Search Results (86)

Compared to the traditional unidirectional power supply system, the bidirectional traction power supply system in an electrified railway offers advantages like improved traction voltage and reduced energy losses, making it more suitable for steep gradient routes. However, its increased electrical complexity necessitates advanced catenary-rail short-circuit fault calculations and relay protection calibration. This paper proposes a fault calibration approach based on deriving electrical quantities with fault distance in the railway bidirectional traction grid system. A multi-loop circuit modeling method is used to accurately model the traction grid system and impedance parameters, incorporating real loop circuits formed by the grid transmission and return conductors for the first time. The approach is validated through real-life experiments on a Chinese railway line. A case study of a direct power supply system with a return cable is used to derive electrical quantities. Faults are categorized into two sections: between the substation and the parallel station (PS), and between the PS and the section post (SP). For each section, electrical quantities are derived under unidirectional substation excitation, and the results are superimposed to obtain fault distance variation curves for currents and voltages of substation, PS, SP, and Thévenin impedance. Finally, a calibration strategy for relay protection is presented. Full article

With the large-scale integration of renewable energy devices into the power grid, the voltage stability of the renewable energy base is becoming increasingly weak, and the problem of transient overvoltage is becoming increasingly severe. Grid-forming (GFM) converters can provide strong voltage support. When GFM converters are paralleled with grid-following (GFL) converters, they can effectively reduce transient overvoltage. However, hybrid systems involve many parameters and exhibit complex dynamics, making assessment of transient overvoltage difficult. To address this, this paper first uses Thevenin’s theorem to reduce the renewable transmission system to an equivalent model. Next, the voltage assessment of the hybrid system is analyzed across the pre-fault, mid-fault, and post-fault stages of a short-circuit fault. Then, based on the characteristics of a phase-locked loop (PLL), this paper innovatively derives an assessment method for transient overvoltage at the common coupling point (PCC) under different PLL stability conditions. Additionally, the influence of GFL converter parameters, GFM converter parameters, the GFM capacity ratio on transient overvoltage, and the external system reactance are analyzed. Finally, the proposed evaluation method and factor analysis are validated through electromechanical transient simulation using the simulation software STEPS v2.2.0. Full article

Supercapacitors (SCs) are emerging as a dependable energy storage technology in industrial applications, valued for their high power output and exceptional longevity. In high-power applications, SCs are not used as single cells but are configured in a series–parallel combination to form a bank. Accurate state-of-charge estimation is essential for effective energy management in power systems employing SC banks. This work presents a novel state estimation approach for SC banks. First, a dynamic model of an SC bank is derived by applying a fractional-order Thévenin equivalent circuit to a single-cell SC. Then, an observability analysis is conducted, which reveals that the system is empirically weakly observable. This is the fundamental challenge for state-of-the-art observers to robustly perform state estimation. To address this challenge, an implicitly regularized observer is developed based on generalized parameter estimation techniques. The performance of the proposed observer is benchmarked against a fractional-order extended Kalman filter using experimental data. The results demonstrate that incorporating a regularization law into the observer dynamics effectively mitigates observability limitations, offering a robust solution for the SC bank state estimation. Full article

The rapid advancement of electronic technology demands circuit designs that minimize power consumption while maximizing performance. The power supply rejection ratio (PSRR) is a critical metric for quantifying an amplifier’s ability to suppress supply noise, yet accurately predicting PSRR in high-frequency domains and complex multi-stage architectures is increasingly challenging. In this work, we introduce a new framework for PSRR prediction that overcomes these limitations. Leveraging a simplified circuit abstraction based on Thevenin’s theorem, we reduced multi-stage operational amplifiers to “black-box” models—collapsing intricate small-signal networks into a tractable form without sacrificing accuracy. Building on this foundation, we proposed the Power-Supply Rejection Gain-Bandwidth (PGB) metric, which concisely captures the trade-off between an amplifier’s DC PSRR and the frequency range over which that rejection is effective. Using PGB, designers gain an intuitive figure-of-merit for early-stage optimization of PSRR. We validated the efficacy of the combined black-box modeling and PGB approach through detailed case studies, including a 180 nm CMOS two-stage op-amp design. These findings confirmed that the proposed black box plus PGB framework can reliably guide the design of analog circuits with stringent PSRR requirements. Full article

