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Flexibility has increasingly gained attention within the field of electrification and energy transition where a common objective is to reduce the electricity consumption peaks. However, flexibility can increase the risk of grid congestion depending on where and when and it is used, thus an overall system perspective needs to be considered to ensure an effective energy transition. This paper presents a framework to assess electricity users' contributions to grid load peaks by splitting electricity consumption data into subsets based on time and temperature. The data in each subset is separately correlated with the grid load using three correlation measures to assess how the user's consumption changes at the same time as typical grid peak loads occur. The framework is implemented on four different types of business activities at Uppsala municipality in Sweden, which is a large public entity, to explore their behaviors and assess their grid peak load contributions. The results of this study conclude that all four activities generally contribute to the grid peak loads, but that differences exist. These differences are not visible without splitting the data, and not doing so can lead to unrepresentative conclusions. The presented framework can identify activities that contribute the most to unfavorable grid peaks, providing a tool for decision-makers to enable an accelerated energy transition.

Existence of periodical solutions, i.e. cycles, in the Impulsive Goodwin's Oscillator (IGO) with the continuous part of an arbitrary order m is considered. The original IGO with a third-order continuous part is a hybrid model that portrays a chemical or biochemical system composed of three substances represented by their concentrations and arranged in a cascade. The first substance in the chain is introduced via an impulsive feedback where both the impulse frequency and weights are modulated by the measured output of the continuous part. It is shown that, under the standard assumptions on the IGO, a positive periodic solution with one firing of the pulse-modulated feedback in the least period also exists in models with any m >= 1. Furthermore, the uniqueness of this 1-cycle is proved for the IGO with m <= 10 whereas, for m>10, the uniqueness can still be guaranteed under mild assumptions on the frequency modulation function.

As the world continues to deal with the effects of climate change, the need for carbon neu-trality becomes increasingly urgent. To achieve this goal, many countries are exploring the potential of distributed energy resources to reduce their dependence on fossil fuels and transition to renewable sources of energy. A microgrid is a small, independent energy system that can operate on its own or in connection with the main power grid. It integrates different energy sources like solar panels and batteries. Inverters are crucial in microgrids as they facilitate the seamless integration of various energy sources and contribute to grid stability. These inverters can be categorized into three distinct groups: grid-feeding, grid-supporting, and grid-forming. Each category serves a unique purpose, from synchronizing power with the main grid to providing support during grid disturbances and even enabling autonomous grid operation. These varying inverter functionalities contribute to the adaptability and resilience of microgrids, ensuring they can meet diverse energy needs and operate effectively in a range of scenarios. The thesis provides a comprehensive background of critical aspects of power systems and distributed energy resources, specifically focusing on microgrids and their significance in the evolving energy landscape. A particular emphasis is placed on the crucial functions served by inverters within microgrid architectures. Additionally, the thesis delves into fault analysis and mitigation strategies to ensure system resilience. Furthermore, the study highlights the importance of hybrid energy storage systems in enhancing the power quality of wave energy converters, achieved through the mitigation of power fluctuations. The outcomes and results of this thesis were developed and simulated using two platforms: MATLAB/Simulink and PSCAD. It delves into five distinct scenarios, each examining microgrid inverters from different perspectives in terms of circuit topology and co

The present work proposes the development of a simulation platform that integrates the PSCAD software with python language, capable of portraying realistic and dynamic characteristics of the operation of an electrical system with high insertion of DERs. This work considers a scenario with high insertion of alternative sources, which are characterized by intermittent conditions of generation and consumption, therefore causing technical-economic challenges in the operation of the network. In this way, an electrical network will be created with photovoltaic systems, electric vehicles and batterys, in addition to local, coordinated and optimization controls. Local and coordinated controls act on network undervoltage and overvoltage problems. The optimized control, created in Python and based on a Predictive Control Model, determines the optimal operating curve for the batterys, reducing the energy cost of the system, being activated when the network does not present technical problems. Results showed a reduction of more than 16% in the energy cost of the network when enabling the optimized control, keeping the voltage profiles within regulatory limits. Thus, the proposed platform manages to carry out the technical-economic monitoring of the network, becoming a promising tool for the creation of active network management algorithms in dynamic simulations.

Porous organic polymers (POPs) constitute an important class of sorbents studied in various adsorption and separation processes. Their unique properties, including high surface areas, adjustable pore sizes, and surface chemistries make them ideal candidates for CO2 capture. To achieve a high CO2 adsorption capacity and selectivity, particularly at the low partition pressures required for post-combustion CO2 capture or direct capture of CO2 from the atmosphere, incorporating amines onto the polymer frameworks or within the pores has shown much promise. This review provides a comprehensive summary of recent studies on the synthesis and CO2 capture performance of amine-functionalized POPs. The review also provides a detailed discussion of structure-performance relationships, focusing on how the loading amount and amine type influence CO2 capture capacity, CO2/N2 selectivity, heat of adsorption, sorption kinetics, and recyclability of POPs. Additionally, the authors offer their perspective on the challenges associated with the practical implementation of amine-modified POPs for CO2 capture.

The electrification of remote islands has long been a subject of research interest, primarily because of their historical reliance on fossil fuels, leading to a significant carbon footprint. Recent advancements in wave energy converters offer a promising avenue to make these islands more self-sustainable while considerably reducing carbon emissions. However, the persistent issue of voltage dips due to weaker grids continues to pose a challenge. This study introduces a multi-mode converter control strategy with the goal of electrifying remote islands, employing a linear generator-based wave energy converter in a unified electrical model. Various scenarios, including voltage dips and mainland grid disconnection, are simulated using MATLAB/Simulink. The study demonstrates the converter's ability to transition swiftly and smoothly in response to these scenarios, ensuring an uninterrupted power supply. Furthermore, the analysis indicates that the power quality at the point of common coupling remains well within acceptable standards.

