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This full-day course is designed to equip engineering professionals with the knowledge and tools needed to combine the strengths of Design Engineering and Systems Engineering into Systems Design Engineering (SDE) principles. These principles will improve engineering efficiency and practically design more sustainable system-level products, all while strategically aligning with digital transformation objectives.

In the emerging economies, there is a growing adoption of electric vehicles into fleet vehicles. With the steady increase in this business area, there is a demand for the innovation in the battery charging methodologies. The swappable charging method is one such charging method that is gaining prominence. Battery swapping involves replacing an EV’s depleted battery with a fully charged one. This approach can significantly reduce wait times for drivers, as swapping batteries typically takes only few minutes, similar to the time it takes to refuel an ICE vehicle. With battery swapping, EV owners can avoid concerns related to battery degradation, since they receive a fully charged, well-maintained battery during each swap. Research is being done either to reduce the cost of operation of Battery Swapping station (BSS), or to reduce the waiting time for the users by charging fast.

To properly compare and contrast the environmental performance of one vehicle technology against another, it is necessary to consider their production, operation, and end-of-life fates. Since 1995, Argonne’s GREET® life cycle analysis model (Greenhouse gases, Regulated Emissions, and Energy use in Technologies) has been annually updated to model and refine the latest developments in fuels and materials production, as well as vehicle operational and composition characteristics. Updated cradle-to-grave life cycle analysis results from the model’s latest release are described for a wide variety of fuel and powertrain options for U.S. light-duty and medium/heavy-duty vehicles. Light-duty vehicles include a passenger car, sports utility vehicle (SUV), and pick-up truck, while medium/heavy-duty vehicles include a Class 6 pickup-and-delivery truck, Class 8 day-cab (regional) truck, and Class 8 sleeper-cab (long-haul) truck.

In recent years, the increasing complexity of modern aerospace systems has driven the rapid adoption of robust Model-Based Systems Engineering (MBSE). MBSE is a development methodology centered around computational models, which are instrumental in supporting the design and analysis of intricate systems. In this context, the Architecture Analysis and Design Language (AADL) and Systems Modeling Language (SysML) are two prominent modeling languages for specifying and analyzing the structure and behavior of a cyber-physical system. Both languages have their own specific use cases and tool environments and are typically employed to model different aspects of system design. Although multiple software tools are available for transforming models from one language to another, their effectiveness is limited by fundamental differences in the semantics of each language.

In today's complex engineering landscape, effective systems engineering is essential for ensuring the success of projects across various industries. The MBSE Design and Development training course offers a comprehensive exploration of Model-Based Systems Engineering (MBSE) principles and practices, providing participants with the technical knowledge and practical skills needed to excel in modern systems engineering. This course serves as a bridge from traditional systems engineering approaches to contemporary systems modeling methodologies.

For commercial vehicles, reliability is key since the vehicle is typically linked to the daily earnings of the owner. To ensure continuous vehicle operation, early diagnostics of critical issues and proactive maintenance are important. However, an electric vehicle is a complex and dynamic system consisting of numerous components interacting with each other and with external environments such as road conditions, traffic, weather, and driving behavior. Thus, vehicle operation and performance are highly contextual and for identifying an abnormal operation (diagnostics) the solution must consider the conditions under which it is driven. To address this, the paper proposes an AI-based digital twin of an electric three-wheeler vehicle. TabNet a deep-learning based model is used to learn and generate near-ideal vehicle behavior. The focus of the paper is motor subsystem. The model is trained using appx 200 vehicles first 1500 km driven data.

To many, a digital twin offers “functionality,” or the ability to virtually rerun events that have happened on the real system and the ability to simulate future performance. However, this requires models based on the physics of the system to be built into the digital twin, links to data from sensors on the real live system, and sophisticated algorithms incorporating artificial intelligence (AI) and machine learning (ML). All of this can be used for integrated vehicle health management (IVHM) decisions, such as determining future failure, root cause analysis, and optimized energy performance. All of these can be used to make decisions to optimize the operation of an aircraft—these may even extend into safety-based decisions.

