The demand for high-temperature dielectric materials arises from numerous emerging applications such as electric vehicles, wind generators, solar converters, aerospace power conditioning, and downhole oil and gas explorations, in which the power systems and electronic devices have to operate at elevated temperatures. This article presents an overview of recent progress in the field of nanostructured dielectric materials targeted for high-temperature capacitive energy storage applications. Polymers, polymer nanocomposites, and bulk ceramics and thin films are the focus of the materials reviewed. Both commercial products and the latest research results are covered. While general design considerations are briefly discussed, emphasis is placed on material specifications oriented toward the intended high-temperature applications, such as dielectric properties, temperature stability, energy density, and charge-discharge efficiency. The advantages and shortcomings of the existing dielectric materials are identified. Challenges along with future research opportunities are highlighted at the end of this review.
Ephrem Chemali, P. Kollmeyer, Matthias Preindl
et al.
Abstract Accurate State of Charge (SOC) estimation is crucial to ensure the safe and reliable operation of Li-ion batteries, which are increasingly being used in Electric Vehicles (EV), grid-tied load-leveling applications as well as manned and unmanned aerial vehicles to name a few applications. In this paper, a novel approach using Deep Feedforward Neural Networks (DNN) is used for battery SOC estimation where battery measurements are directly mapped to SOC. Training data is generated in the lab by applying drive cycle loads at various ambient temperatures to a Li-ion battery so that the battery is exposed to variable dynamics. The DNN's ability to encode the dependencies in time into the network weights and in the process provide accurate estimates of SOC is presented. Moreover, data recorded at ambient temperatures lying between −20 °C and 25 °C are fed into the DNN during training. Once trained, this single DNN is able to estimate SOC at various ambient temperature conditions. The DNN is validated over many different datasets and achieves a Mean Absolute Error (MAE) of 1.10% over a 25 °C dataset as well as an MAE of 2.17% over a −20 °C dataset.
With the development of advanced electronic devices and electric power systems, polymer-based dielectric film capacitors with high energy storage capability have become particularly important. Compared with polymer nanocomposites with widespread attention, all-organic polymers are fundamental and have been proven to be more effective choices in the process of scalable, continuous, and large-scale industrial production, leading to many dielectric and energy storage applications. In the past decade, efforts have intensified in this field with great progress in newly discovered dielectric polymers, fundamental production technologies, and extension toward emerging computational strategies. This review summarizes the recent progress in the field of energy storage based on conventional as well as heat-resistant all-organic polymer materials with the focus on strategies to enhance the dielectric properties and energy storage performances. The key parameters of all-organic polymers, such as dielectric constant, dielectric loss, breakdown strength, energy density, and charge-discharge efficiency, have been thoroughly studied. In addition, the applications of computer-aided calculation including density functional theory, machine learning, and materials genome in rational design and performance prediction of polymer dielectrics are reviewed in detail. Based on a comprehensive understanding of recent developments, guidelines and prospects for the future development of all-organic polymer materials with dielectric and energy storage applications are proposed.
A quadcopter is a highly coupled and nonlinear multivariable system which attracted the attention of many researchers. During the last decade, several techniques and strategies have been proposed for modeling and controlling Quadcopters. In this work, dynamic modelling of the quadcopter is formulated using the Newton-Euler method, and a new control approach for quadcopter trajectory tracking is proposed. In this approach, the state space description of nonlinear quadcopter system is transformed into a linear quasi block controller decoupled form, then eigenstructure assignment using state feedback is applied. The proposed approach is used to control a quadcopter, in order to assess its performance in terms of trajectory tracking capabilities, time response performance, robustness and robust stability.
Applications of electric power, Electric apparatus and materials. Electric circuits. Electric networks
ABSTRACT The synchronously rotating reference frame (SRRF)‐based proportional‐integral (PI) control technique has been though studied in many different inverter applications, a well‐designed and clearly presented application of this technique for the three‐phase, three‐level, three‐leg and four‐wire (3P3L3L4W) grid‐connected (GC) neutral point clamped (NPC) inverter has not been found. Therefore, in this study, firstly, the 3P3L3L4W GC NPC inverter is controlled with the SRRF‐based PI controller. Then, to achieve an optimal artificial neural network (ANN) controller in terms of computational burden and control performance, two different ANN controllers, named ANN‐1 and ANN‐3, are designed with data obtained from the PI controller. The control objectives of the NPC inverter are carried out by a single ANN in ANN‐1 and by three independent ANNs in ANN‐3. The training results for ANN‐1 and ANN‐3 are approximately the same, but their computational burdens are quite different. Because ANN‐3 consists of three ANNs with minimum complexity, it has much less computational burden than ANN‐1. Their control performances are compared by using the MATLAB/Simulink, and presented for constant current reference, sudden changes in current reference, current reference with white Gaussian noise, sudden changes in DC source voltage, grid voltage imbalance, sag and swell, and different line filter parameters.
