Search Results (453)

The proposed work introduces a sizing algorithm to achieve a desired linear transconductance in the optimization of operational transconductance amplifiers (OTAs) by applying the $g_m/I_D$ method to find the initial width (W) and length (L) sizes of the transistors. These size values are used to run the non-dominated sorting genetic algorithm (NSGA-II) to perform a multi-objective optimization of three OTA topologies. The $g_m/I_D$ method begins with transistor characterization using MATLAB R2024a generated look-up tables (LUTs), which map normalized transconductance of the transistor channel dimensions, and key performance metrics of a complementary metal–oxide–semiconductor (CMOS) technology. The LUTs guide the initial population generation within NSGA-II during the optimization of OTAs to achieve not only a desired transconductance but also accuracy alongside linearity, high DC gain, low power consumption, and stability. The feasible W/L size solutions provided by NSGA-II are used to enhance the CMOS design of a memristor emulator, where the OTA with the desired transconductance is adapted to tune the behavior of the memristor, demonstrating improved pinched hysteresis loop characteristics. In addition, process, voltage and temperature (PVT) variations are performed by using TSMC 180 nm CMOS technology. The memristor-based on optimized OTAs is used to design a Leaky Integrate-and-Fire (LIF) neuron, which produces identical spike counts (seven spikes) under the same input conditions, though the time period varied with a CMOS inverter scaling. It is shown that increasing transistor widths by 100 in the inverter stage, the spike quantity is altered while changing the spiking period. This highlights the role of device sizing in modulating LIF neuron dynamics, and in addition, these findings provide valuable insights for energy-efficient neuromorphic hardware design. Full article

Green buildings increasingly couple electrical, thermal, and hydrogen subsystems, yet these assets are typically monitored and controlled through separate standards and protocols. The resulting heterogeneous information models and communication stacks hinder millisecond-level coordination, plug-and-play integration, and resilient operation. To address this gap, we develop a unified information model and a cross-protocol real-time interaction mechanism based on extensions of IEC 61850. At the modeling level, we introduce new logical nodes and standardized data objects that describe electrical, thermal, and hydrogen devices in a single semantic space, supported by a global unit system and knowledge-graph-based semantic checking. At the communication level, we introduce a semantic gateway with adaptive mapping bridges IEC 61850 and legacy building protocols, while fast event messaging and 5G-enabled edge computing support deterministic low-latency control. The approach is validated on a digital-twin platform that couples an RTDS-based multi-energy system with a 5G test network. Experiments show device plug-and-play within 0.8 s, cross-protocol response-time differences below 50 ms, GOOSE latency under 5 ms, and critical-data success rates above 90% at a bit-error rate of 10⁻³. Under grid-fault scenarios, the proposed framework reduces voltage recovery time by about 60% and frequency deviation by about 70%, leading to more than 80% improvement in a composite resilience index compared with a conventional non-unified architecture. These results indicate that the framework provides a practical basis for interoperable, low-carbon, and resilient energy management in green buildings. Full article

Aiming at the problems of voltage distortion caused by voltage drop nonlinearity of voltage source inverter (VSI) tubes, poor recognition accuracy of traditional grey wolf optimization (GWO) in identifying parameters of permanent magnet synchronous motor (PMSM), and slow convergence speed at the later stage, a memory self-learning grey wolf optimization (MSLGWO) algorithm with voltage error compensation is proposed. First, the output voltage error caused by switching tube voltage drop in different conduction states of the inverter is compensated to mitigate its impact on parameter identification. Then, cat mapping is employed to generate the initial position of the grey wolves, combined with an inverse learning strategy to find and select the superior solution among them to secure the variety of the initial population. In addition, the rate of convergence is accelerated by using a cosine-varying convergence factor to maintain a balance between global and local search capabilities. Lastly, inspired by the particle swarm optimization algorithm, a memory-based self-learning mechanism is incorporated to leverage the past experiences of individual wolves. Compared with traditional GWO, the proposed MSLGWO with voltage compensation reduces the identification error by at least 50.0% and completes the process within 0.11 s. Full article

