motors are critical components in Electric Vehicle (EV) & industrial applications. In case of EVs electric motor has a direct impact on the functionality, range and general user experience. Traditional maintenance procedures have several major limitations such as, leaving no choice but to use the expensive warranty claims, restricted predictive maintenance, unavailability of useful data, reducing resale value, and ultimately poor customer satisfaction. The process of building a virtual duplicate of an actual motor that can replicate the physical system in real time is known as the Digital Twin (DT) technology. Here, the DT technology-based monitoring and maintenance is initiated on permanent magnet synchronous motor (PMSM) used in traction, thus helping to overcome the drawbacks of traditional maintenance system. To provide a holistic approach to real time motor monitoring, motor management, ensuring enhanced reliability, efficiency, and predictive maintenance capabilities
The engineering design process employs an iterative approach in which proposed solutions are conceived, evaluated and refined until they satisfy a priori requirements - specifications. This iterative cycle generally uses computer aided designs (CAD), engineering analysis (CAE), numerical simulations per operating scenarios, and laboratory or field prototype testing. The availability of product data can be applied to assess the vehicle requirements – specifications to facilitate the next generation design. However, the calibration and use of a digital twin facilitates exploration of tradeoffs between engineering design, product manufacturing, and business demands, plus a desire to shorten the overall time. For instance, digital twin technology enables the swift evaluation of vehicle performance in various configurations and operating conditions. The question arises of how to best integrate digital twin technology into the design process. This paper will review the engineering design
The automotive industry is amidst an unprecedented multi-faceted transition striving for more sustainable passenger mobility and freight transportation. The rise of e-mobility is coming along with energy efficiency improvements, greenhouse gas and non-exhaust emission reductions, driving/propulsion technology innovations, and a hardware-software-ratio shift in vehicle development for road-based electric vehicles. Current R&D activities are focusing on electric motor topologies and designs, sustainability, manufacturing, prototyping, and testing. This is leading to a new generation of electric motors, which is considering recyclability, reduction of (rare earth) resource usage, cost criticality, and a full product life-cycle assessment, to gain broader market penetration. This paper outlines the latest advances of multiple EU-funded research projects under the Horizon Europe framework and showcases their complementarities to address the European priorities as identified in the 2Zero
E-mobility is revolutionizing the automotive industry by improving energy-efficiency, lowering CO2 and non-exhaust emissions, innovating driving and propulsion technologies, redefining the hardware-software-ratio in the vehicle development, facilitating new business models, and transforming the market circumstances for electric vehicles (EVs) in passenger mobility and freight transportation. Ongoing R&D action is leading to an uptake of affordable and more energy-efficient EVs for the public at large through the development of innovative and user-centric solutions, optimized system concepts and components sizing, and increased passenger safety. Moreover, technological EV optimizations and investigations on thermal and energy management systems as well as the modularization of multiple EV functionalities result in driving range maximization, driving comfort improvement, and greater user-centricity. This paper presents the latest advancements of multiple EU-funded research projects under
Electric vehicles rely on accurate estimation of battery states to operate safely and efficiently. Traditionally, the state estimation is pack level and based on empirical models developed to capture the dynamics of a representative battery pack and hence falls short in accounting for cell-to-cell variations. These variations become more pronounced as the cells age within a battery pack under non-homogeneous mechanical, thermal, manufacturing, and electrical conditions. It is challenging to adapt the traditional physics-based model to changing battery dynamics in real-time. To improve the state estimation at the cell level, a data-driven approach utilizing streamed data from vehicles enabled by connectivity has been shown in this paper. While traditional data-driven approaches result in large models and require large quantities of data for training, the proposed method relies on combining the underlying physics of the electrochemical model with novel data-driven modeling techniques
China Automotive Engineering Research Institute Co., Ltd (CAERI) has completed a new vehicle aero-acoustic wind tunnel (AAWT), which is located in Chongqing, China, and has been in operation for 5 years. To help addressing the Chinese vehicle market’s need to improve fuel economy, reduce exhaust emissions, and decrease product development period, the wind tunnel was designed and implemented to achieve a high degree of automation for vehicle testing next to a high aerodynamic and acoustic test accuracy for product development. The CAERI wind tunnel was in operation in June 2019, achieving a top speed of 250 km/h. A 5-belt rolling road system with a long center belt for proper wake simulation is installed inside, a test section with very low static pressure gradient and background noise. Wind tunnel calibration and customized measurement activities can be performed with an overhead traversing system. In the present paper, the main facilities of the AAWT are described next to necessary
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