Badii Soufiane, El Habbazi Jouad, Boudaoud Abdelghani
et al.
The aim of this study is to provide a comparison of two Battery Management System (BMS) topologies, modular and centralized in a photovoltaic generator whose power is optimized by a hybrid MPPT algorithm based on the integration of an Adaptive Neuro-Fuzzy Inference System (ANFIS) and a fuzzy controller, functioning as a PI regulator. The battery pack, which is composed of lithium-ion batteries, is controlled by a fuzzy PI controller and a DC-DC buck/boost converter, which guarantees precise, flexible management of power flows. This paper stresses the compromise between the two architectures in multiple uniform irradiations through simulations 1000 W/m2, 800 W/m2 and 600 W/m2. The result shows that generalized systems are well suited for cost-sensitive, space-limited applications. However, modular BMSs have better scalability, and increased fault isolation and are thus well suited for electric vehicles, renewable energy storage, and mission-critical applications.
Abstract The T‐type three‐level rectifier has garnered significant attention due to its ability to enhance the voltage waveform quality in power systems and reduce electromagnetic interference with other equipment. To ensure high reliability in high‐power wind and photovoltaic power generation systems, conducting fault diagnosis for T‐type three‐level rectifiers is crucial. This paper first analyzes the input current characteristics of both in‐phase and out‐of‐phase dual transistor open‐circuit faults. A current‐extended observer is developed using a hybrid logic dynamic model to estimate the current value during normal operation. The residual state equation is derived by comparing this estimated current with the actual fault current. A residual information table for fault currents is created through differential solutions of the residual state equation, enabling fault localisation by comparing the residual information against predefined threshold values. Finally, the feasibility and accuracy of the proposed fault diagnosis method are validated through simulations and experiments.
Romain Cocogne, Sebastien Bilavarn, Mostafa El-Mokadem
et al.
The adoption of Interior Permanent Magnet Synchronous Motor (IPMSM) in Battery Electric Vehicle (BEV) and Plug-in Hybrid Electric Vehicle (PHEV) drives the need for innovative approaches to improve control performance and power conversion efficiency. This paper aims at evaluating advanced Model Predictive Control (MPC) strategies for IPMSM drives in a methodic comparison with the most widespread Field Oriented Control (FOC). Different extensions of direct Finite Control Set MPC (FCS-MPC) and indirect Continuous Control Set MPC (CCS-MPC) MPCs are considered and evaluated in terms of reference tracking performance, robustness, power efficiency, and complexity based on <i>Matlab, Simulink™</i> simulations. Results confirm the inherent better control quality of MPCs over FOC in general and allow us to further identify some possible directions for improvement. Moreover, indirect MPCs perform better, but complexity may prevent them from supporting real-time implementation in some cases. On the other hand, direct MPCs are less complex and reduce <i>inverter losses</i> but at the cost of increased <i>Total Harmonic Distortion (THD)</i> and decreased robustness to parameters deviations. These results also highlight various trade-offs between different predictive control strategies and their feasibility for high-performance automotive applications.
Conventional AC Power Flow (ACPF) solvers like Newton-Raphson (NR) face significant computational and convergence challenges in modern, large-scale power systems. This paper proposes a novel, two-stage hybrid method that integrates a Physics-Informed Graph Neural Network (GNN) with a robust, iterative Linear State Estimation (LSE) refinement step to produce fast and physically-consistent solutions. The GNN, trained with a physics-informed loss function featuring an efficient dynamic weighting scheme, rapidly predicts a high-quality initial system state. This prediction is then refined using an iterative, direct linear solver inspired by state estimation techniques. This LSE refinement step solves a series of linear equations to enforce physical laws, effectively bypassing the non-linearities and convergence issues of traditional solvers. The proposed GNN-LSE framework is comprehensively validated on systems ranging from small radial distribution networks (IEEE 33-bus, 69-bus) to a large, meshed transmission system (IEEE 118-bus). Results show that our GNN variants are up to $8.4 \times 10^3$ times faster than NR. The LSE refinement provides a fast route to a physically-consistent solution, while heavy-loading stress tests (120%-150% of nominal) and N-1 contingencies demonstrate the method's reliability and generalization. This work presents a powerful and flexible framework for bridging fast, data-driven models with the rigorous constraints of power system physics, offering a practical tool for real-time operations and analysis.
Suchitra Venkatesan, Krishnan Manickavasagam, Nikita Tengenkai
et al.
