Browse Topic: Systems engineering

Items (1,493)
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
Khan, JalalD'Alessandro, StefanoTramaglia, FedericoFauda, Alessandro
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
Gil, AntonioMonsalve-Serrano, JavierMarco-Gimeno, JavierGuaraco-Figueira, Carlos
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
Gupta, ShobhitHegde, BharatkumarHaskara, IbrahimShieh, Su-YangChang, Insu
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
Upase, BalasahebShroff, Roopesh
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
Bongards, AnitaLiu, XiaobingBeemer, MariaGajowski, DanielRama, NeerajShah, KeyaFallahdizcheh, Amirhossein
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
Shroff, RoopeshUpase, Balasaheb
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
Bossi, AdrienBourniche, EricLeblay, ArnaudDavid, PascalNanjundaswamy, Harsha
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
Valiyil, RinshaR, BharathNair, AnushPuthiyapurayil, ShamalRavi, Reshma
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
Manvi, PranavSuber II, DarrylGriffith, KaitlynTurner, CameronCastanier, Matthew P.Wagner, John
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
Mendes de Oliveira, Arthur HendricksReis, Pedro AlmeidaAnunciação, GabrielVinícius Carlos de Lima, JonathanSarracini Júnior, FernandoGarcia, Matias Ezequiel
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
Functional Safety Committee
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
Zhang, YawenCai, Xianhua
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
S J, SrihariUmorya, DivyanshPatidar, DeepeshJaiswal, Manish
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
Zeeshan, MohammadAkre, Vineet
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
Azevedo, Marcos PauloLahoz, Carlos Henrique Netto
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
Rosa, Leonardo OlimpioBranco, César Tadeu Nasser Medeiros
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
Sarkar, PrasantaPardeshi, RutujaKharwandikar, AnandKondhare, Manish
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
Sasi Kiran, TalabhaktulaKondhare, ManishPatil, SuyogNath, SubhrajyotiCH, Sri RamTank, PrabhuSarkar, Prasanta
Emergence of Software Defined Vehicles (SDVs) presents a paradigm shift in the automotive domain. In this paper, we explore the application of Model-Based Systems Engineering (MBSE) for comprehensive system simulation within the SDV architecture. The key challenge for developing a system model for SDV using traditional methods is the document centric approach combined with the complexity of SDV. This MBSE approach can help to reduce the complexity involved in Software-Defined Vehicle Architecture making it more flexible, consistent, and scalable. The proposed approach facilitates the definition and analysis of functional, logical, and physical architecture enabling efficient feature and resource allocation and verification of system behaviour. It also enables iterative component analysis based on performance parameters and component interaction analysis (using sequence diagrams).
Navas, AkhilPaul, Annie
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
Singh, PrabhsharnThakare, UjvalHivarkar, Umesh
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
Venkatachalapathy, Sreenikethana
Researchers at the Johns Hopkins Applied Physics Laboratory have developed a machine learning method that could have a huge impact on understanding how material is formed during the additive manufacturing process. John Hopkins Applied Physics Laboratory, Laurel, MD Researchers at the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, have demonstrated a novel approach for applying machine learning to predict microstructures produced by a widely used additive manufacturing technique. Their approach promises to dramatically reduce the time and cost of developing materials with tailored physical properties and will soon be implemented on a NASA-funded effort focused on creation of a digital twin. “We anticipate that this new approach will be extremely impactful in helping design and understand material formation during additive manufacturing processes, and this fits into our overarching strategy focused on accelerating materials development for national security,” said
Virtualization features such as digital twins and virtual patching can accelerate development and make commercial vehicles more agile and secure. There is one sure-fire way to secure commercial vehicles from cyber-attacks. “You just remove the connectivity,” quipped Brandon Barry, CEO of Block Harbor Cybersecurity and the moderator of a panel session on “cybersecurity of virtual machines” at the SAE COMVEC 2024 conference in Schaumburg, Illinois. Obviously, that train has left the station - commercial vehicles of all types, including trains, are only becoming more automated and connected, which increases the risks for cyber-attacks. “We have very connected vehicles, so attacks can be posed not just through powertrain solutions but also through telemetry, infotainment systems connected to different applications and services, and also through cloud platforms,” said Trisha Chatterjee, current product support and data specialist for fuel cell and hydrogen technology at Accelera by Cummins.
Gehm, Ryan
Northrop Grumman San Diego, CA jacqueline.rainey@ngc.com
Researchers at the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, have demonstrated a novel approach for applying machine learning to predict microstructures produced by a widely used additive manufacturing technique. Their approach promises to dramatically reduce the time and cost of developing materials with tailored physical properties and will soon be implemented on a NASA-funded effort focused on creation of a digital twin.
Researchers have developed a new method for predicting what data wireless computing users will need before they need it, making wireless networks faster and more reliable. The new method makes use of a technique called a “digital twin,” which effectively clones the network it is supporting.