Over recent years, BorgWarner has intensified its efforts to explore and leverage trending technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to enhance products and processes. This includes digital twin technology, which has potential use cases for system behavior analysis, product optimization and predictive maintenance. This paper outlines the development process of a digital twin for a commercial vehicle battery, which serves as a demonstrator and learning platform for this technology. In order to assess the feasibility as well as hard- and software requirements, a cloud-based digital twin demonstrator was developed, integrating vehicle telemetry data with physics-based battery electric and thermal models, and an aging prediction algorithm. The key components are an Internet of Things (IoT) gateway, simulation models, data processing and ingestion pipelines, a machine learning algorithm for anomaly detection, and visualizations of telemetry and simulation
In recent years, simulation-based performance of the models is a highly effective way to finalize the model at design stage itself. But simulation-based models are complex owing to more parameters involved hence resulting in more computational time. With the increasing demand for electric vehicles, the development time for electric vehicle (EV) powertrain is reduced, thereby increasing pressure on original equipment manufacturers (OEMs) to develop products faster. Digital twin is a platform where replication of physical models is made possible with extremely limited data to predict the performance of the model hence providing the most accurate results in a short time. Electric vehicles are the best alternatives for reducing emissions. An Electric vehicle is run by an electric motor which in turn is powered by a battery. Interior permanent magnet synchronous motors (IPMSMs) are the conventional type of motors in electric vehicles because of their high-power density and efficiency. This
Steering feeling plays a critical role in the driving experience and is one of the most significant topics during a new vehicle development process. To reach a consensus for the customers’ satisfaction in both the subjective and objective characteristics in a particular market segment, there have been several studies to investigate the correlations between subjective and objective evaluations of on-center steering feeling. However, it is still not clear how to determine the steering characteristic based on the correlations. In this paper, a series of new correlations between subjective and objective evaluations are built, which focus on steering stiffness, on-center feel, torque symmetry, torque ripple etc. Firstly, a set of objective metrics which are followed by professional test drivers or tuning experts have been extracted from 12 vehicles’ on-center handling test based on ISO 13674-1 2023, these vehicles covering different motorcycle type, various brands and diverse tuning styles
The intensive use of software applications in modern vehicles has highlighted the critical role of Systems Engineering (SE) in the automotive industry. These “computers on wheels” are thoroughly interconnected, by their own connections and with the cloud, due to the advancement of Electronic Control Units (ECU) technologies and the widespread use of sensors transmitting real-time data. This interconnectedness and the level of software abstraction that are known today, significantly escalates the complexity of these systems. This has made it necessary to adopt an approach that is flexible to change, structured, agile, and traceable. The modern approach to SE, now model-based, offers numerous advantages over the previous paradigm, which was predominantly document-based. MBSE (Model-Based Systems Engineering) emerges as a contemporary approach, providing the scalability needed for engineering teams to develop robust products. Its “model-based” essence ensures that the model acts as the
In the automotive industry, there have been many efforts of late in using Machine Learning tools to aid crash virtual simulations and further decrease product development time and cost. As the simulation world grapples with how best to incorporate ML techniques, two main challenges are evident. There is the risk of giving flawed recommendations to the design engineer if the training data has some suspect data. In addition, the complexity of porting simulation data back and forth to a Machine Learning software can make the process cumbersome for the average CAE engineer to set up and execute a ML project. We would like to put forth a ML workflow/platform that a typical CAE engineer can use to create training data, train a PINN (Physics Informed Neural Network) ML model and use it to predict, optimize and even synthesize for any given crash problem. The key enabler is the use of an industry first data structure named mwplot that can store diverse types of training data - scalars, vectors
The vehicle wake region is of high importance when analyzing the aerodynamic performance of a vehicle. It is characterized by turbulent separated flow and large low-pressure regions that contribute significantly to drag. In some cases, the wake region can oscillate between different modes which can pose an engineering challenge during vehicle development. Vehicles that exhibit bimodal wake behavior need to have their drag values recorded over a sufficient time period to take into account the low frequency shift in drag signal, therefore, simulating such vehicle configurations in CFD could consume substantial CPU hours resulting in an expensive and inefficient vehicle design iterations process. As an alternative approach to running simulations for long periods of time, the impact of adding artificial turbulence to the inlet on wake behavior and its potential impact on reduced runtime for design process is investigated in this study. By adding turbulence to the upstream flow, the wake
Physical testing is required to assess multiple vehicles in different conditions, specially to validate those related to regulations. The acoustic evaluations have difficulties and limitations in physical test; cost and time represent important considerations every time. Additionally, the physical validation happens once a prototype has been built, this takes place in a later phase of the development. Sound pressure is measured to validate different requirements in a vehicle, horn sound is one of these and it is related to a regulation of united nations (ECE28). Currently the validation happens in physical test only and the results vary depending on the location of the horn inside the front end of every vehicle. [7] In this article, the work for approaching a virtual validation method through CAE is presented with the intention to get efficiency earlier in product development process.
