Electrical Design Research Articles

An Analysis of Direct Operating Costs for the Wright Spirit Electric Aircraft

Global warming and CO2 emissions have become increasingly pressing concerns, with the aviation industry contributing significantly to these issues. In response, efforts have been made to develop environmentally sustainable aviation solutions. This paper examines the Direct Operating Costs (DOCs) of the Wright Spirit, one of the options for electric flight, compared to the conventional BAe 146 on which the Wright Spirit is based. Utilising methodologies adapted from previous studies (largely the AEA method), the analysis investigates various factors influencing DOC including battery prices, flight duration, and charging time. The results indicate a 73% increase in overall DOCs from the BAe 146 to the Wright Spirit, largely influenced by battery costs and lifespan. However, an 83% reduction in fuel/energy costs suggests the potential viability of the Wright Spirit, particularly with anticipated reductions in battery prices. For instance, a quartering of battery prices could result in a GBP 5 increase in costs for 1 h flights; compared to the BAe 146. Moreover, the analysis finds the battery lifespan and charging time to be the most important factors to control in order to increase commercial feasibility. Ticket price comparisons suggest that the Wright Spirit’s costs align closely with current market prices, with a flight from Paris to Heathrow predicted to cost the airline GBP 136.10 per passenger. Future research could explore alternative electric aircraft designs to further assess their impact on DOCs and ticket prices

A novel construction and evaluation framework for driving cycle of electric vehicles based on energy consumption and emission analysis

The driving cycle (DC) is essential for establishing vehicle emission standards, transportation policies, and urban planning. However, existing driving cycles demonstrate poor representativeness and excessive randomness due to the insufficient capture of driving characteristics. Therefore, a novel framework for constructing and evaluating driving cycles of electric vehicles (EVs) based on energy consumption and emissions analysis is proposed to enhance the representativeness of the constructed driving cycles. First, based on road information, an improved dual-chain Markov chain method combined with the self-organizing mapping (SOM) neural network is introduced for clustering and constructing driving cycles. Subsequently, a double-layer evaluation model oriented towards energy consumption and emissions is established through a combination of model-driven and data-driven approaches to select a representative driving cycle (RDC). Finally, comparative experiments are conducted to evaluate the feasibility and scientific validity of the proposed method in multiple dimensions. The results indicate that the driving cycle constructed in this study demonstrates excellent representativeness, with an emission error of 2.04% and a comprehensive characterization parameter (CCP) value of 0.097. This study emphasizes the necessity of incorporating reasonable constraints in the driving cycle construction. This improved representativeness provides a reliable foundation for electric vehicle design and transportation policy development.

Automatic Extraction of Cable Connection Information from 2D Drawings for Electrical Outfittings Design in Shipyards

Automatic Extraction of Cable Connection Information from 2D Drawings for Electrical Outfittings Design in Shipyards

Integrated electric vehicle design and simulation: Combining a mechanical and electrical model using Simscape/Multibody

This paper presents a novel methodology for modeling and implementing an electric vehicle (EV) simulation model using the Multibody approach. The proposed method provides a robust solution for studying EV performance, taking into consideration the dynamic behavior of its complex mechanical system. The mechanical design was carried out using SOLIDWORKS software and then exported to the Simscape/Multibody platform. This process was performed to combine the obtained mechanical design with an electrical model, developed using the Simscape and MATLAB/Simulink toolbox, in the same simulation platform. The proposed EV is powered by an electromechanical PowerDrive based on a permanent magnet synchronous machine (PMSM), supplied by a hybrid energy storage system (HESS). The implemented HESS, operating in a fully active topology, is controlled by a deterministic rule-based energy management system (EMS). To evaluate the proposed model performance, simulations were conducted under ECE-15.

Capital budgeting for electrical engineering projects: A practical methodology

The paper presents the capital budgeting process and identifies key elements in the process. A standardized approach in capital budgeting by application of information technology resources for both proposal submission and documentation becomes key to success in handling large volumes. An electrical engineering distribution system design project involves the application of engineering codes and standards to stay compliant and provide safe design and construction. A model starting with proposals acceptance to evaluation and approval of capital projects was presented. The proposal stage was decoded further into assessments of the existing electrical infrastructure based on aging and engineering standards. Upon review of the standards, the proposals were accepted for evaluation and approval. For business professionals, there is a gap when working in an electrical engineering construction industry when it comes to knowing applicable engineering standards and their application. This research filled that gap by enlisting the intent of the standards review process. Although the focus was on understanding a holistic approach to electrical engineering projects, a detailed analysis of specific tasks may be designed based on the model presented in this paper. Additionally, the transmission side of projects will have a similar application of capital budgeting.