The virtual synchronous generator (VSG) scheme has proven to be an attractive solution in grid-forming converter applications integrated into distributed generation (DG) systems. Thus, this paper presents the dynamic performance of power flow control using the VSG approach under Thevenin impedance variations seen by the grid-forming converter. The dynamic analysis is based on a discrete-time model that describes the power flow transient characteristics of the system operating in medium- and high-voltage networks. Based on the proposed model, a controller design procedure for the discrete-time VSG scheme is presented. This methodology aims to assist researchers in implementing VSG control in digital environments. Then, the Thevenin impedance parameters’ influence on the discrete-time VSG strategy dynamic performance is discussed. The VSG technique’s performance in different operating scenarios is assessed by means of simulation results. A case study is provided to validate the effectiveness of the theoretical analysis and the discrete-time VSG control scheme. The results assess the effectiveness of the theoretical analysis performed. Full article

This study focuses on the critical problem of precise state of charge (SOC) estimation for electric unmanned aerial vehicle (UAV) battery systems, addressing a fundamental challenge in enhancing energy management reliability and flight safety. The current data-driven methods require big data and high computational complexity, and model-based methods need high-quality model parameters. To address these challenges, a multi-timescale fusion method that integrates battery model and data-driven technologies for SOC estimation in lithium-ion batteries has been developed. Firstly, under the condition of no data or insufficient data, an adaptive extended Kalman filtering with multi-innovation algorithm (MI-AEKF) is introduced to estimate SOC based on the Thévenin model in a fast timescale. Then, a hybrid bidirectional time convolutional network (BiTCN), bidirectional gated recurrent unit (BiGRU), and attention mechanism (BiTCN-BiGRU-Attention) deep learning model using battery model parameters is used to correct SOC error in a relatively slow timescale. The performance of the proposed model is validated under various dynamic profiles of battery. The results show that the the maximum error (ME), mean absolute error (MAE) and the root mean square error (RMSE) for zero data-driving, insufficient data-driving, and sufficient data-driving under various dynamic conditions are below 2.3%, 1.3% and 1.5%, 0.9%, 0.4% and 0.4%, and 0.6%, 0.3% and 0.3%, respectively, which showcases the robustness and remarkable generalization performance of the proposed method. These findings significantly advance energy management strategies for Li-ion battery systems in UAVs, thereby improving operational efficiency and extending flight endurance. Full article

It is necessary to maintain safe, efficient, and compatible energy storage systems to meet the high demand for electric vehicles (EVs). Lithium manganese nickel cobalt (NMC) and lithium ferro phosphate (LFP) batteries are the most commonly used lithium batteries in EVs. It is imperative to note that batteries are classified according to their electrochemical performance. A number of factors play a crucial role in determining how efficiently batteries can be used. These factors include the cell temperature, energy density, self-discharge, current limits, aging, and performance measurements. This paper offers a proposed electrothermal model for comparison between LFP and NMC batteries. This model demonstrates the different behaviors according to their application in EVs. This is carried out through studies of state of charge (SoC), state of health (SoH), thermal runaway, self-discharge, and remaining useful life (RUL) in EVs. According to numerical analysis, this paper examines how these different types of batteries behave in EVs to assist in the selection of the most suitable battery taking into account the operating temperature and discharge current using a helpful thermoelectric model reflecting battery safety and life span effectively. Using MATLAB Simulink, the data selected in the electrothermal model are combined from a number of references that are incorporated into lookup tables that affect the change in values in the electrothermal model. The cells are implemented in an EV system using a current test to examine the measured current that goes in and comes out of the battery cells during charging and discharging processes taking into account motoring and regenerative braking for a specified drive cycle time and a number of discharging cycles. It was found that LFP batteries have better stability for open circuit voltages of 3.34 volts over a wide range of conducted temperatures. NMC batteries, on the other hand, exhibit some open circuit voltage variation of 0.053 volts over the temperature range used. Furthermore, the self-discharging current of LFP batteries was about 12 times lower than that of NMC batteries. Compared to LFP batteries, NMC batteries have a higher energy density per unit of mass of 150%, which reflects their greater discharge range. As a result of temperature effects, it has been revealed that LFP batteries are about two times more stable during discharging than NMC batteries, particularly at higher temperatures, such as 45 degrees. Full article