Rydberg atoms are neutral atoms excited to high principal quantum number states, which endows them with exaggerated properties such as large electric dipole moments, long lifetimes, and extreme sensitivity to external electromagnetic fields. These characteristics form the foundation of Rydberg atom-based sensors, an emerging class of quantum devices capable of optically detecting electric fields across frequencies from DC to the terahertz regime. Rydberg-based electrometry operates through both Autler–Townes (AT) splitting of resonant Rydberg transitions and Stark-shift measurements for high-frequency or far-detuned fields, enabling broadband field sensing from DC to the THz regime. Using ladder-type electromagnetically induced transparency (EIT) and AT splitting, these sensors enable non-invasive, SI-traceable measurements of field amplitude, frequency, phase, and polarization. Recent developments have demonstrated broadband electric field probes, voltage calibration standards, and compact RF receivers based on thermal vapor cells and integrated photonic architectures. Furthermore, innovations in multi-photon EIT, superheterodyne readout, and multi wave mixing have expanded the dynamic range and bandwidth of Rydberg-based electrometry. Despite challenges related to environmental perturbations, linewidth broadening, and laser stabilization, ongoing advances in atomic control, hybrid photonic integration, and EIT-based readout promise scalable, chip-compatible sensors. This review summarizes the physical principles, experimental progress, and emerging applications of Rydberg atom-based sensing, emphasizing their potential for next generation quantum metrology, wireless communication, and precision field mapping. Full article

As a critical energy component in electric vehicles, energy storage systems, and other applications, the accurate estimation of the State of Health (SOH) of lithium-ion batteries is crucial for performance optimization and safety assurance. To this end, this paper proposes a hybrid model named Transformer–KAN, which integrates Transformer architecture with Kolmogorov–Arnold Networks (KANs) for precise SOH estimation of lithium-ion batteries. Initially, five health features (HF1–HF5) strongly correlated with SOH degradation are extracted from the historical charge–discharge data, including constant-voltage charging duration, constant-voltage charging area, constant-current discharging area, temperature peak time, and incremental capacity curve peak. The effectiveness of these features is systematically validated through Pearson correlation analysis. The proposed Transformer–KAN model employs a Transformer encoder to capture long-term dependencies within temporal sequences, while the incorporated KAN enhances the model’s nonlinear mapping capability and intrinsic interpretability. Experimental validation conducted on the NASA lithium-ion battery dataset demonstrates that the proposed model outperforms comparative baseline models, including CNN–LSTM, Transformer, and KAN, in terms of both RMSE and MAE metrics. The results indicate that the Transformer–KAN model achieves superior estimation accuracy while exhibiting enhanced generalization capabilities across different battery instances, indicating its strong potential for practical battery management applications. Full article

This review investigates how cell form factors (cylindrical, prismatic, and pouch) and electrode architecture (jelly-roll, stacked, and blade) influence the performance, safety, and manufacturability of lithium-ion batteries (LIBs) across the main commercial chemistries LiFePO₄ (LFP), Li (NiMnCo)O₂ (NMC), LiNiCoAlO₂ (NCA), and LiCoO₂ (LCO). Literature, OEM datasheets, and teardown analyses published between 2015 and 2025 were examined to map the interdependence among geometry, electrode design, and electrochemical behavior. The comparison shows trade-offs among gravimetric and volumetric energy density, thermal runaway tolerance, cycle lifespan, and cell-to-pack integration efficiency. LFP, despite its lower nominal voltage, offers superior thermal stability and a longer cycle life, making it suitable for both prismatic and blade configurations in EVs and stationary storage applications. NMC and NCA chemistries achieve higher specific energy and power by using jelly-roll architectures that are best suited for tabless or multi-tab current collection, enhancing uniform current distribution and manufacturability. Pouch cells provide high energy-to-weight ratios and flexible packaging for compact modules, though they require precise mechanical compression. LCO remains confined to small electronics owing to safety and cost limitations. Although LFP’s safety and affordability make it dominant in cost-sensitive applications, its low voltage and energy density limit broader adoption. LiMnFePO₄ (LMFP) cathodes offer a pathway to enhance voltage and energy while retaining cycle life and cost efficiency; however, their optimization across various form factors and electrode architecture remains underexplored. This study establishes an application-driven framework linking form factors and electrode design to guide the design and optimization of next-generation lithium-ion battery systems. Full article

Data-driven fault location methods based on deep learning offer strong feature learning and nonlinear mapping capabilities; however, in low-voltage distribution grids (LVDG) the scarcity of high-rate sampling devices and the variability introduced by distributed renewable generation lead to data insufficiency and data imbalance, which reduce the accuracy of deep-learning-based fault location. To address this, this paper proposes an adaptive working condition-based fault location method that integrates S-transform-enhanced feature extraction with progressive transfer learning. The method clusters working conditions using k-means on a 21-dimensional indicator set covering load, photovoltaic, and voltage. For each condition, a CNN is trained on the corresponding data, and the S-transform extracts distinctive time-frequency signatures from limited measurements to separate fault points at similar distances from the feeder head. Then, progressive transfer learning with Euclidean distance-based domain adaptation migrates effective parameters from data-rich conditions to data-scarce ones through fine-tuning and medium-tuning, thereby addressing the degradation of fault-location accuracy in scenarios with limited data. Experimental validation on a 400 V LVDG demonstrates superior performance, achieving 99.80% fault location accuracy and 99.72% fault type classification. The S-transform enhancement improves fault location by 6.63%, while transfer learning maintains 96% accuracy in edge conditions using only 200 samples. Full article