The demand for highly efficient and dynamic electric vehicles (EVs) has increased dramatically. The traction inverter, a pivotal component in an EV powertrain, plays a crucial role. This study is dedicated to designing a traction inverter with focus on achieving high efficiency and elevated power density and mitigating electromagnetic interference (EMI) issues. To realize these objectives, autonomous gate drivers (AGDs) are proposed and designed using LTspice simulation software. The aim is to achieve zero voltage switching (ZVS) at both turn-on and turn-off through the utilization of triangular current mode (TCM) control on the gate driver. The AGDs implement a current modulation scheme by sensing the current and voltage and generating gate-source voltage signals with minimal delays. The implemented current modulation scheme by the AGDs results in an efficiency exceeding 99% for a 10 kW power rating. The sinusoidal output waveforms not only contribute to extending the motor lifespan by mitigating sharp-edge voltages but also bring advantages such as reduced switch stress, decreased EMI, and simplified thermal management., QC 20240321

The current power system is a network of electrical components forming a physical system. It is experiencing changes, such as the deployment of electric vehicles and distributed energy sources. Meanwhile, cybernetworks are becoming coupled into the physical grid to an increasing degree. This transformation of electrical grids into smart grids is often the focus of research literature. It is strongly linked to the concurrent evolution of the cyberinfrastructure, which is the focus of this thesis. It aims to support the incremental upgrade of data systems, taking real-world constraints into account, as well as opportunities offered by sensors and machine learning. With this background, two research questions have been identified: i) How to use available data more efficiently to improve asset management? and ii) How much and which kind of new data are actually needed? With regard to the first research question, ways to use available data more efficiently are investigated in collaboration with a distribution system operator (DSO). One option is for DSOs to adopt best practices in terms of data management, which include: de-silo data, enhance reporting practices, and automatize tasks. To illustrate how combining available data may deliver additional relevant information, criticality indices have been calculated and assigned to components of a substation, by combining outage data, operation data and network diagram. Another option is to develop machine learning algorithms to perform specific or new tasks. A failure warning system has been developed using machine learning to leverage existing data about power components. It provides component-specific red flags, in situations where the widespread installation of component-specific sensors is unrealistic. With regard to the second research question, a methodology has been developed to identify which data are actually needed. The approach is scenario-based, and formalizes mathematically the relations between data, grid manag, Det nuvarande kraftsystemet är ett nät av elektriska apparater som utgör ett fysiskt system. Det upplever förändringar, till exempel ökande antal elfordon och distribuerade energikällor. Samtidigt kopplas cybernätverk in i det fysiska nätet i allt högre grad. Dessa förändringar förvandlar elnätet till ett smart nät som är ofta i fokus för forskningslitteraturen. Den omvandlingen är starkt kopplat till den samtidiga utvecklingen av cyberinfrastrukturen, som är fokus för denna avhandling. Det syftar till att stödja den stegvisa uppgraderingen av datasystem, med hänsyn till verkliga begränsningar, såväl som möjligheter som sensorer och maskininlärning erbjuder. Med denna bakgrund har två forskningsfrågor identifierats: i) Hur kan man använda tillgängliga data mer effektivt för att förbättra nätförvaltning? och ii) Hur mycket och vilken typ av nya data behövs egentligen? När det gäller den första forskningsfrågan undersöks sätt att använda till-gängliga data mer effektivt i samarbete med en elnätsägare. Ett alternativ är att elnätsägare använder erkänt välfungerande metoder när det gäller datahantering, som inkluderar: kombinera data från olika källor, förbättra rapporteringsmetoder och automatisera uppgifter. För att illustrera hur en kombination av tillgängliga data kan ge ytterligare relevant information, har kritikalitetsindex beräknats och tilldelats komponenter i en transformatorstation genom att kombinera avbrottsdata, driftdata och nätstruktur. Ett annat alternativ är att utveckla maskininlärningsalgoritmer för att genomföra specifika eller nya uppgifter. Ett felvarningssystem har utvecklats med hjälp av maskininlärning för att utnyttja befintliga data om elkraftapparater. Det ger apparatspecifika röda flaggor, i situationer där en utbredda installation av särskilda sensorer är orealistisk. När det gäller den andra forskningsfrågan har en metodik utvecklats för att identifiera vilka data som faktiskt behövs. Tillvägagångssättet är scenariobaserat och formaliser, QC 20240321

This article presents a novel design of a full-band E -plane waveguide bend for direct flange-to-chip connection. The proposed E -plane bend concept is validated with a reduced-height bend prototype designed for standard WR-3.4 waveguide flange-to-chip connection, fabricated by silicon micromachining, and characterized by de-embedding the S -parameters with a custom-made offset-short calibration kit. The measured insertion and return losses are 0.08–0.3 dB and better than 14.7 dB, respectively, for the whole waveguide band of 220–320 GHz, and better than 0.15 and 20 dB, respectively, for more than 80% of the waveguide band. The measured results are in excellent agreement with the simulation data. Besides, a two-port waveguide structure with WR-3.4 interfaces is fabricated and measured to confirm the functionality of the designed E -plane bend. Furthermore, sensitivity analysis shows the robustness of the proposed geometry against fabrication tolerances., QC 20240318

To be able to efficiently maintain a continuous balance between supply and demand in power systems with high shares of variable renewable energy (VRE) sources, a variety of studies related to the topic are needed. A fundamental input parameter for such studies is an assessment of the power system's physical needs for balancing power, in form of power imbalances. This article presents a new model for simulating physical power imbalances with a 1-minute time resolution based on multi-area economic dispatch simulations. Compared to existing models with the same purpose, the new model includes the combination of simulating power imbalances with 1-minute time resolution, simulating forecast uncertainty, simulating the continuous behaviour of all power system components and simulating the transmission for netting of power imbalances between balancing areas. By applying the model to a case study of the Nordic synchronous power system in year 2045, the impact of including these features in the model is highlighted. Case study results also show that the size and pattern of power imbalances much depends on the characteristics of a balancing area, in terms of electricity demand, available generation technologies and interconnections to other balancing areas., QC 20240325

This article presents a novel subterahertz (sub-THz) crossover waveguide switch concept operating in the 220–260-GHz frequency band. The crossover switching circuit is implemented by two hybrid couplers and two single-pole-single-throw (SPST) switching mechanisms, utilizing microelectromechanically reconfigurable switching surfaces. The silicon-micromachined crossover switch prototype is very compact, with a total footprint of 5.6 × 5 × 1.2 mm, including four standard WR-3.4 waveguide ports and the waveguide routing to these ports. The measured insertion loss (IL) is 0.9–1.4 dB in the crossover state and 0.8–1.3 dB in the straight state from 220 to 260 GHz, and the isolation (ISO) is better than 29.3 and 29 dB, respectively, for these states. The measured return loss (RL) is better than 14 dB in the crossover state and better than 13.6 dB in the straight state. Besides, the measured input-to-input ISO is better than 13.7 and 34 dB in the crossover and straight states, respectively. The measurement results are in excellent agreement with the simulation data. Moreover, the signal paths are fully symmetric for all input-to-output signal paths, making the crossover switching circuit suitable for redundancy applications., QC 20240325