Battery modelling is essential for optimizing the performance and reliability of Unmanned Aerial Vehicles (UAVs), particularly given the challenges posed by their dynamic power demands and limited onboard computational resources. This study evaluates two widely adopted Equivalent Circuit Models (ECMs), the fixed resistance model and the Thevenin model to determine their suitability for UAV applications. Using the Specific Hybrid Pulse Power Characterization (SHPPC) method, key parameters, including Open Circuit Voltage (OCV), internal resistance (Ri), polarization resistance (R1), and polarization capacitance (C1), were estimated across multiple states of charge (SOC). The models were analyzed under nine parameterization scenarios, ranging from fully average parameters to configurations where selected parameters were tied to SOC. Results indicate that the Thevenin model, with selective SOC-dependent parameters, demonstrated superior predictive accuracy, achieving error reductions of up to 4.26 times compared to the fixed resistance model. Additionally, findings reveal that modelling all parameters as SOC-dependent is unnecessary, as simpler configurations can balance accuracy and computational efficiency, particularly for UAVs with constrained BMS capabilities. Full article

This study utilized a multi-stage constant current (MSCC) charge protocol to identify the optimal current pattern (OCP) for effectively charging lithium-ion batteries (LiBs) using a Dandelion optimizer (DO). A Thevenin equivalent circuit model (ECM) was implemented to simulate an actual LiB with the ECM parameters estimated from the offline time response data obtained through a hybrid pulse power characterization (HPPC) test. For the first time, DO was applied to metaheuristic optimization algorithms (MOAs) to determine the OCP within the MSCC protocol. A composite objective function that incorporates both charging time and charging temperature was constructed to facilitate the use of DO in obtaining the OCP. To verify the performance of the proposed method, various algorithms, including the constant current-constant voltage (CC-CV) technique, formula method (FM), particle swarm optimization (PSO), war strategy optimization (WSO), jellyfish search algorithm (JSA), grey wolf optimization (GWO), beluga whale optimization (BWO), levy flight distribution algorithm (LFDA), and African gorilla troops optimizer (AGTO), were introduced. Based on the OCP extracted from the simulations using these MOAs for the specified ECM model, a charging experiment was conducted on the Panasonic NCR18650PF LiB to evaluate the charging performance in terms of charging time, temperature, and efficiency. The results demonstrate that the proposed DO technique offers superior charging performance compared to other charging methods. Full article

With the integration of a high proportion of distributed generators (DGs), the imbalance between source and load power intensifies, causing the distribution grid to become ‘weak’ during certain periods, which can easily lead to voltage over-limit for the distribution grid. Due to the coupling of active and reactive power in weak power grids for voltage regulation, the effectiveness of single reactive voltage regulation for voltage over-limit is not satisfactory. This paper uses an Energy Storage type Intelligent Soft Open Point (E-SOP) with grid-forming controlled energy storage to simultaneously adjust the active and reactive power between different grid clusters to suppress voltage over-limit for a weak grid. Firstly, based on the system architecture and bus load characteristics, the distribution grid is divided into clusters, and the cluster Thevenin equivalent model is established. Then, different voltage support schemes are compared and analyzed through the cluster active power-voltage (P-V) curve. Through simulation verification of the IEEE 33-bus system containing DGs, it is concluded that the scheme of installing E-SOP in the distribution grid containing DGs enhances the active power transmission capability of the grid and provides better voltage support compared to traditional reactive power compensation and system capacity expansion schemes. Full article

This paper presents a modeling approach to capture the coupled effects of electrical–thermal aging in Li-ion batteries at the cell level. The proposed semi-empirical method allows for a relatively high accuracy and low computational cost compared to expensive computer simulations. This is something current models often lack but is essential for system level simulations, relevant for electric vehicle manufacturers. The aging analysis includes both cycling and calendar effects across the lifetime of the cell and reversible and irreversible heat in a lumped-mass model to capture the temperature evolution of the cell in operation. The Thévenin equivalent circuit model with capacitance used to simulate the electrical behavior of the cell was experimentally validated, showing a high correlation with the proposed model during the charging and discharging phases. A maximum error of 3% on the voltage reading was identified during discharge with the complete model. This model was also designed to be used as a stepping stone for a comprehensive model at the module and vehicle levels that can later be used by designers. Full article