The growing market penetration of Electric Vehicles (EVs) requires very efficient bidirectional on-board chargers. These converters must allow the power transfer from the grid to the battery of the vehicle and vice versa, since Vehicle to Grid (V2G) applications enable a mitigation of the peak demand and help regulate both the voltage and the frequency of the grid. In this paper, an innovative double asymmetric modulation was studied and applied to a resonant Dual Active Bridge (DAB), CLLC resonant filter configuration. The results of the study showed a significant efficiency boost and an easier controllability of the converter with respect to more traditional modulations or variable frequency techniques, maintaining Zero-Voltage Switching (ZVS) conditions for all the switches in a wide operating range, from 28 to 100% of the maximum power (4–14 kW). A map of optimum points, where converter losses are minimized, is calculated offline through an algorithm in MATLAB R2024a and a proper interpolation between these points allows any output power for each possible voltage level of the battery to be achieved: from 250 V up to 400 V. The modulations are compared and evaluated through simulations carried out in PLECS, both offline and using hardware-in-the-loop (HIL), as well as through experimental tests. Full article

Aiming at the dynamic characteristics and stability of smart distribution network stations under the combined effect of the uncertainty of new energy output and the control logic of power electronics, an adaptive dimensionally increasing linear dynamic modeling method based on Koopman theory is proposed. Firstly, a regional nonlinear model of an intelligent transformer integrating photovoltaic, wind power, battery, hydrogen fuel cell, and synchronous generator is constructed. The control logic of the virtual synchronous generator is then integrated to characterize the dynamic response of the power electronic interface. Secondly, by constructing a set of nonlinear observation functions, including high-order polynomials, exponents, and periodic functions, the dimensional upgrade mapping of the system state is carried out. The dynamic mode decomposition algorithm is adopted to adaptively extract the dominant dynamic modes in the dimensional upgrade space, achieving global linear approximation of complex nonlinear dynamical systems. Finally, the simulation example results show that the average RMAE error of the Koopman method proposed in this paper in voltage spatiotemporal reconstruction is 0.1419, and the maximum RMSE error is 0.1915, significantly improving the accuracy and stability of dynamic modeling. Full article

Reliable timing is a crucial issue in all synchronous systems. Delay locked loops are capable of dynamically synchronizing clock signals; the increasing speed of deeply scaled technologies however leads to long and complex delay lines. In this paper, a sawtooth-based delay locked loop is proposed to address the increasing difficulty of delay generation in high speed systems. The proposed architecture replaces a conventional delay line with a sawtooth waveform-based mechanism for delay generation, reducing the need for numerous delay elements. The timing offset is mapped to a voltage level on the sawtooth waveform, where the required delay is the time to cross this voltage level. The architecture, evaluated using a 7 nm device model, achieves a locking speed as low as four cycles for a 1 GHz clock signal. The DLL achieves a full period locking range, lowers the clock skew to 16 ps at room temperature, and exhibits 65 ps clock skew variations over extreme temperature corners. Full article

Maximum power point tracking (MPPT) control is a key technology for increasing the power generation of photovoltaic arrays under varying light and temperature conditions. Traditional perturb and observe methods and incremental conductance methods can achieve good tracking performance for single-peak characteristics. However, under complex conditions such as partial shading or dust accumulation, the power-voltage curve of a photovoltaic array exhibits multi-peak characteristics. In such cases, traditional methods may get trapped in local optima, preventing the photovoltaic array from operating at the maximum power point. Swarm intelligence algorithms perform well when solving multi-extremum functions and can be used for MPPT control of photovoltaic arrays in complex environments. Therefore, this paper focuses on the fireworks algorithm (FWA). To improve the computational speed and global optimization capability of the FWA, the characteristics of each stage of the algorithm are analyzed, a comprehensive improved fireworks algorithm (CIFWA) is proposed, and it is applied to the MPPT control of photovoltaic systems. The improved algorithm introduces an adaptive resource allocation and selection strategy with community inheritance features and applies tent chaos mapping to the algorithm’s explosion behavior. Multiple sets of test functions are used to compare the performance metrics of the optimization algorithm, demonstrating improvements in computational speed and global search capability of CIFWA. Finally, a control strategy for the MPPT of photovoltaic arrays based on CIFWA is presented, and a simulation experimental platform is built to analyze and verify the control performance. Full article