Manufacture of a 4-Degree-of-Freedom Robot to Support an IRB 120 Robot

In this work, we present the construction and control of a four-degrees-of-freedom (DOF) manipulator aimed at addressing one of the key challenges faced by the Academy-Industry Cooperation Center (CCAI): the need for mechatronic equipment to support and facilitate the development of advanced robotic cells. We begin by designing the robot’s structure and components using SolidWorks software for computer-aided design (CAD) modeling. This ensures that all the links and parts fit together properly without collisions. The robot links are then manufactured using 3D printing. Additionally, we performed kinematic modeling, dynamic analysis, and PI-V control, along with control using a trigonometric function (hyperbolic tangent). To evaluate the robot’s movement, we simulate these processes using Matlab R2019a/Simulink software, focusing on key parameters such as position, velocity, and acceleration, which inform the design of PI-V control for each link. We also present the electrical and electronic designs, followed by system implementation. The kinematics of the robot play a crucial role in the dynamics and controller design. We validate the kinematics using Peter Corke’s libraries based on the Denavit–Hartenberg parameters. The results show that the controller based on the trigonometric function improves the response time, particularly enhancing the performance of axes 2 and 3.

Study on Electrical Characteristics Analysis and Electrical Circuit Model Design of Vanadium Redox Flow Battery Systems Based on Current and Flow Rate Conditions

Study on Electrical Characteristics Analysis and Electrical Circuit Model Design of Vanadium Redox Flow Battery Systems Based on Current and Flow Rate Conditions

Electrical Tortuosities of Porous Structures Based on Triply Periodic Minimal Surfaces and Honeycombs for Power-to-Heat Systems

Power-to-heat (P2H) systems offer an efficient solution for decarbonization by facilitating the integration of renewable energy into the industrial, heating, and transport sectors. Its key requirements include high thermal efficiency and an appropriate electrical resistivity to meet application-specific electrical needs. When designing P2H systems, materials and electrical boundary conditions are often limited by application-specific requirements, whereas geometric structures offer high degrees of freedom. While thermal design calculations are often straightforward due to a variety of available Nusselt and pressure loss correlations, simplified design pathways, particularly for porous structures, are lacking in electrical design. Given the wide range of geometric degrees of freedom for porous structures and the fact that detailed modeling involves substantial computational effort, this work employed electrical tortuosity to capture and correlate the geometry-dependent impacts on the effective electrical resistance in a compact way. Honeycomb and triply periodic minimal surface (TPMS)-based structures were selected for this purpose, as they are characterized by high specific surfaces, allowing for high total heat transfer coefficients. The results show that the effective electrical resistance of both TPMS and honeycomb structures can be adjusted by the geometric structure. It was found that the electrical tortuosities of the investigated TPMS structures are nearly identical, while honeycomb structures show slightly higher values. Furthermore, the electrical tortuosity is mainly a function of the void fraction and does not change with the specific surface when the void fraction is kept constant. Finally, correlations for electrical tortuosity depending on geometric parameters with a mean error below 5% are derived for the first time, thereby providing a basis for simplified and computationally efficient electrical design calculations for P2H systems.

Dense Server Design for Immersion Cooling

The growing demands for computational power in cloud computing have led to a significant increase in the deployment of high-performance servers. The growing power consumption of servers and the heat they produce is on track to outpace the capacity of conventional air cooling systems, necessitating more efficient cooling solutions such as liquid immersion cooling. The superior heat exchange capabilities of immersion cooling both eliminates the need for bulky heat sinks, fans, and air flow channels while also unlocking the potential go beyond conventional 2D blade servers to three-dimensional designs. In this work, we present a computational framework to explore designs of servers in three-dimensional space, specifically targeting the maximization of server density within immersion cooling tanks. Our tool is designed to handle a variety of physical and electrical server design constraints. We demonstrate our optimized designs can reduce server volume by 25--52% compared to traditional flat server designs. This increased density reduces land usage as well as the amount of liquid used for immersion, with significant reduction in the carbon emissions embodied in datacenter buildings. We further create physical prototypes to simulate dense server designs and perform real-world experiments in an immersion cooling tank demonstrating they operate at safe temperatures. This approach marks a critical step forward in sustainable and efficient datacenter management.