This article presents a highly integrated novel silicon micromachined single-pole-single-throw waveguide switch based on two microelectromechanically reconfigurable switching surfaces (MEMS-RSs), which allows optimizing the switching performance by tuning the interference between the two such MEMS-RSs utilizing integrated electrostatic comb-drive actuators. The switch prototype is implemented with axially aligned standard WR-3.4 waveguide ports with a total footprint of 3 mm×3.5 mm×1.2 mm. The measured blocking ( off ) state insertion loss (isolation) and return loss, measured between two standard WR-3.4 waveguide flanges, are 28.5–32.5 dB and better than 0.7 dB, and the propagating ( on ) state insertion and return losses are 0.7–1.2 dB and better than 17 dB in the 220–290 GHz frequency band, respectively. The measured results were in excellent agreement with the simulation data, implying 27.5% fractional bandwidth, which is very close to a full waveguide band performance. For further investigations, two variants of the switching circuit with only a single MEMS-RS and without any MEMS-RSs have also been fabricated. The single MEMS-RS switch achieved the off -state isolation, on -state insertion loss, and return loss of only 11.5–12.5 dB, 0.8–1.3 dB, and better than 12 dB from 220 to 274 GHz, respectively, which clearly indicates the drastic performance improvement of the interference-based double MEMS-RS switch design. Moreover, measurement of the waveguide-only reference structure showed that the waveguide section alone attributed to 0.2–0.5 dB of the measured on -state insertion loss of the double MEMS-RS switch, and the rest is due to the introduction of the MEMS-RSs inside the waveguides., QC 20240315

Renewable energy sources (RES) and electric vehicles (EVs) are two promising technologies that are widely recognized as key components for achieving sustainable cities. However, intermittent RES generation and increased peak load due to EV charging can pose technical challenges for the power systems. Many studies have shown that improved load matching through energy system optimization can minimize these challenges. This paper assesses the optimal urban-scale energy matching potentials in a net-zero energy city powered by wind and solar energy, considering three EV charging scenarios: opportunistic charging, smart charging, and vehicle-to-grid (V2G). This paper takes a city on the west coast of Sweden as a case study. The smart charging and V2G schemes in this study aim to minimize the mismatch between generation and load and are formulated as quadratic programming problems. Results show that the optimal load matching performance is achieved in a net-zero energy city with the V2G scheme and a wind-PV electricity production share of 70:30. The load matching performance is increased from 68% in the opportunistic charging scenario to 73% in the smart charging scenario and to 84% in the V2G scenario. It is also shown that a 2.4 GWh EV battery participating in the V2G scheme equals 1.4 GWh stationary energy storage in improving urban-scale load matching performance. The findings in this paper indicate a high potential from EV flexibility in improving urban energy system performance., De två första författarna delar förstaförfattarskapet

Dynamic thermal rating allows transformers to operate beyond the nameplate rating according to the actual weather and loading conditions. This paper proposes a methodology to improve the application of this technology in the design of new transformers or in the operation of existing transformers connected to wind farms by accurately predicting their load profiles, accounting for the influence of wake effect and turbine availability. Specifically, the variation of turbine availability due to the intermittent wind is considered in the load profile estimation. Additionally, a correction method, which can be incorporated into any wake model, is proposed to improve the accuracy of wake loss computation. A case study shows that the wake effect and the changing turbine availability shorten the time that the transformers maintain at full load, thereby reducing the aging rate of the wind farm export transformers. The findings suggest that considering these two factors in the DTR application can benefit the longevity and efficiency of wind farm exported transformers.

An approximate computational model of an electromagnetic (EM) pulse excited thin-wire antenna is developed. The presented solution methodology is based on the Cagniard-deHoop method of moments (CdH-MoM) and Hallén's approximation of the thin-wire model. It is shown that the proposed time-domain (TD) solution leads to an inversion-free, efficient updating procedure that mitigates the marching-on-in-time accumulation error. An illustrative numerical example demonstrates the validity of the proposed model., Validerad;2024;Nivå 2;2024-03-18 (hanlid);Full text license: CC BY

Environmental transmission electron microscopy (ETEM) can provide unique insights into nanocarbon oxidation processes through atomic resolution and real time imaging of materials at high temperatures in reactive atmospheres. However, the electron beam can also influence the reaction rates, and even alter the processes entirely, complicating the interpretation of the in situ observations. Many mechanisms have been proposed to account for the impact of the electron beam, predominantly involving ionization of the oxidative gases to form more reactive species. However, these mechanisms have not been critically evaluated and compared to predictions from theory. Here, we evaluate the impact of the electron beam both qualitatively (oxidation mode and spatial extent) and quantitatively (oxidation rates), using high resolution imaging and electron energy loss spectroscopy, at different electron energies and dose rates. We demonstrate that transient defects generated by elastic scattering, forming highly active sites for carbon abstraction by oxygen, is the main mechanism for the enhanced oxidation rates observed in situ. This is evident from an insensitivity to electron energy and saturation of the effects at high electron dose rates. To avoid undue influence of the electron beam in future ETEM studies, we therefore recommend conditions where the intrinsic oxidation dominates over the beam-enhanced oxidation (note that no conditions are completely “safe”) and extensive comparisons with other methods., Validerad;2023;Nivå 2;2023-12-06 (hanlid);Full text license: CC BY

For inclusion in large-scale power system models, various aggregations and simplifications in the modeling of relevant actors are needed. This paper focuses on reduced models of hydropower, so called area Equivalent models. They use a simplified topology but are not a direct aggregation of the real hydropower system. Instead, the area Equivalent is constructed to mimic the simulated power production of a more detailed hydropower reference model. Here, this goal is fulfilled by formulating a bilevel problem minimizing the difference in simulated power production between the area Equivalent and its reference. Solving this can be computationally heavy. Thus, for a fast solution of this bilevel problem, a single-level reduction is done, which is then solved using two methods. The first method includes McCormick envelopes to form a linear single-level problem. Second is a modified Benders with a relaxed sub-problem to handle the non-convex single-level. These are then also compared to Particle Swarm Optimization. Moreover, six new upper-level objective functions are investigated for a case study of hydropower in northern Sweden. The method using McCormick envelopes is fast (2–5 min), but the area Equivalent shows lower average performance. The modified Benders finds a solution in 5–31 min with good performance., QC 20240503

In this paper, a lattice-based model of balanced filters is proposed. The symmetric lattice model is derived directly from the transversal circuit model of coupled-resonators filters through a series of equivalent transformations. Expressions for S-parameters of general symmetric lattice network are derived, as well as equations for S-parameters of balanced bandpass and bandstop filters utilizing the lattice model; the dualism of their transmission and reflection coefficients is demonstrated. Two numerical examples of modelling balanced bandpass and bandstop filters are presented.