With the increasing share of renewable energy, green power parks face challenges such as high electricity purchasing costs and fluctuations in power quality. To address these issues, this paper proposes an integrated optimization method based on power quality responsibility modeling and a differentiated reward–penalty pricing mechanism (DRPPM). First, an integrated operation model of “source–grid–load–storage” is established. Within the pressure–state–response (PSR) framework, power quality deviations are quantified and mapped into economic costs. Then, a differentiated reward–penalty pricing mechanism is designed to dynamically adjust power quality deviations through a continuous function, guiding users toward adaptive energy consumption behavior. Finally, a green power park in Gansu Province dominated by wind and photovoltaic generation is used as a case study with four typical simulation scenarios. The results show that the proposed mechanism reduces the park’s electricity purchasing cost and increases the green power consumption ratio by up to 74.9%. Meanwhile, it effectively improves power quality indicators such as frequency, voltage, and harmonics. The study verifies the comprehensive advantages of the proposed framework in terms of economy, energy efficiency, and stability, providing a reference for low-carbon and efficient operation of high-energy-consumption green power parks. Full article

Gallium nitride (GaN) devices are gaining rapid adoption in electric vehicle (EV) power electronics because of their high switching speed, efficiency, and passive size reduction. The remaining gaps concern reliability across real drive cycles, integration with vehicle-level thermal subsystems, and scalability to high-voltage platforms. This review addresses these gaps by synthesizing experimental reports and automotive case studies from 2019 to 2025. We examine reliability through junction stress and derating maps derived from urban/highway duty profiles and temperature extremes, and we link device hot-spots to thermal pathways (TIMs, spreaders, liquid/air cooling) within the EV thermal budget. We then compare GaN-based onboard chargers (OBCs), DC–DC stages (LLC/CLLC/DAB), traction inverters, and EMI strategies against Si/SiC baselines. Results indicate OBC efficiencies of 96–98% at 100–500 kHz, with 30–60% passive reduction; inverter efficiencies > 98% on 400 V platforms; and strong potential for GaN paired with Vienna or T-type rectifiers in 800 V charging, while >900 V traction remains largely SiC-led. We conclude with a topology-selection framework that balances switching and conduction losses, gate-driver complexity, and EMI, plus a roadmap toward EMI-compliant MHz operation and data-driven reliability evaluation. Full article

SiC MOSFETs face prominent reliability issues due to higher voltage resistance requirements and continued device miniaturization. The lifetime prediction of SiC MOSFET plays a crucial role in improving the reliability of devices and systems. However, existing methods still face challenges in terms of adaptability, stability, and accuracy due to the complexity of the failure process in SiC MOSFET. This article proposes an improved grey wolf optimizer-based long short-term memory (IGWO-LSTM) model for SiC MOSFET lifetime prediction. The model introduces a Tent chaotic mapping to generate an initial population with optimal distribution, ensuring comprehensive search space coverage and enhancing dynamic search adaptability. Then, a nonlinear control parameter strategy and the principle of particle swarm optimization (PSO) are added. The feature extraction capability of the model is strengthened, and the exploration and exploitation phases are dynamically balanced. The optimizations enable faster discovery of the global optimum while maintaining solution quality, thereby improving prediction accuracy and stability. Finally, power cycling experiments were conducted on two types of SiC MOSFETs with different internal resistances to validate the effectiveness of the proposed model. The proposed IGWO-LSTM model achieves high prediction accuracy, with R² values of 96.2%, 94.8%, 94.1%, and 93.9% for four SiC MOSFETs, and RMSE values as low as 0.0117, 0.0143, 0.0152, and 0.0158, respectively. This represents an average improvement in R² by 16%, 8%, and 4%, and a reduction in RMSE by up to 67.03%, 50.39%, and 31.57% compared with other intelligent models. Similarly, IGWO-LSTM achieves reductions in MAE of approximately 68%, 50%, and 30%, with corresponding reductions in MAPE of about 70%, 48%, and 26%, respectively. The results demonstrate superior performance in prediction accuracy, stability, and adaptability of the proposed model. Full article

This paper proposes a novel AC side series arc fault (SAF) identification method based on Generalized Lissajous Trajectory (GLT) learning for low-voltage residential circuits. The method addresses challenges in detecting SAFs—characterized by high concealment, random occurrence, and limitations in existing protection devices—by leveraging the Hilbert transform to map current signals into 2D Generalized Lissajous Trajectories. These trajectories amplify key SAF features (e.g., zero-break distortion and random pulses). A ResNet50-based image recognition model achieves high-precision fault detection under specific load types, with a validation accuracy of up to 99.91% for linear loads and 98.93% for nonlinear loads. The algorithm operates within 1.6 ms, enabling real-time circuit breaker tripping. The proposed method achieves higher recognition accuracy with lower computational cost compared to other image-based methods. In this paper, an adjustable load signal modeling approach is proposed to visualize the current signal using GLT and complete the lightweight identification based on ResNet network, which provides new ideas and methods for series arc fault detection. Full article