Theoretical Design of Smart Bionic Skins with Self-Adaptive Temperature Regulation.

Thermal management presents a significant challenge in electric design, particularly in densely packed electronic systems. This study proposes a theoretical model for radiative bionic skin that emulates human skin, enabling the self-adaptive modulation of the thermal exhaustion rate to maintain homeostasis for objects covered by the skin in fluctuating thermal environments. The proposed artificial skin consists of phase change material (VO2) nanoparticles embedded in a low-loss matrix situated on a metallic substrate with a minimal thickness of several micrometers. The findings from our theoretical analyses indicate that substantial alterations in thermal radiation power around the phase transition temperature of 340 K enable a silicone substrate to sustain a relatively stable temperature, with variations confined to ±6 K, despite external heat fluxes ranging from 150 to 450 W/m2. Furthermore, to improve the spectral resemblance to natural skin, a plasmonic surface composed of self-assembled silver nanocubes is incorporated, allowing for modifications to the visible light properties of the bionic skin while maintaining its infrared characteristics. This theoretical investigation offers a cost-effective and conformal approach to the design of ultra-compact, fully passive, and versatile thermal management solutions for robotic systems and related technologies.

Harmonizing aerodynamic efficiency, stability, and acoustic excellence: multi-objective optimization for electric vehicle rear-end design

Harmonizing aerodynamic efficiency, stability, and acoustic excellence: multi-objective optimization for electric vehicle rear-end design

Mathematical Optimization of Electric Motor Designs

When designing and improving the performance of electric motors, mathematical optimization is very important. The main goals are usually to make the motors more efficient, lower their costs, and work within certain limits. The main topic of this study is how to improve electric motor designs using advanced optimization methods, such as multi-objective optimization. The study looks at how to use different types of computer algorithms together, like gradient-based methods, genetic algorithms, and particle swarm optimization, to solve difficult design problems. Some of these problems are reducing energy loss, making the best use of materials, and finding the right balance between different performance measures such as speed, power density, and temperature management. Building mathematical models of the motor's physical and functional features is the first step in this method. Then, optimization methods are applied to these models. Finite element analysis (FEA) is used to correctly model the motor's electric behavior. This makes sure that the optimization process considers physical limits and nonlinearities that happen in the real world. The study also looks into how different design factors, like the shape of the motor's core, the way the windings are set up, and the materials used, affect its total performance. The study also looks at the fact that motor design has more than one goal by using Pareto front analysis to find the best ways to balance different goals. This lets people come up with motor designs that are good for speed, efficiency, and cost-effectiveness. Case studies of the design of different kinds of electric motors, such as induction motors, permanent magnet synchronous motors (PMSMs), and brushless DC motors (BLDCs), show that the proposed optimization method works well. The results show that mathematical optimization has the ability to make the planning process a lot better. This could lead to motors that work better, cost less, and are better suited to certain uses. The study ends with a talk of how optimization methods could be used in the future to improve the design of electric motors, especially for new technologies like electric cars and green energy systems.

Hybrid regret-based p-robust and distributionally robust optimization models for electric vehicle charging station network design