Salient pole wound field synchronous motors find many industrial applications thanks to their favorable characteristics: reactive power regulation, stiff mechanical characteristic and overall outstanding efficiency. Nevertheless, their competitiveness towards the induction motors, especially for medium and small power sizes, depends crucially on their capability to be asynchronously started as well. Regrettably, the asynchronous run-up of a synchronous motor can be sometimes very problematic because of thermal issues, torsional vibrations and grid voltage disturbances. This paper presents an alternative method of starting salient pole wound field synchronous machines by activating the field winding in a special manner, which makes it possible to mitigate the three problems at once. The suggested method is validated through a 2D finite elements simulation and by starting a 60 kVA prototype generator. The requirements for the application of the proposed run-up strategy are critically discussed together with related pros and cons.

In this paper, we introduce a novel negative coupling structure that is suitable for fabrication using milling and micromachining technologies. The proposed negative coupling structure is designed in three sections: a central bow-shaped iris is sandwiched between two sections that manipulate electromagnetic fields, causing a 180-degree phase rotation. To validate this concept, we have developed a silicon micromachined fourth-degree quasi-elliptic bandpass filter with a center frequency of 270 GHz and a fractional bandwidth of 2.2\%. The measured results of the filter display two transmission zeros in the stopband thus demonstrating the correct performance of the negative coupling structure., Part of proceedings ISBN: 979-835034532-2QC 20240430

Constant pH molecular dynamics (MD) is a powerful technique that allows the protonation state of residues to change dynamically, thereby enabling the study of pH dependence in a manner that has not been possible before. Recently, a constant pH implementation was incorporated into the GROMACS MD package. Although this implementation provides good accuracy and performance, manual modification and the preparation of simulation input files are required, which can be complicated, tedious, and prone to errors. To simplify and automate the setup process, we present phbuilder, a tool that automatically prepares constant pH MD simulations for GROMACS by modifying the input structure and topology as well as generating the necessary parameter files. phbuilder can prepare constant pH simulations from both initial structures and existing simulation systems, and it also provides functionality for performing titrations and single-site parametrizations of new titratable group types. The tool is freely available at www.gitlab.com/gromacs-constantph. We anticipate that phbuilder will make constant pH simulations easier to set up, thereby making them more accessible to the GROMACS user community., QC 20240305

Grid-forming converters can emulate the behavior of a synchronous generator through frequency droop control. The stability of grid-forming modular multilevel converters can be studied via the impedance-based stability criterion. This paper presents an ac-side impedance model of a grid-forming modular multilevel converter which includes a complete grid-forming control structure. The impact of different control schemes and parameters on the closed-loop output impedance of the converter is thoroughly analyzed and the learnings have been used in mitigating undesired control interactions with the grid. The results are verified through simulations in time- and frequency-domains along with experiments on a down-scaled laboratory prototype., QC 20240301

Stochastic nonlinear dynamical systems are ubiquitous in modern, real-world applications. Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains a challenging problem. The majority of existing methods employ maximum likelihood or Bayesian estimation. However, these methods suffer from some limitations, most notably the substantial computational time for inference coupled with limited flexibility in application. In this work, we propose DeepBayes estimators that leverage the power of deep recurrent neural networks. The method consists of first training a recurrent neural network to minimize the mean-squared estimation error over a set of synthetically generated data using models drawn from the model set of interest. The a priori trained estimator can then be used directly for inference by evaluating the network with the estimation data. The deep recurrent neural network architectures can be trained offline and ensure significant time savings during inference. We experiment with two popular recurrent neural networks — long short term memory network (LSTM) and gated recurrent unit (GRU). We demonstrate the applicability of our proposed method on different example models and perform detailed comparisons with state-of-the-art approaches. We also provide a study on a real-world nonlinear benchmark problem. The experimental evaluations show that the proposed approach is asymptotically as good as the Bayes estimator., QC 20231129

This paper introduces a novel filtering approach that employs integrated periodic structures with a conventional Vivaldi antenna to achieve a fully integrated bandpass filtering antenna. The approach results in a wide out-of-band suppression, high passband selectivity, adjustable operational bandwidth, and low insertion loss. The proposed filtering approach maintains the original size of the conventional Vivaldi antenna (base antenna) without requiring additional modifications. To validate the approach, we present two filtering Vivaldi antennas: filtering antenna I (center frequency: 18GHz, fractional bandwidth: 21%, insertion loss: 0.32dB) and filtering antenna II (center frequency: 6.5GHz, fractional bandwidth: 12%, insertion loss: 0.6dB). Their wide out-of-band gain suppression (typically >= 15dB) covers the conventional Vivaldi antenna's frequency range (4-24GHz). A prototype of the filtering antenna I is manufactured. Its measurement results validate the proposed approach and show good agreement with the simulated reflection coefficient, realized gain, and radiation patterns. The features of the proposed filtering antenna approach, make it suitable for various applications requiring efficient frequency filtering., QC 20240216

The phase-locked loop (PLL) is a commonly used synchronization control method for grid-tied inverters. The PLL-synchronized inverters tend to have poor stability robustness with weak grid interconnections, especially when the PLL is designed with a high control bandwidth. To tackle this challenge, this article proposes an enhanced q-axis voltage-integral damping control, which not only stabilizes PLL-synchronized inverters in weak grids but also lifts the restriction on PLL bandwidth. This superior feature enables inverters to operate stably in ultraweak grids and with a superior transient response. The experimental tests confirm the performance of the method with 400-Hz PLL bandwidth under a short-circuit ratio of 1.28 of the grids., QC 20240206