Paris Climate Agreement defined the strategy to contrast the current climate change trend. Therefore, a complete and deep review of the entire lifespan of a product is necessary. Recently, in the agri-tech field, also tractors manufacturers have begun to explore the adoption of full-electric or hybrid-electric powertrains to contrast pollutants emissions and to misrepresent tractor functionalities, due to diesel engines stricter regulations in terms of pollutants emissions. The aim of this work is to evaluate the carbon intensity of an ICE and hybrid-electric orchard tractor trough Life Cycle Assessment technique. The assessment has been conducted considering production, use and disposal phases of the tractor. Lastly, the results obtained are illustrated according to gate-to-gate and cradle-to-gate approach.

The transportation industry has been scrutinized for its contribution towards the global greenhouse gas emissions over the years. While the automotive sector has been regulated by strict emission legislation globally, the emissions from marine transportation have been largely neglected. However, during the past decade, the international maritime organization focused on ways to lower the emission intensity of the marine sector by introducing several legislations. This sets limits on the emissions of different oxides of carbon, nitrogen and sulphur, which are emitted in large amounts from heavy fuel oil (HFO) combustion (the primary fuel for the marine sector). A 40% and 70% reduction per transport work compared to the levels of 2008 is set as target for CO2 emission for 2030 and 2050, respectively. To meet these targets, commonly, methanol, as a low-carbon fuel, and ammonia, as a zero-carbon fuel, are considered.

Since vehicles are comprised of thousands of components, it is essential to reduce the Life Cycle Inventory (LCI) modelling workload. This study aims to compare different LCI modeling workload-reducing scenarios to provide a trade-off between the workload efforts and result accuracy. To achieve the optimal balance between computational effort and data specification requirements, the driver seat is used as a case study, instead of the entire vehicle. When all the components of a conventional light-duty commercial vehicle are sorted by mass descending order, seats are among the first five. In addition, unlike the other components, seats are comprised of metals as well as a wide range of plastics and textiles, making them a representative test case for a general problem formulation. In this way, methodology and outcomes can be reasonably extended to the entire vehicle.

This work covers the historical development of Built-In-Test (BIT) for fiber optic interconnect links for aerospace applications using Optical Time Domain Reflectometry (OTDR) equipped transceivers. The original failure modes found that installed fiber optic links must be disconnected before diagnosis could begin, often resulting in “no fault found” (NFF) designation. In fact, the observed root cause was that most (85%) of the fiber optic link defects were produced by contamination of the connector end faces. In March of 2006, a fiber optics workshop was held with roughly sixty experts from system and component manufacturers to discuss the difficulties of fiber optic test in aerospace platforms. During this meeting it was hypothesized that Optical Time Domain Reflectometry (OTDR) was feasible using an optical transceiver transmit pulse as a stimulus. The time delay and amplitude of received reflections would correlate with the position and severity of link defects, respectively.

The changing mobility landscape of India reveals that the erstwhile transport modes of the 20th century i.e., railways and road buses are making way for airlines, personal vehicles, shared mobility, metro rails. Rapid technological changes, stricter regulations, new transport cultures autonomous, connected, electric and shared (ACES), state-of-the-art and environmental concerns are shaping up the eco-system for automobiles. Despite these challenges roadways and automobiles will continue to be most prominent solution in India for future. But for that, the automobile sector should be agile, innovative, and adaptable to changing eco-system, vigilant to thwart threat of alternate mobility solutions and must provide sustainable solutions for the future. The purpose of this paper to evaluate various mobility solutions, ascertain prominence of upcoming automobile solutions and their sustainability for future in India.

Electric propulsion is the object of intense research efforts all over the world, as a viable solution to fossil resource exploitation and pollutant emissions, towards a sustainable development. In this paper, we perform a thorough Life Cycle Assessment (LCA) of multiple electrical solutions for urban mobility, from bicycles to buses, comparing the results to those of traditional, fossil-fuel-based vehicles. This activity is of particular interest as the decision of European Parliament to interrupt the fossil fuel vehicles starting from 2035. To assess the life-cycle impact of each solution, several routes within middle size Italian cities, representative of the most Italian cities have been considered. This analysis has been performed by means of an ad-hoc integrated procedure with on-line, free tools that account also for traffic distribution.