The precise estimation of the state of charge (SOC) in lithium batteries is crucial for enhancing their operational lifespan. To address the issue of reduced accuracy in SOC estimation caused by the random missing values of lithium battery current measurements, a joint estimation method which combines recursive least squares with missing input data (MIDRLS) and the unscented Kalman filter (UKF) algorithm is proposed, called the MIDRLS-UKF algorithm. Firstly, the equivalent circuit model of a Thevenin battery is formulated. Then, the current imputation model is designed to interpolate the missing data, based on which the MIDRLS algorithm is derived by solving the unbiased estimation of the gradient of the objective function, thus realizing the online high-precision identification of the circuit model parameters. Furthermore, the proposed algorithm is combined with the UKF algorithm to facilitate the online precise estimation of SOC. The simulation results indicate a marked decrease in the SOC estimation error when employing the proposed joint algorithm, as opposed to the conventional forgetting factor recursive least squares (FFRLS) algorithm combined with the UKF joint estimation algorithm, which verifies the precision and effectiveness of the proposed joint algorithm. Full article

An accurate assessment of the state of charge (SOC) of electric vehicle batteries is critical for implementing frequency regulation and peak shaving. This study proposes mechanism- and data-driven SOC fusion calculation methods. First, a second-order Thevenin battery model is developed to obtain the physical parameters of the battery. Second, data from the Thevenin battery model and data from four standard cycling conditions in the electric vehicle industry are added to the dataset of the feed-forward neural network data-driven model to construct the test and training sets of the data-driven model. Finally, the error of the mechanism and data-driven fusion modeling method is quantitatively analyzed by comparing the estimation error of the method for the battery SOC at different temperatures with the accuracy of the data-driven SOC estimation method. The simulation results show that the root mean square error, the mean age absolute error, and the maximum error of mechanism and data-driven method for the estimation error of battery SOC are lower than those of the data-driven method by 0.9%, 0.65%, and 1.3%, respectively. The results show that the mechanism and data-driven fusion SOC estimation method has better generalization performance and higher SOC estimation accuracy. Full article

The increasing adoption of batteries in a variety of applications has highlighted the necessity of accurate parameter identification and effective modeling, especially for lithium-ion batteries, which are preferred due to their high power and energy densities. This paper proposes a comprehensive framework using the Levenberg–Marquardt algorithm (LMA) for validating and identifying lithium-ion battery model parameters to improve the accuracy of state of charge (SOC) estimations, using only discharging measurements in the N-order Thevenin equivalent circuit model, thereby increasing computational efficiency. The framework encompasses two key stages: model parameter identification and model verification. This framework is validated using experimental measurements on the INR 18650-20R battery, produced by Samsung SDI Co., Ltd. (Suwon, Republic of Korea), conducted by the Center for Advanced Life Cycle Engineering (CALCE) battery group at the University of Maryland. The proposed framework demonstrates robustness and accuracy. The results indicate that optimization using only the discharging data suffices for accurate parameter estimation. In addition, it demonstrates excellent agreement with the experimental measurements. The research underscores the effectiveness of the proposed framework in enhancing SOC estimation accuracy, thus contributing significantly to the reliable performance and longevity of lithium-ion batteries in practical applications. Full article

This paper presents a complete electromechanical (EM) model of piezoelectric transducers (PTs) independent of high or low coupling assumptions, vibration conditions, and geometry. The PT’s spring stiffness is modeled as part of the domain coupling transformer, and the piezoelectric EM coupling coefficient is modeled explicitly as a split inductor transformer. This separates the coupling coefficient from the coefficient used for conversion between mechanical and electrical domains, providing a more insightful understanding of the energy transfers occurring within a PT and allowing for analysis not previously possible. This also illustrates the role the PT’s spring plays in EM energy conversion. The model is analyzed and discussed from a circuits and energy harvesting perspective. Coupling between domains and how loading affects coupled energy are examined. Moreover, simple methods for experimentally extracting model parameters, including the coupling coefficient, are provided to empower engineers to quickly and easily integrate PTs in SPICE simulations for the rapid and improved development of PT interface circuits. The model and parameter extractions are validated by comparing them to the measured response of a physical cantilever-style PT excited by regular and irregular vibrations. In most cases, less than a 5–10% error between measured and simulated responses is observed. Full article