Following early adoption, the BEV market has shifted towards a mass market strategy, emphasizing on crucial attributes, such as system cost reduction and range extension. System efficiency is crucial in BEV product development, where efficiency metric influenced greatly vehicle range and cost. For instance, higher iDM efficiency reduces the need for larger battery, cutting cost, or extends range with the same battery size. BorgWarner adopted Digital Twin technology to optimize Integrated Drive Module (iDM) within a vehicle ecosystem. Digital Twin comprises high-fidelity physics based numerical tool suites offering greater degree of freedom to engineers in designing, sizing, optimizing a component versus system benefit tradeoff, thus enabling most efficient product design within economic constraints. BorgWarner’s Analytical System Development (ASD) plan used as framework provides a global unified process for tool development and validation, ensuring the digital print of a real product
This standard documents what is required to execute a System Theoretic Process Analysis (STPA) of safety-critical products or systems in all industries. This standard defines the terminology, the steps in using STPA, the activities flow, and the expected deliverables. This standard may be used when addressing compliance with contractual or regulatory requirements regarding risk assessments, safety assessments, development assurance, system security engineering, or other similar requirements as appropriate. In addition, this standard can be used to demonstrate that an effective STPA evaluation has been conducted when compliance is not of paramount concern. This standard is applicable to a broad set of uses including, but not limited to, corporate product development processes, organizational processes, regulatory groups, supplier processes, defense programs (e.g., government awards a contract to a company and the contract mandates STPA), defense program office (e.g., government safety
Developing safe and reliable autonomous vehicles is crucial for addressing contemporary mobility challenges. While the goal of autonomous vehicle development is full autonomy, up to SAE Level 4 and beyond, human intervention remains necessary in critical or unfamiliar driving scenarios. This article introduces a method for gracefully degrading system functionality and seamlessly transferring decision-making and control between the autonomous system and a remote safety operator when needed. This transfer is enabled by an onboard dependability cage, which continuously monitors the vehicle’s performance during its operation. The cage communicates with a remote command control center, allowing for remote supervision and intervention by a safety driver. We assess this methodology in both lab and test field settings in a case study of last-mile parcel delivery logistics and discuss the insights and results obtained from these evaluations.