This paper presents a compact penta-band Multiple Input Multiple Output (MIMO) Dielectric Resonator Antenna (DRA). The proposed MIMO DRA consists of two rectangular DRs incorporating complementary split ring resonators (CSRRs) and an inverted T-shaped slot. The MIMO DRA achieves low impedance matching through simple microstrip feedlines, which is further enhanced by integrating a microstrip line with the DRA itself. Remarkably, the introduction of CSRRs enables the design to resonate at five distinct frequency bands, namely 2.8-GHz, 4.4-GHz, 5.8-GHz, 6.4-GHz, and 6.9-GHz. The simulated properties of the MIMO DRA were validated through over-the-air measurements performed on a fabricated prototype. The proposed MIMO design demonstrates impedance bandwidths of 2.09 %, 2.43 %, 2.17 %, 2.55 %, and 4.91 % at the above respective resonance frequencies. The proposed design exhibits exceptional stability in radiation pattern, featuring a noteworthy peak gain of 6.08 dBi and an efficiency of 91.35 %. Notably, the incorporation of an inverted T-shaped slot effectively enhances isolation between the MIMO elements, achieving a maximum diversity gain of 10 dB and an envelope correlation coefficient of 0.005 over 20-dB isolation. A good agreement between the simulation and measured results is obtained, which underscores the suitability of the CSRR-based MIMO DRA for multiband wireless applications (blue tooth, radio astronomy, remote sensing, WIFI, satellite television and 6G), making it a very valuable contribution to the field., QC 20240201

Multiphase electrical machines are inherently fault tolerant due to the higher number of phases. An important step in achieving a fault tolerant control is the ability to detect and identify the fault. In variable phase-pole drives, which are a class of multiphase machines that change the number of pole pairs during real-time operation, there are further unexplored possibilities. Based on the harmonic plane decomposition theory for variable phase-pole machines, a fault detection and identification algorithm is proposed, which analyzes the spatial harmonics of the current distribution. The fault detection is fast, operation independent, non-invasive, parameter-insensitive, and computationally simple. Experimental tests validate the proposed method., QC 20240110

Considering inverter as the source of electromagnetic emission signals in a photovoltaic (PV) plant, a comprehensive set of measurements of conducted emissions at the input and output of the inverter in a 10 kW PV plant are presented. These are particularly relevant on the backdrop of (a) ban of products in the EU market due to non-compliance and (b) the increased switching frequency in the inverters ( 100s of kHz) in near future. Specifically, the common mode (CM) and differential mode (DM) currents and voltages are measured, and their frequency domain behavior is studied. It is suggested that conducted emissions from PV can be classified into three zones: viz., extremely low frequency (ELF) zone, power frequency zone, and switching frequency zone. Important observations from this exercise are measurement of harmonic contents of current with total rated current distortion (TRD), imbalance in the output voltage and low frequency ripples in the DC voltage. Frequency domain behavior of the CM quantities is studied which throws light on important points like relation between input and output CM quantities, relation between CM voltage and CM current., QC 20240131

Organic electrochemical transistors (OECTs) are promising devices for bioelectronics, such as biosensors. However, current cleanroom-based microfabrication of OECTs hinders fast prototyping and widespread adoption of this technology for low-volume, low-cost applications. To address this limitation, a versatile and scalable approach for ultrafast laser microfabrication of OECTs is herein reported, where a femtosecond laser to pattern insulating polymers (such as parylene C or polyimide) is first used, exposing the underlying metal electrodes serving as transistor terminals (source, drain, or gate). After the first patterning step, conducting polymers, such as poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS), or semiconducting polymers, are spin-coated on the device surface. Another femtosecond laser patterning step subsequently defines the active polymer area contributing to the OECT performance by disconnecting the channel and gate from the surrounding spin-coated film. The effective OECT width can be defined with high resolution (down to 2 µm) in less than a second of exposure. Micropatterning the OECT channel area significantly improved the transistor switching performance in the case of PEDOT:PSS-based transistors, speeding up the devices by two orders of magnitude. The utility of this OECT manufacturing approach is demonstrated by fabricating complementary logic (inverters) and glucose biosensors, thereby showing its potential to accelerate OECT research., QC 20240123

Open science aims to improve research accessibility, replicability, and consequently its quality. Empirical software engineering entails both data and artifacts, which may be shared more or less openly, to support transparency. However, the trade-offs involved in balancing the openness against integrity and secrecy concerns need methodological guidance. Aim. We aim to derive such advice, based on our own experiences from a research project, in the field of gaze-assisted code reviews – the Gander case. Method. We draw on literature about open data and artifacts in socio-technical research. Next, we describe our case project and derive a conceptual framework of steps in research data analysis and artifact development, using our data and artifacts as illustrating examples. Results. The conceptual framework contains 1) a categorization of humans involved as participants and their concerns, 2) four steps for data analysis, each resulting in corresponding data and meta-data, and 3) three steps of artifact distribution, matching different levels of openness. We derive a preliminary set of recommendations for open science practices for data and artifacts. Conclusion. The conceptual framework has proven useful in summarizing and discussing data and artifacts in the studied case project. We envision that the framework and recommendations will provide a foundation for further advancement of open science research practices in empirical, socio-technical software engineering.

A new transition from coplanar waveguide probe to micromachined rectangular waveguide for on-wafer device characterization is presented in this article. The transition is fabricated in the same double H-plane split silicon micromachined waveguide technology as the devices under test, requiring no additional post-processing or assembly steps. We outline the design and fabrication process of the transition for the frequency band of 220–330 GHz. A coplanar waveguide structure acts as the probing interface, with an E-field probe protruding in the waveguide cavity exciting the fundamental waveguide mode. Guard structures around the E-field probe increase the aspect ratio during deep reactive ion etching and secure its geometry. A full equivalent circuit model is provided by analyzing its working principle. RF characterization of fabricated devices is performed for both single-ended and back-to-back configurations. Measured S-parameters of the single-ended transition are obtained by applying a two-tiered calibration and are analyzed using the equivalent circuit model. The insertion loss of the single-ended transition lies between 0.3 dB and 1.5 dB over the whole band, with the return loss in excess of 8 dB. In addition to previously reported characterization of a range of devices under test the viability of the transition for on-wafer device calibration is demonstrated by characterizing a straight waveguide line, achieving an insertion loss per unit length of 0.02–0.08 dB/mm in the frequency band of 220–330 GHz., QC 20240111