Internet of vehicles (IoV) system as a typical application scenario of smart city, trajectory planning is one of the key technologies of the system. However, there are some unstructured spaces such as road shoulders and slopes pose challenges for trajectory planning of connected-automated vehicle (CAV). Therefore, this paper addresses the problem of CAV trajectory planning affected by unstructured space. Firstly, based on cyber-physical system (CPS), the cyber-physical trajectory planning system (CPTPS) framework was built. A high-precision digital twin CAV is established based on the physical properties and geometric constraints of CAV, and the digital model is mapped to cyber space of the CPTPS. In order to further reduce the energy consumption of the CAV during driving and the time spent from the start to the end, a model was established. Further, based on the sand cat swarm hybrid particle swarm optimization algorithm (SCSHPSO), global path planning for connected-automated vehicles
Ma, ShiziMa, ZhitaoShi, YingYang, ZhongkaiLai, DaoyinQi, Zhiguo
A digital twin is a digital representation of a real physical system, product, or process that functions as its practically identical digital counterpart for tasks such as testing, integration, monitoring, and maintenance. Creating digital twins allows the ‘digital system’ or ‘digital product’ to be tested at faster-than-real-time which improves overall program efficiency and shortens the programme duration. The HORIBA Intelligent Lab virtual engineering toolset was used to generate an Empirical Digital Twin (EDT) of a contemporary off-highway diesel Internal Combustion Engine (ICE) from physical testing, accounting for the effects of altitude and combustion air temperature. The EDT was subsequently used to predict engine performance and emissions for several synthetic off-highway machine cycles at sea-level and 3000m altitude. The synthetic agricultural cycles which included ploughing, seeding, spraying, fertilising, and roading were generated using a machine simulation programme
Roberts, PhilBates, LukeWhelan, SteveMaroni, ClaudioLeo, ElisabettaPezzola, Marco EzioChild, Steven
Many organizations are falling far short of achieving the lifecycle potential of their new product designs. One major source of this suboptimal business performance stems from underleveraging key Systems Engineering and Design Engineering principles in the early phases of the design process. If these are being poorly applied, the following will likely occur: Inefficient use of engineering (and other cross-functional) resources Unnecessarily high product development costs Delayed time-to-market Subpar launch quality Poor system-level safety Suboptimal lifecycle sustainability-related performance Compromised design innovation This report addresses these challenges and articulates how Systems Design Engineering provides nonburdensome and quickly applied methods for overcoming these shortcomings, placing a dedicated focus on the three high-level principles that govern lifecycle product design success. Excellent and efficient performance against each of them is needed to achieve a new
Genter, David Paul
This document draws from, summarizes, and explains existing broadly accepted engineering best practices. This document defines the process and procedure for application of various best practice methods. This document is specifically intended as a standard for the engineering practice of development and execution of a link loss power budget for a general aerospace system related digital fiber optic link. It is not intended to specify the values associated with specific categories or implementations of digital fiber optic links. This document is intended to address both existing digital fiber optic link technology and accommodate new and emerging technologies. The proper application of various calculation methods is provided to determine link loss power budget(s), that depend on differing requirements on aerospace programs. A list of parameters is provided as guidance for aerospace fiber optics applications along with a check list to help assure that appropriate parameters and
AS-3 Fiber Optics and Applied Photonics Committee
Heather Cummings, a 27-year old senior flight controls and autonomy engineer at Sikorsky, is the winner of the Aerospace/Defense category for SAE Media Group's inaugural Women in Engineering: Rising Star Awards program. In addition to her role developing flight control software and improving Sikorsky's Innovations department's processes for software and model-based systems engineering, she is also a pilot. Among her career accomplishments at Sikorsky include leading the flight controls software development and flight testing program on a technology demonstrator aircraft for autonomy and reduced crew operations. The project involved Heather dividing up sub-tasks for the project and working with each individual on the team to mentor them on the engineering skills necessary for completion. She also served as the onboard flight test engineer for the project. One of her career goals is to serve as the lead engineer on new technologies that form the next generation of semi and fully
Effective thermal management is crucial for vehicles, impacting both passenger comfort and safety, as well as overall energy efficiency. Electric vehicles (EVs) are particularly sensitive to thermal considerations, as customers often experience range anxiety. Improving efficiency not only benefits customers by extending vehicle range and reducing operational costs but also provides manufacturers with a competitive edge and potential revenue growth. Additionally, efficient thermal management contributes to minimizing the environmental impact of the vehicle throughout its lifespan. Digital twins have gained prominence across various industries due to their ability to accelerate development while minimizing testing costs. Some applications have transitioned to comprehensive three-dimensional models, while others employ model reduction techniques or hybrid approaches that combine different modeling methods. The discovery of unknown working mechanisms, more efficient and effective control
Palacio Torralba, JavierKapoor, SangeetJaybhay, SambhajiLocks, OlafKulkarni, Shridhar DilipraoShah, Geet