To facilitate the construction of a robust transport infrastructure, it is essential to implement a digital transformation of the current highway system. The concept of digital twins, which are virtual replicas of physical assets, offers a novel approach to enhancing the operational efficiency and predictive maintenance capabilities of highway networks. The present study begins with an exhaustive examination of the demand for the smart highway digital twin model, underscoring the necessity for a comprehensive framework that addresses the multifaceted aspects of digital transformation. The framework, as proposed, is composed of six integral components: spatiotemporal data acquisition and processing, multidimensional model development, model integration, application layer construction, model iteration, and model governance. Each element is critical in ensuring the fidelity and utility of the digital twin, which must accurately reflect the dynamic nature of highway systems. The
Noise, Vibration, and Harshness (NVH) simulations of vehicle bodies are crucial for assessing performance during the design phase. However, these simulations typically require detailed computer-aided design (CAD) models and are time-consuming. In the early stages of vehicle development, when only high-level vehicle sections are available, designing the body-in-white (BIW) structure to meet target values for bending and torsional stiffness is challenging and often requires multiple iterations. To address these challenges, this study deploys a reduced-order beam modelling approach. This method involves identifying the beam-like sections and major joints within the BIW and calculating their sectional properties (area, area moments of inertia along the plane’s independent axes, and torsion constant). These components form a simplified skeleton model of the BIW. Load and boundary conditions are applied to the suspension mount locations at the front and rear of the vehicle, and torsional and
India has seen a significant boost in automotive research and development, specific to Vehicle Dynamics active safety systems and ADAS. To develop these systems, without excessive reliance on full working prototypes, vehicle manufacturers are relying on virtual models to better fine tune the design parameters. For this, there is a real requirement of digital twins of the proving grounds. This virtual testing surfaces will help in reducing test costs, test times and increase iteration counts, leading to fine-tuned prototype vehicle and finally a market leading product. National Automotive Test Tracks (NATRAX) is already playing a crucial role in the testing and development of these technologies, on its test tracks. Recognizing the need to assist in virtual testing for Indian automotive manufacturers, NATRAX is taking steps to develop virtual proving grounds to complement physical testing and reduce the development time. This paper targets a comparative analysis of dynamic parameters
The increasing reliance on lithium-ion batteries in manufacturing necessitates advanced monitoring techniques to ensure their longevity and reliability. Cloud technology offers a solution by enabling real-time data collection, analysis, and accessibility, facilitating thorough monitoring and predictive maintenance. Digital twin technology, creating a virtual replica of the physical battery system, provides a platform for simulating real-world conditions and predicting potential issues before they arise. By integrating sensor data and historical usage patterns, the digital twin model can accurately predict battery degradation, aiding in timely maintenance strategies. This proactive approach enhances battery operational efficiency and extends lifespan, leading to cost savings and improved safety. The paper explores using cloud-based monitoring systems to enhance the health estimation and management of lithium-ion batteries. A comprehensive feasibility study on adopting battery digital
In electric vehicles development, manufacturing variations pose big challenge in designing various mechanical components as these variations directly impact various customer perceivable performance outputs. If the manufacturing variations can be included in design phase itself, overall robustness of the design can be enhanced. This paper delineates machine learning based methods to include manufacturing variations in designing drive units for electric vehicles. In an electric vehicle, the drive unit transfers power or torque from a battery through an inverter to wheels. The drive units are subjected to different types of loads under various vehicle maneuvers. To evaluate the drive unit system virtually, system level simulations are performed. Traditionally, nominal values of the several inputs such as bearing parameters, gear parameters and clearances etc. are used. However, the drive unit must be designed in such a way that outputs meet target considering all the variations of inputs
Systems Engineering is a method for developing complex products, aiming to improve cost and time estimates and ensure product validation against its requirements. This is crucial to meet customer needs and maintain competitiveness in the market. Systems Engineering activities include requirements, configuration, interface, deadlines, and technical risks management, as well as definition and decomposition of requirements, implementation, integration, and verification and validation testing. The use of digital tools in Systems Engineering activities is called Model-Based Systems Engineering (MBSE). The MBSE approach helps engineers manage system complexity, ensuring project information consistency, facilitating traceability and integration of elements throughout the product lifecycle. Its benefits include improved communication, traceability, information consistency, and complexity management. Major companies like Boeing already benefit from this approach, reducing their product