Axial-flux electrical machines are ideal candidates as in-wheel motors for electrical vehicles (EVs). Due to their characteristics of high power density and compact structure, thermal management is vital for them. Lowering the temperature of the stator windings can protect the insulation material from rapid degradation and reduce the extra copper losses by decreasing their electrical resistance. Contrary to the widely reported axial-flux permanent magnet synchronous machines, thermal modeling methods of axial-flux induction machines are rarely seen in previous literature. Hence, the present work aims at investigating their thermal response based on both the finite element method (FEM) and computational fluid dynamics (CFD) techniques. In addition, a test rig is built to validate the computed results of these two thermal models with the experimental measurement. The CFD conjugate heat transfer analysis is found to be more accurate than the FEM thermal analysis in predicting the temperature distribution of different components in the machine and the temperature rise of the airflow, with lower than 5 ∘C average errors deviating from the corresponding measured data at three rotation speeds. Additionally, the CFD simulation is able to capture the backflow occurring near the outlets of the casing that has been found during the experiments., Correction in DOI 10.1016/j.csite.2024.104530QC 20240108

QC 20231019

Induction heating (IH) devices transfer the electric power to the contactless cookware via the electromagnetic field. Therefore, the temperature of cookware is measured remotely, and the early detection of cookware overheating will ensure the user’s safety as well as extend the remaining useful life of electronic components. Therefore, this work presents a clustering model for outlier detection in IH systems based on clustering algorithms and measured data using two thermal sensors. First, a healthy dataset is collected for the temperatures of inverters and cookware under different sizes and materials of cookware items, different amounts of water in cookware, and different amounts of electrical power. After that, K-means and fuzzy c-means were utilized to cluster this normal dataset, where the maximum distance between their centers and data points was selected as a threshold. Finally, the clustered model is investigated using a testing dataset that includes outliers. According to the results, the K-means algorithm detected around 96% of the produced outliers, however, the fuzzy c-means algorithm detected around 68%. In conclusion, the deployment of the clustering model in outlier detection is simple and uses only the threshold and the cluster centers.

As one of the major causes of antimicrobial resistance, beta-lactamase develops rapidly among bacteria. Detection of beta-lactamase in an efficient and low-cost point-of-care testing (POCT) way is urgently needed. However, due to the volatile environmental factors, the quantitative measurement of current POCT is often inaccurate. Herein, we demonstrate an artificial intelligence (AI)-assisted mobile health system that consists of a paper-based beta-lactamase fluorogenic probe analytical device and a smartphone-based AI cloud. An ultrafast broad-spectrum fluorogenic probe (B1) that could respond to beta-lactamase within 20 s was first synthesized, and the detection limit was determined to be 0.13 nmol/L. Meanwhile, a three-dimensional microfluidic paper-based analytical device was fabricated for integration of B1. Also, a smartphone-based AI cloud was developed to correct errors automatically and output results intelligently. This smart system could calibrate the temperature and pH in the beta-lactamase level detection in complex samples and mice infected with various bacteria, which shows the problem-solving ability in interdisciplinary research, and demonstrates potential clinical benefits., Funding Agencies|National Key R&D Program of China [2020YFA0709900]; National Natural Science Foundation of China [62288102, 22077101, 52073230]; Joint Research Funds of Department of Science & Technology of Shaanxi Province; Northwestern~Polytechnical University [2020GXLH-Z-008, 2020GXLH-Z-013]; Shaanxi Provincial Science Fund for Distinguished Young Scholars [2023-JC-JQ-32]; Key Research and Development Program of Shaanxi [2020ZDLGY13-04]; Fundamental Research Funds for the Central Universities and Innovation Foundation for Doctorate Dissertation of Northwestern Polytechnical University [CX2021121]

In recent years great advances in the field of biomedical engineering and organic electronics have been achieved. One promising application would be the regulation of blood glucose concentration in type 2 diabetes patients. This application would eliminate medication and would improve the standard of life. To achieve this goal a system is needed which receives information about the glucose concentration and reacts upon it. This output reaction could then be used to stimulate the body's own glucose regulation mechanisms. This thesis combined a glucose sensor with an artificial neuron to take the first step towards such a system. Two different artificial neurons, the Axon-Hillock neuron and the astable multivibrator, were characterized and examined upon their usability. The Axon-Hillock, build with organic electrochemical transistors, revealed that it could be applied for both regulating high and low blood glucose concentrations. The astable multivibrator, build with silicon-based transistors, was not as functional as the Axon-Hillock neuron but with more development it could become as good. The placement of the glucose sensor in the astable multivibrator circuit is essential parameter to consider. The results demonstrate that the examined system is functional and could become a part of a larger closed-loop system. Future tests on an animal model may demonstrate its viability as a treatment for type 2 diabetes.

This thesis was written in collaboration with the department of electrical engineering at Uppsala University. It evaluates and tests the potentials and limitations of using the OPAL-RT simulator as a tool for designing and developing control strategies used in microgrids with a high penetration of distributed energy resources. As the world gradually transitions to a more decentralized power system with less conventional power production, new strategies to ensure reliable and robust power transmission and distribution must be developed. The objectives of this thesis were to develop a model of a microgrid that can be used as a base for developing control strategies to limit slow voltage variations in microgrids with high penetration of distributed energy resources. When a template model and strategy for conducting this type of research had been developed the thesis started to focus on the Real-Time simulator. The power system part of the microgrid was converted into a Real-Time compatible model and was allocated to be run on one of the CPU cores of the simulator. In an iterative process, the power system was built and run on the simulator to get results concerning the size of the power system that can be simulated using the CPU cores where the limiting factor is the computational power of the cores. Since the power electronics found in the inverter part of the voltage source converter is operating at a very high switching frequency the inverter must be simulated on the simulator's FPGA. The last objective was to integrate the CPU model and the FPGA model into one model where two different time steps were used. The results show that one CPU can simulate the 18-bus power system developed during this thesis. It is also assessed that the CPU is capable of simulating a considerably bigger system if no calculations or control strategies are present in the system. The FPGA is functioning and can simulate high-frequency switching power electronics in Real Time. The con

The possibility of measuring the state of health of a NiMH battery without doing it in a lab is evaluated, the goal was to see if it was possible to perceive any differences between batteries of different states of health and whether it’s worth further exploring this solution in a more detailed manner. In order to try and extract and analyze the state of health of the batteries a series of tests consisting of discharging batteries at different lengths of times and different resistive loads were made, the voltage of the batteries being captured by a multimeter. The study shows that getting the state of health of a battery with simple components is a possibility and is useful for battery-powered items. Situations where batteries can’t deliver enough energy for the item to function can be prevented by measuring the health of the batteries and then subsequently switching out the batteries if needed.

