Browse Topic: Systems engineering
As electric vehicles (EVs) become more advanced, so ensuring the reliability of critical components like the motor and Motor Control Unit (MCU) is essential. This paper presents a digital twin model designed to predict failures in motor and MCU components using machine learning. The approach focuses on detecting early signs of failure through real-world data and advanced analytics. We collected thermal and performance data from field vehicles, capturing both normal (healthy) and abnormal (faulty) operating conditions. Using this dataset, we developed and trained an Auto Encoder-based machine learning model that learns what “normal” looks like and flags deviations as potential issues. One key outcome of this study is the successful early prediction of Insulated Gate Bipolar Transistor (IGBT) degradation, where the system identified subtle behavioral changes long before any visible failure symptoms appeared. This digital twin acts as a virtual replica of the physical components
This study explores the application of reverse engineering (RE) and digital twin (DT) technology in the design and optimization of advanced powertrain systems. Traditional approaches to powertrain development often rely on legacy designs with limited adaptability to modern efficiency and emission standards. In this work, we present a methodology combining 3D scanning, computational modeling, and machine learning to reconstruct, analyze, and enhance internal combustion engines (ICEs) and electric vehicle (EV) drivetrains. By digitizing physical components through RE, we generate high-fidelity DT models that enable virtual testing, performance prediction, and iterative improvement without costly physical prototyping. Key innovations include a novel mesh refinement technique for scanned geometries and a hybrid simulation framework integrating finite element analysis (FEA) and multi-body dynamics (MBD). Our case study demonstrates a 12% increase in thermal efficiency for a retrofitted ICE
In the context of increasing global energy demand and growing concerns about climate change, the integration of renewable energy sources with advanced modelling technologies has become essential for achieving sustainable and efficient energy systems. Solar energy, despite its considerable potential, continues to face challenges related to performance variability, limited real-time insights, and the need for reactive maintenance. To overcome these barriers, this work presents a Digital Twin framework aimed at optimizing solar-integrated energy systems through real-time monitoring, predictive analytics, and adaptive control. This work presents a Digital Twin framework designed to address the challenges of designing, operating, maintaining, and estimating renewable energy systems, specifically solar power, based on dynamic load demand. The framework enables real-time forecasting and prediction of energy outputs, ensuring systems operate efficiently and maintain peak performance across
The automotive industry has undergone significant transformation with the adoption of electric vehicles (EVs). However, the inadequate driving range is still a major limitation and to tackle range anxiety, the focus has shifted to energy management strategies for optimal range under different driving conditions. Developing an optimal energy management algorithm is crucial for overcoming range anxiety and gaining a competitive edge in the market. This paper introduces Dynamic Energy Management Strategy (DEMS) for electric vehicles (EVs), designed to optimize battery usage and extend the driving range. Utilizing vehicle digital twin model, DEMS estimates energy consumption across Eco, Normal, and Sports driving modes by analyzing vehicle velocity profiles and pedal inputs. By calculating actual battery consumption and identifying excess power usage, DEMS operates in a closed loop to periodically assess the power gap based on real-time vehicle conditions, including HV components like the
The automotive industry faces increasing challenges in managing vehicle lifecycle complexity, including inefficiencies in design, manufacturing, and maintenance. Traditional reactive maintenance approaches often lead to unexpected downtimes, increased costs, and diminished customer experience. Moreover, rapidly evolving technologies demand agile and adaptive development processes. The Digital Twin (DT) concept which involves leveraging advanced technologies to create virtual representations of physical systems offers a promising solution by enabling real-time simulation, prediction, and optimization throughout the vehicle lifecycle. By bridging physical and digital realms, Digital Twins provide a powerful tool for improving system efficiency, adaptability, and quality. This paper highlights the benefits of applying Digital Twin principles at the systems engineering level, offering a solution for more resilient, innovative, and customer-centric vehicle systems. This study explores the
This paper presents Nexifi11D, a simulation-driven, real-time Digital Twin framework that models and demonstrates eleven critical dimensions of a futuristic manufacturing ecosystem. Developed using Unity for 3D simulation, Python for orchestration and AI inference, Prometheus for real-time metric capture, and Grafana for dynamic visualization, the system functions both as a live testbed and a scalable industrial prototype. To handle the complexity of real-world manufacturing data, the current model uses simulation to emulate dynamic shopfloor scenarios; however, it is architected for direct integration with physical assets via industry-standard edge protocols such as MQTT, OPC UA, and RESTful APIs. This enables seamless bi-directional data flow between the factory floor and the digital environment. Nexifi11D implements 3D spatial modeling of multi-type motor flow across machines and conveyors; 4D machine state transitions (idle, processing, waiting, downtime); 5D operational cost