The path towards clean mobility points in the direction of battery electric vehicles (BEVs) as a possible transportation solution. Despite a growing market penetration worldwide, emerging countries are struggling to successfully adopt BEV with current vehicle models. The literature presents an embracing discussion about BEV barriers but lacks into suggesting practical actions into BEV design. Based on a product development methodology and value analysis, this research aims to review factors holding back the BEV adoption in developing countries and to apply these factors into BEV features and design specifications. The literature was systematically reviewed based on the Brazilian case scenario to cast customer requirements for numerical evaluation through the Mudge Method. These were later translated into design requirements and ranked according to their relative importance with the quality function deployment (QFD). The results show that vehicle safety, pricing, and range anxiety are
Autonomous vehicles for mining operations offer increased productivity, reduced total cost of ownership, decreased maintenance costs, improved reliability, and reduced operator exposure to harsh mining environments. A large flow of data exists between the remote operation and the ore haul vehicle, and part of the data becomes information for the maintenance sector which it monitors the operating conditions of various systems. One of the systems deserving attention is the suspension system, responsible for keeping the vehicle running and within a certain vibration condition to keep the asset operational and productive. Thus, this work aims to develop a digital twin-assisted system to evaluate the harmonic response of the vehicle’s body. Two representations were created based on equations of motion that modeled the oscillatory behavior of a mass-damper system. One of the representations indicates a quarter of the ore transport truck’s hydraulic system in a healthy state, called a virtual
Throughout the years, the legislations which drive the vehicle development have experimented constant evolutions. Especially when it comes about pollutant emissions and NVH ( Noise, Vibration & Harshness). However, it is complex to understand which calibration strategy promotes the best balance about lowest levels of emissions, vibrations, and noise if considered the number of inputs to be explored, becoming the searching for the optimum calibration a huge challenge for the development engineering team. This work proposes a methodology development in which complex problems can be solved by model based solutions regarding the best balance finding of emissions reduction and noise attenuation. The methodology is based in machine learning approach which provides a virtual behavior of engine phenomena making possible a wider comprehension of the problem and hence the opportunity to explore enhanced solutions. The study case scenario used to apply the method was a 6.4 liters engine which
In vehicle design, the H point is a theoretical relative location measured in relation to specific characteristics that determines a group of dimensions to define vehicle interior roominess. Based on theoretical H point automakers concept their vehicle and have to make important decisions on vehicle architectural that could result in a bad product for the future customers and during the early phase of vehicle development, one of the key design attributes to consider is in relation to the interior comfort of the user, so that its design and its components enabling a favorable interaction with the occupant. Vehicle interior roominess is one of the key factors for buyers’ satisfaction with certain features such as the shoulder room, headroom and couple distance, among others, may influence the level of satisfaction of the occupants’ comfort. One of these items refers to the rear chair height (H30-2), which is presented while by the distance of rear H-point to the vehicle floor affecting
During the early phase of vehicle development, one of the key design attributes to consider is the trunk. Trunk is the pillar that is responsible for user’s accommodate their baggage and make into customer needs in engineer metrics. Therefore, it is one of the key requirements to be considered during the vehicle design. Certain internal vehicle trunk characteristics such as the trunk height and length are engineer metrics that influence the occupants’ perception for trunk. One specific characteristic influencing satisfaction is the rear opening width lower for notch back segment, which is the subject of this paper. The objective of this project is to analyze the relationship between the rear opening width lower with the occupant’s satisfaction under real world driving conditions, based on research, statistical data analysis and dynamic clinics.
During the early stages of vehicle development, a crucial design attribute to consider is the access of occupants into the vehicle. Access is essential for ensuring comfortable entry and egress, a key factor in occupant satisfaction. The challenge in development lies in arranging various vehicle dimensions to maintain a competitive level of comfort compared to other vehicles in the same segment. Engineering metrics such as the trimmed opening rearward and forward of the Seating Reference Point (SgRP) significantly influence occupants’ perceptions of access comfort. One specific dimension that directly affects satisfaction is entrance height, the focus of this study. This project successfully correlates the entrance height of a vehicle’s first-row door opening with occupant satisfaction under real-world driving conditions. The research involved statistical data analysis and clinics to validate these findings. The results indicate that greater levels of satisfaction are achieved with
Design validation plays a crucial role in the overall cost and time allocation for product development. This is especially evident in high-value manufacturing sectors like commercial vehicle electric drive systems or e-axles, where the expenses related to sample procurement, testing complexity, and diverse requirements are significant. Validation methodologies are continuously evolving to encompass new technologies, yet they must be rigorously evaluated to identify potential efficiencies and enhance the overall value of validation tests. Simulation tools have made substantial advancements and are now widely utilized in the development phase. The integration of simulation-based or simulation-supported validation processes can streamline testing timelines and sample quantities, all the while upholding quality standards and minimizing risks when compared to traditional methods. This study examines various scenarios where the implementation of advanced techniques has led to a reduction in