The purpose of this project was to perform model-based diagnosis on Li-ion batteries using real-world data and sensor fusion algorithms. The data used in this project was collected and distributed by NASA and mainly consists of voltage and current measurements collected on numerous batteries that were repeatedly charged and discharged from their beginning of life, and until surpassing their end of life. The health of the batteries was decided by estimating their state of health through the increase in internal resistance. To validate the results, the increase in resistance was later compared with the decrease in charge capacity. Firstly an attempt was made to parameterize equivalent circuit models by fitting the model parameters to the data with a forgetting-factor recursive least squares filter, and a batch-wise recursive least squares filter. The forgetting-factor recursive least squares filter proved unreliable and unable to consistently be able to parameterize the models. The batch-wise recursive least squares filter was able to consistently parameterize the models providing a root mean squared error of around 0.35V in simulated voltage response tests. In total 20 equivalent circuit models were parameterized for four batteries. The models were then used in conjunction with a standard Kalman filter and an unscented Kalman filter to fur-ther estimate the increase in internal resistance of the batteries throughout their lifetime. In addition, the state of charge of the batteries was tracked through Coulomb counting and later attempted to refine via the Kalman filters. The results were partly successful as both the standard and unscented Kalman filters were able to track the state of health of the batteries, albeit to a varying degree. However, the Kalman filters were unable to improve the state of charge estimation, thus highlighting a limitation in their application. While the Kalman filters showcased limitations in tracking the state of charge, their effectivenes

Hela världen står inför nya utmaningar till följd av klimatförändringar. I syfte att mitigera människans påverkan på klimatet har den Europeiska unionen och Sverige som EU-land beslutat att fasa ut användningen av fossila drivmedel. Omställningen från fossildrivna fordon till eldrivna har påbörjats men Sverige är i dagsläget långt ifrån en fullständig omställning. Förutsättningen för att en hundraprocentig omställning skall vara möjlig är att en fungerande laddningsinfrastruktur är på plats och kan hantera det ökande behovet av laddning. I denna rapport studeras den nuvarande svenska laddningsinfrastrukturen och de faktorer som hämmar en fortsatt utveckling presenteras. Avslutningsvis redovisas det att satsning på publik- och hemmaladdning är viktiga faktorer i alla nivåer av omställning, insyn och samverkar är något som saknas idag och är något som måste finnas för att fullständig omställning skall vara möjlig., The entire world is confronting new challenges as a result of climate change. To mitigate human impact on the climate, the European Union and Sweden, as an EU member state, have decided to phase out the use of fossil fuels. The transition from fossil-fueled vehicles to electric ones has commenced, but Sweden is currently far from achieving a complete transition. The prerequisite for a complete transition is the establishment of a functional charging infrastructure capable of handling the increasing demand for charging. This report examines the current state of the Swedish charging infrastructure, highlights the factors hindering further development, and concludes by outlining the necessary steps for the Swedish charging infrastructure to facilitate transitions of 50-, 75-, and 100-percent from fossil-fueled to electric vehicles. Based on this analysis, it is evident that investments in public and home charging are critical factors at all levels of transition. Transparency and collaboration are currently lacking but are essential elements that must be in place for a successful and complete transition.

Antimicrobial resistance is considered by many prominent researcher and scientist as a profound global health crisis that us humans must face in the next decade. It is threatening the effectiveness of these once-reliable weapons against bacterial infections and leaving us susceptible to pathogenic agents. The indiscriminate overprescription of antibiotic in healthcare and animal husbandry, has led to an increased emergence of “super bugs”— a resistant strain of bacteria that were once susceptible to antibiotic—. The escalating creation of those resistant bacteria has been coupled with a proliferation of research papers that seek to explain the working mechanism of antibiotics and their efficacy on the bacterial pathogens, however these efforts often fall short of explaining the impact that antibiotics has on the bacterial metabolism. This project utilizes an established technology, specifically silicone nano-wire ion-selective field-effect transistor in an innovative approach to discern alteration in the metabolic pathways induced by various antibiotics. The methodology involves measuring extracellular acidity of the tested culture and converting it to an electrical signal to extract valuable information about the metabolic process of the bacteria, and how is altered in the presence of antibiotics. Empirical observations pertaining bacteriostatic antibiotics suggests comprehensive suppression of metabolic pathways, encompassing the efflux transition from acetyl-CoA to acetate, resulting an elevated pH level in cultures treated with bacteriostatic agents relative to their wild-type counterparts. Our experimental data also indicates a shift in bacterial metabolic and physiological responses to bactericidal antibiotic-induced stress which include an increased respiration rate, and a heightened activity of the TCA cycle in the test group with bactericidal antibiotics, causing acetate uptake from the medium and decelerating the acidification of the treated culture compar

The advent of battery electric aircraft represents a ground-breaking shift in the aviation landscape, promising a future characterized by sustainable air travel. However, the realization of this vision hinges on the establishment of a robust charging infrastructure. Striking the right balance between widespread accessibility and the flexibility and safety demands of airports poses a considerable challenge. In this study, an innovative strategy is proposed and designed to overcome the identified hurdles associated with charging electric aircraft at airports. The idea investigated includes a mobile charging system equipped with advanced energy storage capabilities, complemented by the integration of a megawatt-level charging station. This multi-component mobile system not only addresses the current challenges but also anticipates the dynamic needs of future electric aircraft. By providing both flexibility and scalability, the solution emerges as a pivotal step in shaping the trajectory of aviation electrification, fostering a sustainable and efficient future for air travel.