The rise of electric vehicles (EVs) in recent years has underscored the need for well-established Electric Vehicle Charging Station (EVCS) infrastructure. The lack of a reliable network of EVCS is a significant obstacle to the widespread adoption of EVs. Also, one often overlooked aspect of electric vehicles is the importance of using renewable energy sources to recharge their batteries. Without sustainable energy, EVs could produce more greenhouse gas emissions than traditional vehicles. Therefore, it seems essential to design networks of EVCSs that depend on renewable sources to meet their energy needs and ensure convenient access for customers while reducing environmental impact. This study proposes a two-stage stochastic optimization model to determine the optimal locations and capacities for EVCSs and supporting sustainable energy plants. The model considers uncertain factors such as vehicle flows and renewable energy supply. The model combines a hybrid adjusted p-robust and min–max regret approach to address uncertainties to ensure robustness in the network design process. Two approaches including Sample Average Approximation (SAA) and Fuzzy C-means Clustering are employed to solve the model efficiently. Additionally, the Distributionally Robust Optimization (DRO) approach accounts for uncertainty in the probability distribution function of scenarios. The results of these solution approaches are validated through parameter realization, which involves simulating uncertain parameters and verifying the robustness of the solutions obtained from each approach. This validation process is done by comparing the obtained results with the optimal values based on two criteria: the sum of deviation from optimality and the maximum deviation from optimality. By adopting these solution methods, the proposed methodology contributes to developing efficient and sustainable EV infrastructure. This methodology gives investors and city officials robust decision support for establishing EVCS.

Closing the Gap between Electrical and Physical Design Steps with an Analog IC Placement Optimizer Enhanced with Machine-Learning-Based Post-Layout Performance Regressors

The design of integrated circuits in the analog spectrum is intricate due to the signals’ continuous nature. Additionally, it is strongly affected by the physical implementation of their devices and interconnections on the layout, a design task that has stubbornly defied all automation attempts. In this paper, one limitative factor is identified that must be addressed to finally push automation tools into the analog integrated circuit design flow: accurate assessment of post-layout performance degradation. For this purpose, a performance-driven placement generator highly integrated with off-the-shelf tools already adopted by circuit/layout designers, i.e., circuit simulator, verification tools (layout-versus-schematic) and layout extractor, is proposed. Toward maximum post-layout accuracy, this generator promotes an exhaustive simulation-based synthesis, extracting, simulating and verifying the post-layout functional behavior of every candidate floorplan. Additionally, to bypass the time-consuming extractions/simulations and accelerate synthesis, novel post-layout performance regressors based on different highly accurate machine learning techniques are also being developed. The data used to train them can be directly and conveniently acquired from previous precise post-placement simulations. Experimental results over two analog circuit structures show that a set of performance regressors based on tree-based models, while operating on compressed design spaces, allow for the speeding up of synthesis by more than 20×, which represents a step toward an efficient fully automatic performance-driven analog integrated circuit design flow.

Design, Kinematics, and Deployment of a Continuum Underwater Vehicle-Manipulator System

Abstract Underwater vehicle-manipulator systems (UVMSs) are underwater robots equipped with one or more manipulators to perform intervention missions. This article provides the mechanical, electrical, and software design of a novel UVMS equipped with a continuum manipulator, referred to as a continuum-UVMS. A kinematic model for the continuum-UVMS is derived in order to build an algorithm to resolve the robot’s redundancy and generate joint space commands. Different methods to optimize the trajectory for specific tasks are proposed using both the weighted least norm solution and the gradient projection method. Kinematic simulation results are analyzed to assess the performance of the proposed algorithm. Finally, the continuum-UVMS is deployed in an experimental demonstration in which both teleoperation and autonomous control are tested for a given reference trajectory.

Dynamic analysis of two-stage steering reducer for electric vehicle based on ROMAX

Abstract. With the rapid development of electric vehicles in the modern market, the efficiency and performance requirements of electric vehicle transmission systems are getting higher and higher. As an important part of the electric vehicle reducer, the steering reducer can make full use of the advantages of the two-stage deceleration transmission, achieve more efficient power transmission, and improve the acceleration performance and driving performance of electric vehicles. This paper aims to design a two-stage steering reducer for an electric vehicle, which adopts planetary gear transmission type. Firstly, the working principle of the electric vehicle steering reducer is analyzed, and the overall design scheme of the reducer is drawn up. Secondly, the transmission system parameters of the reducer are designed and calculated, including transmission ratio distribution, tooth number ratio calculation, etc. Secondly, the transmission structure of the reducer is designed, including input shaft, output shaft, first planetary gear train, second planetary gear train, reducer shell, bearing, etc., and the reducer model is drawn by three-dimensional software Solidworks. Finally, the strength and deformation check analysis of the two-stage reducer is carried out by using the finite element software ROMAX to evaluate the performance of the designed reducer under this working condition. The full text design has a certain reference, and can be applied to other electric vehicle reducer design to provide theoretical guidance.