Electric mobility has become an essential part of the future of transportation. Detection, diagnosis and prognosis of fault in electric drives are improving the reliability, of electric vehicles (EV). Permanent magnet synchronous motor (PMSM) drives are used in a large variety of applications due to their dynamic performances, higher power density and higher efficiency. In this study, health monitoring and prognosis of PMSM is developed by creating intelligent digital twin (i‐DT) in MATLAB/Simulink. An artificial neural network (ANN) and fuzzy logic are used for mapping inputs distance, time of travel of EV and outputs casing temperature, winding temperature, time to refill the bearing lubricant, percentage deterioration of magnetic flux to compute remaining useful life (RUL) of permanent magnet (PM). Health monitoring and prognosis of EV motor using i‐DT is developed with two approaches. Firstly, in‐house health monitoring and prognosis is developed to monitor the performance of the motor in‐house. Secondly, Remote Health Monitoring and Prognosis Centre (RHMPC) is developed to monitor the performance of the motor remotely using cloud communication by the service provider of the EV. The simulation results prove that the RUL of PM and time to refill the bearing lubricant obtained by i‐DT twins theoretical results.
A review of the literature revealed that nanofluids are more effective in transferring heat than conventional fluids. Since there are significant gaps in the illumination of existing methods for enhancing heat transmission in nanomaterials, a thorough investigation of the previously outlined models is essential. This study’s main objective is to examine the Casson nanofluid’s Darcy-Forchheimer flow across a stretching sheet. Investigations are being conducted on the viscous and Joule dissipations that the electroosmosis forces (EOF) have on the casson nanofluid boundary layer. The method transforms partial differential equations originating in nanofluidic systems into nonlinear differential equation systems with the proper degree of similarity. With a precision of order 4 to 5, the nonlinear nanofluid problem is solved using the (FDM) finite difference approach (Lobatto IIIA), which is accomplished using a number of collocation locations. The ability of Lobatto IIIA to handle coupled differential equations that are very nonlinear is one of its strengths. The boundary value dilemma (bvp4c) solver, which is a component of the MATLAB software programme, is used to reduce the higher order differential equations into a first order technique and computationally analyze the simplified mathematical model. When compared to previously published studies, the data acquired showed a high degree of accuracy and symmetry. The study’s primary results included that when the Casson fluid expands, the velocity field decreases, but the electric parameter, Forchheimer number, local Reynolds number, and permeability parameter show the opposite trend. Furthermore, High temperature is connected with the non-Newtonian heating parameter and the electric parameter. This work provides insights into practical applications such nanofluidic, energy conservation, friction reduction, and power generation. However, the work makes a significant point that the flow of a Casson fluid including nanoparticles can be regulated by appropriately modifying the Casson parameter, thermophoresis parameter, and Brownian motion parameter.
In isolated sites, the extension of the electricity network requires a very high capital cost. For agriculture work, irrigation is a necessity. A possible solution is using renewable energy sources like solar power, which is environmentally friendly and available for free. This paper presents the design and simulation of a photovoltaic water pumping system for irrigation of a farm located at a place named Tsabit in Adrar southwest Algeria. A detailed approach for the design of an optimized PV water pumping system based on real water usage data is proposed. Besides, system design work and performance assessment were carried out based on hourly climatic conditions. PV SYST software is used to carry out this work. A comparison was also made between two water pumping systems with and without use of a sun tracker. From the obtained results, the use of this kind of system could have an important contribution in the social and economic development of a country, especially in the presence of a sun tracker device, where we recorded more efficiency.
Applications of electric power, Electric apparatus and materials. Electric circuits. Electric networks
While wireless sensor node (WSNs) have proliferated with the rise of the Internet of Things (IoT), uniformly sampled analog–digital converters (ADCs) have traditionally reigned paramount in the signal processing pipeline. The large volume of data generated by uniformly sampled ADCs while capturing most real-world signals, which are highly non-stationary and sparse in information content, considerably strains the power budget of WSNs during data transmission. Given the pressing need for intelligent sampling, this work proposes an extrema pulse generator devised to trigger ADCs at significant signal extrema, thereby curbing the volume of data points collected and transmitted, and mitigating transmission power draw. After providing a comprehensive signal-theoretic rationale, we construct and experimentally validate these circuits on a system-on-chip field-programmable analog array in a 350 nm complementary metal-oxide-semiconductor (MOS) process. Operating within a power range of 4.3–12.3 µW (contingent on the input bandwidth requirements), the extrema pulse generator has proven to be capable of effectively sampling both synthetic and natural signals, achieving significant reductions in data volume and signal reconstruction error. Using a nonideality-resilient reconstruction algorithm, that we develop in this work, experimental comparisons between extrema and uniform sampling show that extrema sampling achieves an 18-fold lower normalized root mean square reconstruction error for a quadratic chirp signal, despite requiring 5-fold fewer sample points. Similar improvements in both the reconstruction error and effective sampling rate objectives are found experimentally for an electrocardiogram signal. Using both theoretical and experimental methods, this work demonstrates the potential of extrema-triggered systems for extending Pareto frontiers in modern, resource-constrained sensing scenarios.