Over-the-Air (OTA) update technology has come forth as a transformative aider in the domain of automotive technology, allowing Original Equipment Manufacturers (OEMs) and Tier-1 suppliers of Electric vehicles (EVs) to frequently make software modifications, enhancements, and bug fixes that are essential to optimize the performance of powertrain components such as the motor controller unit (MCU), Battery Management System (BMS), and Vehicle Control Unit (VCU). This facilitates them to remotely supply updates to the vehicle firmware and software by giving inputs of calibration data without requiring physical access to the vehicle. However, as OTA updates have a direct impact on vehicle’s performance, safety and cybersecurity, a stringent validation methodology is of prime importance prior to deployment process. This paper explores the integration of Hardware-in-Loop (HIL) simulation into the OTA validation pipeline as a means to ensure reliability, safety, and functional correctness of
The automotive industry is rapidly evolving with technologies such as vehicle electrification, autonomous driving, Advanced Driver Assistance Systems (ADAS), and active suspension systems. Testing and validating these technologies under India’s diverse and complex road conditions is a major challenge. Physical testing alone is often impractical due to variability in road surfaces, traffic patterns, and environmental conditions, as well as safety constraints. Virtual testing using high-fidelity digital twins of road corridors offers an effective solution for replicating real-world conditions in a controlled environment. This paper highlights the representation of Indian road corridors as digital twins in ASAM OpenDRIVE and OpenCRG formats, emphasizing the critical elements required for realistic simulation of vehicle, tire, and ADAS performance. The digital twin incorporates detailed 3D road profiles (X-Y-Z coordinates), capturing the geometry and surface variations of Indian roads. The
Civil vehicles, commonly seen as complex products, involve many high-tech aspects, several fields working together, many investments spent on projects, and challenging management. Through the entire life-cycle of aircraft development, the application of requirement-driven systems engineering methodologies helps to manage the aircraft development process while addressing the needs of the market and of stakeholders. The operational needs of an aircraft are design inputs for aircraft development, and the precision, authenticity, and comprehensiveness of these needs influence the efficiency of the development processes and the quality of the products. When the design and research-and-development activities are based on accurate and complete needs, the development interval for such projects can be shortened significantly, and the costs of R&D lowered. Especially because it is one of the fundamental phases of establishing whether aircraft meet the design requirements, design verification is
This paper presents an in-depth study on configuration management for civil aircraft electromechanical systems, grounded in process methodologies and practical experience of configuration management. Beginning with the definition and significance of configuration management, the study analyzes existing configuration management practices in domestic and international aviation enterprises. It systematically examines the requirements and frameworks for configuration management in civil aircraft electromechanical systems, refining critical elements through two primary dimensions: the establishment, refinement and implementation of configuration management processes. Critical refined elements are highlighted to offer actionable insights for civil aviation enterprises in advancing their configuration management practices.
Simulation has become mission-critical for ADAS development. Model-based systems engineering can integrate modeling and simulation from the start of the design process. Advanced Driver Assistance Systems (ADAS) are transforming vehicle safety, acting as the bridge between conventional driving and full autonomy. From adaptive cruise control to emergency braking and blind-spot detection, these technologies rely on a dense network of radar sensors, antennas, electronic control units and software. What unites them is the need for precise functionality under complex real-world situations. Achieving full reliability requires more than testing on the road; it demands a virtual approach grounded in simulation. Simulation has become mission-critical for ADAS development. As new vehicles integrate dozens of sensors into tightly constrained spaces, even subtle design decisions can affect system performance. Radar solutions, in particular, present unique challenges, especially as vehicle surfaces
The global electronics supply chain has always run in cycles — tight supply followed by sudden gluts — but in recent years, the pace and scale of disruption have accelerated. From semiconductor shortages to shifting trade policies and pandemic-driven bottlenecks, OEMs across every sector have been forced to rethink how they source and secure critical components.
The evolution of Autonomous off-highway vehicles (OHVs) has transformed mining, construction, and agriculture industries by significantly improving efficiency and safety. These vehicles operate in high dust, uneven terrain, and potential communication failures, where safety is challenged. To guarantee vehicle safety in such situations, a robust architecture that combines AI-driven perception, fail-safe mechanisms, and conformance to many ISO standards is required. In unstructured environments, AI-driven perception, decision-making, and fail-safe mechanisms are not fully addressed by traditional safety standards like ISO26262 (road vehicles), ISO19014 (earth-moving machinery and it is replacing withdrawn ISO 15998), ISO12100 (Safety of machinery) and ISO25119 (agriculture), ISO 18497 (safety of highly automated agricultural machinery), and ISO/CD 24882 (cybersecurity for machinery).These standards mainly concentrate on the reliability of mechanical and electric/electronic systems
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