Selective Catalytic Reduction (SCR) systems are crucial for automotive emissions control, as they are essential to comply with stringent emissions regulations. Model-based SCR controls are used to minimize NOx emissions in a broad range of real-word driving scenarios, constantly adapting the urea injection to diverse load and temperature operating conditions, also accounting for different catalyst ageing status. In this framework, Neural Networks (NN) based models offer a promising alternative to reduced-order physical models or map-based controls. This study introduces a hybrid modeling approach for SCR systems, leveraging the integration of machine learning techniques with detailed physics-based models. A high fidelity 1D-CFD plant model of a SCR catalyst, previously calibrated on experimental data, was used as digital twin of the real component. A standardized simulation protocol was defined to virtually characterize the SCR thermal and chemical behavior under the full range of
Sapio, FrancescoAglietti, FilippoFerreri, PaoloSavuca, Alexandru
Hydrogen-powered mobility is believed to be crucial in the future, as hydrogen constitutes a promising solution to make up for the non-programmable character of the renewable energy sources. In this context, the hydrogen-fueled internal combustion engine represents one of the suitable technical solution for the future sustainable mobility. In a short-term perspective, the development of the green hydrogen production capability and distribution infrastructure do not allow a substantial penetration of pure hydrogen IC engines. For this reason, natural gas – hydrogen blends can represent a first significant step towards decarbonization, also determining a trigger effect on the hydrogen market development. The present paper is focused on the analysis of the combustion and performance characteristics of a production PFI natural gas engine, run on blends with 15% in volume of hydrogen (HCNG). More specifically, a fuel-flexible, predictive 1D simulation model has been developed within the
Baratta, MirkoDi Mascio, ValerioMisul, DanielaMarinoni, AndreaCerri, TarcisioOnorati, Angelo
The energy transition is a key challenge and opportunity for the transport sector. In this context, the adoption of electric vehicles (EVs) is emerging as a key solution to reduce environmental impact and mitigate problems related to traditional energy sources. One of the biggest problems related to electric mobility is the limited driving range it offers compared to the time needed for recharging, leading to what’s commonly known as “range anxiety” among users. Significant part of the energy consumption of an electric vehicle is represented by the management of the HVAC system, which aim is to ensure the achievement and maintenance of thermal comfort conditions for the occupants of the vehicle. Currently the HVAC control logics are based on the pursuing of specific cabin setpoint temperature, which does not always guarantee the thermal comfort; more advanced human-based control logics allow to attain the thermal comfort in a zone around the subjects, as known as “heat bubble”, rather
Bartolucci, LorenzoCennamo, EdoardoCordiner, StefanoDonnini, MarcoFrezza, DavideGrattarola, FedericoMulone, VincenzoAimo Boot, MarcoGiraudo, Gabriele
In the early 2010s, LightSquared, a multibillion-dollar startup promising to revolutionize cellular communications, declared bankruptcy. The company couldn't figure out how to prevent its signals from interfering with those of GPS systems. Now, Penn Engineers have developed a new tool that could prevent such problems from ever happening again: an adjustable filter that can successfully prevent interference, even in higher-frequency bands of the electromagnetic spectrum.
A company says that its digital twin alignment system, incorporating a sophisticated AI algorithm and an off-the-shelf camera, has the potential to revolutionize the auto industry, potentially saving it up to a staggering $20 billion in the effort to detect defects on the manufacturing line. Generally, such inspections of spot welds, bolt holes and the like are handled one of three ways: Slow manual inspections that can have high error rates. Even slower inspection with coordinate-measuring machines (CMMs) that can take hours to inspect 150 spot welds. Tremendously expensive technology, such as lasers, that still aren't perfect.
Clonts, Chris
The rise of AI models across diverse domains includes promising advancements, but also poses critical challenges. In particular, establishing trust in AI-based systems for mission-critical applications is challenging for most domains. For the automotive domain, embedded systems are operating in real-time and undertaking mission-critical tasks. Ensuring dependability attributes, especially safety, of these systems remains a predominant challenge. This article focuses on the application of AI-based systems in safety-critical contexts within automotive domains. Drawing from current standardization methodologies and established patterns for safe application, this work offers a reflective analysis, emphasizing overlaps and potential avenues to put AI-based systems into practice within the automotive landscape. The core focus lies in incorporating pattern concepts, fostering the safe integration of AI in automotive systems, with requirements described in standardization and topics discussed
Blazevic, RomanaVeledar, OmarStolz, MichaelMacher, Georg
AEB systems are critical in preventing collisions, yet their effectiveness hinges on accurately estimating the distance between the vehicle and other road users, as well as understanding road conditions. Errors in distance estimation can result in premature or delayed braking and varying road conditions alter road-tire friction coefficients, affecting braking distances. The integration of advanced sensors like LiDARs has significantly enhanced distance estimation. Cameras and deep neural networks are also employed to estimate the road conditions. However, AEB systems face notable challenges in urban environments, influenced by complex scenarios and adverse weather conditions such as rain and fog. Therefore, investigating the error tolerance of these estimations is essential for the performance of AEB systems. To this end, we develop a digital twin of our test vehicle in the IPG CarMaker simulation environment, which includes realistic driving dynamics and sensor models. Our simulated
Wang, YifanIatropoulos, JannesThal, SilviaHenze, Roman
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