This SAE Standard defines the test conditions, procedures, and performance requirements for circuit breakers in ratings up to and including 200 A. The document includes automatic reset, modified reset, and manually reset types of circuit breakers for 12 VDC, 24 VDC, and 48 VDC electrical systems. Some circuit breakers may have dual voltage ratings (AC and DC); however, this document evaluates DC performance only.

Driving safely is a complex task. It involves a broad range of skills invoked in a vast number of potential scenarios. Assessing a set of behavioral competencies offers a valuable directional indication of automated driving system-dedicated vehicle (ADS-DV) safety performance. Behavioral competencies provide a starting point for additional assessment and contribute to a manufacturer’s case for safety. This best practice provides an approach to specify testable ADS behavior by: Clarifying a lexicon surrounding ADS behaviors Enumerating an elemental set of behaviors Demonstrating how to derive metrics to evaluate behavioral competence To evaluate as many dynamic driving task (DDT) subtasks as possible, ADS developers decompose the DDT into a generalized set of behaviors. Subsequently, developers use system engineering techniques to ensure that this decomposition maps to an elemental set of behavioral competencies.

Aiming at the problem of configuration selection of hybrid electric bus, a method of configuration optimization of plug-in hybrid electric bus is proposed. Several hybrid system configurations (P2, EVT+2AMT, EVT+CL1) for bus are presented, and the basic parameters of each system and the advantages and disadvantages of each configuration are introduced. The life cycle cost differentiation model of hybrid system configuration is established with the vehicle life cycle cost as the optimization index. On the premise of meeting the vehicle dynamics, the system configuration with the lowest life cycle cost is selected as the optimal configuration. To verify the rationality of the optimal hybrid system configuration, the fuel price and vehicle service life are selected as sensitivity factors to analyze the sensitivity of the life cycle cost differentiation model.

Use of Model-Based Systems Engineering (MBSE) has been growing across industry, extending beyond defense and aerospace to include various commercial enterprises such as automotive and healthcare. Tool vendors are quick to point out benefits of this model-based approach and practices but are not always clear how MBSE benefits can be realized on a project. When deployed successfully, several key considerations should be addressed that maximize the value for a use-case. This four-hour class will discuss the nature and purpose of the MBSE approach and how key information is used for successful MBSE deployment as it relates to Systems Management.

Abstract Threat identification and security analysis have become mandatory steps in the engineering design process of high-assurance systems, where successful cyberattacks can lead to hazardous property damage or loss of lives. This article describes a novel approach to perform security analysis on embedded systems modeled at the architectural level. The tool, called Security Threat Evaluation and Mitigation (STEM), associates threats from the Common Attack Pattern Enumeration and Classification (CAPEC) library with components and connections and suggests potential defense patterns from the National Institute of Standards and Technology (NIST) Special Publication (SP) 800-53 security standard. This article also provides an illustrative example based on a drone package delivery system modeled in AADL.

The complete lifecycle of complex systems, such as ground vehicles, consists of multiple phases including design, manufacturing, operation and sustainment (O&S) and finally disposal. For many systems, the majority of the lifecycle costs are incurred during the operation and sustainment phase, specifically in the form of uncertain maintenance costs. Testing and analysis during the design phase, including reliability and supportability analysis, can have a major influence on costs during the O&S phase. However, the cost of the analysis itself must be reconciled with the expected benefits of the reduction in uncertainty. In this paper, we quantify the value of performing the tests and analyses in the design phase by treating it as imperfect information obtained to better estimate uncertain maintenance costs.

In recent year, Electronic Control Unit (ECU) virtualization is being promoted for development and validation of automotive software. ECU virtualization allows execution of integrated software on developer’s computer, enabling faster algorithm testing. However, the challenge remains to establish a process to be followed at organization level and its integration in the existing development process/es. In this paper, an approach for integration of Virtual ECU (V-ECU) in Software Development Life-Cycle (SDLC) is discussed. The rationale of merging ECU virtualization in standard SDLC V-cycle supplements milestones in testing & validation. Addition of this milestone with the implementation of ECU virtualization is also presented. On the foundation of modified SDLC V-cycle, a standard process flow is created to establish ECU virtualization as integral part of software development in agile methodology.