Recent advancements in occupancy and indoor environmental monitoring have encouraged the development of innovative solutions. This paper presents a novel approach to room occupancy detection using Wi-Fi probe requests and machine learning techniques. We propose a methodology that splits occupancy detection into two distinct subtasks: personnel presence detection, where the model predicts whether someone is present in the room, and occupancy level detection, which estimates the number of occupants on a six-level scale (ranging from 1 person to up to 25 people) based on probe requests. To achieve this, we evaluated three types of neural networks: CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit). Our experimental results show that the GRU model exhibits superior performance in both tasks. For personnel presence detection, the GRU model achieves an accuracy of 91.8%, outperforming the CNN and LSTM models with accuracies of 88.7% and 63.8%, respectively. This demonstrates the effectiveness of GRU in discerning room occupancy. Furthermore, for occupancy level detection, the GRU model achieves an accuracy of 75.1%, surpassing the CNN and LSTM models with accuracies of 47.1% and 52.8%, respectively. This research contributes to the field of occupancy detection by providing a cost-effective solution that utilizes existing Wi-Fi infrastructure and demonstrates the potential of machine learning techniques in accurately classifying room occupancy., RUGGEDISED

This study investigates the temporal and spatio-temporal correlations of solar power generation among different prosumers of Uppsala and Halmstad, Sweden. Using solar power generation data from seven prosumer in Uppsala and five in Halmstad, we evaluate the correlation of solar power production generation at specific locations correlates with itself over different time lags (autocorrelation). In addition, we examine the spatiotemporal correlations of solar power production at various locations over a range of lags using time shifted cross correlation. These spatio-temporal correlations can facilitate the development of synchronized demand response strategies and dynamic energy pricing. Moreover, the time-shifted cross-correlation analysis assists in improving forecasting models for solar power generation. By identifying significant correlations between solar generation data from different locations and applying time shifts to account for variations in weather and sunlight exposure, operators can enhance the accuracy of their predictions. This methodology enables them to fill in missing data points by leveraging correlated information from neighboring regions. Consequently, more robust forecasting models can be developed, leading to better resource allocation, improved energy management, and reduced operational uncertainties in the grid. This research highlights the evaluation and potential of utilizing spatio-temporal and temporal correlations in solar power data to enhance energy management and planning.

We report the design of an all-fiber single-mode polarization maintaining widely tunable Ho-doped master oscillator power amplifier (MOPA) system operating in the wavelength range of 2005-2135 nm based on a pre-amplifier and two power amplification stages. The single clad Ho-doped active fibers in the ring cavity based master oscillator, pre-amplifier, and power amplifiers are pumped using 1950 nm semiconductor laser diode pump sources. The amplification of the MOPA system is investigated over the entire spectral range of 2005-2135 nm. The highest output power achieved by the continuous wave (CW) Ho-doped MOPA system is 43 W at a wavelength of 2028.6 nm using pump power of 20 W with power conversion efficiency (PCE) of 90% for second power amplifier while the total pump power of Ho-doped MOPA system is 45 W. Optical bandpass filters (OBPFs) are employed between different stages of MOPA system to enable amplification over the wider spectral range of 2005-2135 nm by suppressing the amplified spontaneous emission (ASE). Not using OBPFs results in amplification that is limited to the spectral range of 2045-2130 nm. Therefore, a penalty of 45 nm can be avoided at the expense of using the OBPFs between different stages of MOPA system. This flexible MOPA system can be used in multiple applications, especially for space-borne high power transmitters operating in atmospheric transmission windows.

Highly sensitive, low-power, and chip-scale H2 gas sensors are of great interest to both academia and industry. Field-effect transistors (FETs) functionalized with Pd nanoparticles (PdNPs) have recently emerged as promising candidates for such H2 sensors. However, their sensitivity is limited by weak capacitive coupling between PdNPs and the FET channel. Herein we report a nanoscale FET gas sensor, where electrons can tunnel between the channel and PdNPs and thus equilibrate them. Gas reaction with PdNPs perturbs the equilibrium, and therefore triggers electron transfer between the channel and PdNPs via trapping or de-trapping with the PdNPs to form a new balance. This direct communication between the gas reaction and the channel enables the most efficient signal transduction. Record-high responses to 1–1000 ppm H2 at room temperature with detection limit in the low ppb regime and ultra-low power consumption of ∼300 nW are demonstrated. The same mechanism could potentially be used for ultrasensitive detection of other gases. Our results present a supersensitive FET gas sensor based on electron trapping effect in nanoparticles., Swedish Strategic Research Foundation FFL15-0174, Wallenberg Academy Fellow KAW2015-0127, Wallenberg Academy Fellow KAW2020-0190, H2020-MSCA-RISE program through “Canleish” 101007653, Olle Engkvist Foundation 196-0077

Rapidly increasing global energy demand and environmental concerns have shifted the attention of policymakers toward the large-scale integration of renewable energy resources (RERs). Wind energy is a type of RERs with vast energy potential and no environmental pollution is associated with it. The sustainable development goals: affordable and clean energy, climate action, and industry, innovation and infrastructure, can be achieved by integrating wind energy into the existing power systems. However, the integration of wind energy will bring instability challenges due to its intermittent nature. Mitigating these challenges necessitates the implementation of effective wind power forecasting models. Therefore, we have proposed a novel integrated approach, Boost-LR, for hour-ahead wind power forecasting. The Boost-LR is a multilevel technique consisting of non-parametric models, extreme gradient boosting (XgBoost), categorical boosting (CatBoost), and random forest (RF), and parametric approach, linear regression (LR). The first layer of the Boost-LR uses the boosting algorithms that process the data according to their tree development architectures and pass their intermediary forecast to LR which is deployed in layer two and processes the intermediary forecasts of layer one models to provide the final predicted wind power. To demonstrate the generalizability and robustness of the proposed study, the performance of Boost-LR is compared with the individual models of CatBoost, XgBoost, RF, deep learning networks: long short-term memory (LSTM) and gated recurrent unit (GRU), Transformer and Informer models using root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and normalized root mean square error (NRMSE). Findings demonstrate the effectiveness of the Boost-LR as its forecasting performance is superior to the compared models. The improvement in MAE of Boost-LR is recorded as to be 31.42%, 32.14%, and 27.55% for the datasets of Bruska, Jelina

The active short-circuit is a standard safety measure for permanent-magnet synchronous machine drives in electric vehicles, but it can lead to harmful torque and current transients. This paper introduces a mathematical and graphical method to determine the safe operating area of pre-fault current conditions, complying with user-defined torque bounds and preventing magnet demagnetization during short-circuit transients. The method incorporates an inductance-based motor model considering magnetic saturation, which is used to outline a strategy for transitioning to safe initial conditions in the minimum time for the available voltage. Experiments on a 35 kW permanent-magnet synchronous machine support the efficacy of the proposed strategy, offering promise for its use in automotive propulsion where compliance to safety standards such as the ISO 26262 is paramount., QC 20240813