Recent advancements in electric vertical take-off and landing (eVTOL) aircraft and the broader advanced air mobility (AAM) movement have generated significant interest within and beyond the traditional aviation industry. Many new applications have been identified and are under development, with considerable potential for market growth and exciting potential. However, talent resources are the most critical parameters to make or break the AAM vision, and significantly more talent is needed than the traditional aviation industry is able to currently generate. One possible solution—leverage rapid advancements of artificial intelligence (AI) technology and the gaming industry to help attract, identify, educate, and encourage current and future generations to engage in various aspects of the AAM industry. Beyond Aviation: Embedded Gaming, Artificial Intelligence, Training, and Recruitment for the Advanced Air Mobility Industry discusses how the modern gaming population of 3.3 million
As vehicles adopt software-centric architectures, assessing vehicle software behavior becomes more complex, which can lead to the exploitation of overlooked or untreated vulnerabilities. Using these backdoors, attacks frequently targeted automotive products for malicious reasons. Automotive security incident management involves continuous monitoring of incidents and vulnerabilities. However, it faces challenges in reproducing attacks and revalidating security goals. The lack of visualization of attack scenarios, and vectors, and the knowledge required to replicate attacks hinders vulnerability assessment. The proposed approach aims to improve vulnerability assessment and document residual risks. It promotes replicating attack scenarios using cyber digital twins to support threat modeling, risk assessment, and threat analysis. The research paper focuses on utilizing digital twins for cybersecurity incident response, threat monitoring, and vulnerability exploitation by examining elastic
As a journey to green initiatives, one of the focus areas for automotive industry is reducing environmental impact especially in case of internal combustion engines. Latest digital twin technology enable modelling complicated, fast and unsteady phenomena including the changes of emission gases concentration and output torque observed during diesel emission and combustion process. This paper presents research on the emission and combustion characteristics of a heavy vehicle diesel engine, elaborating an engineered architecture for prognostics/diagnostics, state monitoring, and performance trending of heavy-duty vehicle engine (HDVE) and after treatment system (ATS). The proposed architecture leverages advanced modeling methodologies to ensure precise predictions and diagnostics, using data-driven techniques, the architecture accurately model’s engine and exhaust system behaviors under various operating conditions. For exhaust system, architecture demonstrates encouraging predictive
Today's battery management systems include cloud-based predictive analytics technologies. When the first data is sent to the cloud, battery digital twin models begin to run. This allows for the prediction of critical parameters such as state of charge (SOC), state of health (SOH), remaining useful life (RUL), and the possibility of thermal runaway events. The battery and the automobile are dynamic systems that must be monitored in real time. However, relying only on cloud-based computations adds significant latency to time-sensitive procedures such as thermal runaway monitoring. Because automobiles operate in various areas throughout the intended path of travel, internet connectivity varies, resulting in a delay in data delivery to the cloud. As a result, the inherent lag in data transfer between the cloud and cars challenges the present deployment of cloud-based real-time monitoring solutions. This study proposes applying a thermal runaway model on edge devices as a strategy to reduce
With the trend of increasing technological complexity, software content and mechatronic implementation, there are increasing risks from systematic failures and random hardware failures, which is to be considered within the scope of functional safety. ISO 26262 series of standards provides guidance to mitigate these risks by providing appropriate requirements and processes. To develop a safe product with respect to above mentioned complexities, it is very critical to develop a safe system and hence a thorough and robust “Technical Safety Concept” is very important to ensure absence of unreasonable risk due to hazards caused by malfunctions of E/E systems. ISO26262-Part 4 provides guidelines for “Product development at the system level”, to design safety-related systems that include one or more electrical and/or electronic (E/E) systems and that are installed in series production road vehicles. Defining requirements at system level for each individual technology and systematically
A BDT (Battery digital Twin) is a virtual representation of a vehicle's physical battery system, combining electrochemical and machine learning models to provide insights into key battery parameters like State of Charge (SOC), State of Health (SOH), Internal Resistance (IR), and Remaining Useful Life (RUL). This BDT model is calibrated using cell testing throughout its degradation process up to 80% SOH, alongside vehicle data for accurate predictions under diverse conditions. By continuously monitoring the battery under various operating scenarios, the BDT aids in effective battery management, identifying cells that degrade more quickly and the likely causes of this degradation. Current and temperature profiles offer insights into battery usage patterns. The BDT aggregates fleet-wide parameters and analyzes individual cell performance, providing critical information on SOC, SOH, IR, RUL, and voltage. Additionally, the BDT includes prognostic capabilities to alert users of potential
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