Electric Bicycle Battery Charging System Design Using Solar Panel

This solar-powered electric bicycle charging system design and development uses solar panels as a renewable energy source to charge the battery, which drives the electric motor. The system consists of four main components: solar panel, solar charge controller, battery, and electric motor. The solar panels are connected in parallel to generate 36V, which is then stored in the battery. The system is designed to optimize energy harvesting and reduce dependence on fossil fuels. Testing was conducted with research stages, including system design, hardware implementation, and testing. The results show that the system can charge the battery efficiently, with a charging time of 3 hours. The system was also tested under different conditions, including morning, afternoon, and night, to evaluate its performance. It can be concluded that the solar-powered electric bicycle charging system is a viable alternative to traditional fossil fuel-based systems. The system is environmentally friendly, reduces energy dependence, and provides a sustainable solution for transportation. The results of this research can be used to develop a more efficient and sustainable transportation system.

Blended Learning Approach in TVET Colleges: Lecturers and Students Perceptions of Teaching and Learning Practical Subjects Online

With the advent of artificial intelligence, institutions are now adopting the Blended learning approach, which infuses face-to-face strategies and online teaching methodologies. A blended learning approach is a way to enhance the quality of teaching and learning in higher education institutions, but how feasible is that in TVET Colleges? This study thus investigated the global perceptions of lecturers and students in Technical Vocational Education (TVET) colleges regarding Blended learning for teaching practical subjects online. It examined the challenges and opportunities of moving practical learning online, considering the unique requirements of first-hand subjects like Electrical Engineering, Tourism, Hospitality, Art, and Design. The study utilised articles from Google Scholar and Science Direct databases to comprehensively understand the effectiveness of Blended learning in the TVET context. Using Blended learning as a theoretical framework, a new methodological approach, and a promising blueprint for transforming higher education, the study adopted an integrative literature review methodology to synthesise identified cases and draw themes from the survey. The study brings new knowledge regarding the significant shift in online learning usage during and after the COVID-19 pandemic, particularly in vocational education, providing valuable insights for designing effective online learning strategies. Keywords: Artificial Intelligence, Blended Learning, Integrative Literature Review, Technical Vocational Education (TVET) Colleges

A Survey on Quantum Computing Architectures: Implications for Electrical Circuit Design and Optimization

Quantum computing, a paradigm shift with the potential to revolutionize fields ranging from cryptography to optimization, is rapidly evolving. This survey delves into the key quantum computing architectures—gate-based and quantum annealing—and their profound impact on circuit design and optimization. Gate-based systems, such as those utilizing superconducting qubits and trapped ions, offer versatile platforms capable of executing a broad array of quantum algorithms. However, they present unique challenges for circuit designers, including noise sensitivity, scalability, and the need for complex error correction circuits. Quantum annealing, exemplified by D-Wave systems, offers a more specialized approach to solving optimization problems, requiring distinct circuit design considerations focused on energy efficiency and coherence preservation.This paper reviews the foundational research on these architectures, compares their respective design challenges, and examines the optimization techniques that have emerged in response to these challenges. Gate count reduction, error correction, and hybrid quantum-classical methods are highlighted as key approaches for improving circuit efficiency and scalability in quantum systems. Furthermore, this survey emphasizes the importance of interdisciplinary collaboration between quantum physicists, electrical engineers, and computer scientists to address the complex challenges associated with quantum circuit design. By combining expertise from these fields, researchers can develop innovative solutions that bridge the gap between theoretical concepts and practical hardware implementations. Ultimately, this survey underscores the need for continued innovation at the intersection of quantum computing and electrical engineering, as researchers strive to overcome current limitations and develop practical, large-scale quantum systems that can harness the full potential of this revolutionary technology.

Comparative Analysis and Integrated Methodology for the Electrical Design and Performance Evaluation of Thermoelectric Generators (TEGs) in Energy Harvesting Applications

Comparative Analysis and Integrated Methodology for the Electrical Design and Performance Evaluation of Thermoelectric Generators (TEGs) in Energy Harvesting Applications