Characteristics Of Nonlinear Components Research Articles

Real-time energy management strategy with dynamically updating equivalence factor for through-the-road (TTR) hybrid vehicles

Through-the-road–coupled hybrid electric vehicles, comprising a forward-driving internal combustion engine (ICE) and two rear-mounted hub motors, have been attracting increasing attention for their energy efficiency and excellent road passability. However, real-time energy allocation in the ICE and motors is challenging because of complex road scenarios, nonlinear characteristics of components, and the requirement of real-time executability of energy management strategies (EMSs). This study developed an online EMS with an equivalent factor (EF) which is timely updated through the offline rule extraction and online parameter feedback based on real-time driving scenarios. Initially, the utilization of a global dynamic programming approach aims at delineating the potential switching boundary, thus substantially mitigating the computational burden on the controller. Subsequently, an innovative Model Predictive Control-based Equivalent Fuel Consumption Strategy (MPC-ECMS) is introduced. This method integrates real-time velocity prediction with State of Charge (SOC) feedback to enhance fuel efficiency. Notably, the efficiency factor (EF) within ECMS undergoes optimization through a genetic algorithm, with adaptive corrections based on driving condition recognition outcomes. Consequently, the EF is continuously adjusted to ensure convergence of actual SOC towards the reference SOC within the MPC time frame. Through rigorous Hardware in Loop experimentation, the efficacy of the proposed EMS is validated. The outcomes demonstrate its superiority over traditional charge-depleting mode–charge sustaining mode strategies, yielding a notable 13.3 % reduction in total fuel consumption during high power demand scenarios. Furthermore, the method's feasibility within embedded systems is convincingly affirmed.

An Exploration into the Fault Diagnosis of Analog Circuits Using Enhanced Golden Eagle Optimized 1D-Convolutional Neural Network (CNN) with a Time-Frequency Domain Input and Attention Mechanism.

With the continuous operation of analog circuits, the component degradation problem gradually comes to the forefront, which may lead to problems, such as circuit performance degradation, system stability reductions, and signal quality degradation, which could be particularly evident in increasingly complex electronic systems. At the same time, due to factors, such as continuous signal transformation, the fluctuation of component parameters, and the nonlinear characteristics of components, traditional fault localization methods are still facing significant challenges when dealing with large-scale complex circuit faults. Based on this, this paper proposes a fault-diagnosis method for analog circuits using the ECWGEO algorithm, an enhanced version of the GEO algorithm, to de-optimize the 1D-CNN with an attention mechanism to handle time-frequency fusion inputs. Firstly, a typical circuit-quad op-amp dual second-order filter circuit is selected to construct a fault-simulation model, and Monte Carlo analysis is used to obtain a large number of samples as the dataset of this study. Secondly, the 1D-CNN network structure is improved for the characteristics of the analog circuits themselves, and the time-frequency domain fusion input is implemented before inputting it into the network, while the attention mechanism is introduced into the network. Thirdly, instead of relying on traditional experience for network structure determination, this paper adopts a parameter-optimization algorithm for network structure optimization and improves the GEO algorithm according to the problem characteristics, which enhances the diversity of populations in the late stage of its search and accelerates the convergence speed. Finally, experiments are designed to compare the results in different dimensions, and the final proposed structure achieved a 98.93% classification accuracy, which is better than other methods.

An Eigenvalues Method for Stability Analysis of Power Grid with Energy Storage Devices Based on Discrete Time Domain Model

The eigenvalue analysis based on state space model is an important method to study stability of power grid. With the large-scale access of energy storage devices and power electronics converters in power grid, the conventional state space modelling method suffers from complicated process of determining state variables and large calculation amount of eigenvalues, and nonlinear stability characteristics of power grid cannot be accurately reflected. To obtain the eigenvalues of power grid with energy storage devices more efficiently, this paper proposes a method for constructing state space model of power grid based on discrete time domain model (DTDM) and presents the corresponding stability criterion. Compared with the conventional state space modelling method, the nonlinear characteristics of components under proposed method can be linearized, and the constructed state space model can capture a wider range of system information including energy storage devices. Besides, the complete state space model of power grid can be obtained only according to the connection between component's DTDM and grid. Moreover, because eigenvalue solution under proposed method relies on the sparse grid node voltage matrix, the calculation of eigenvalues is simple and efficient, and it visually presents variation trend of eigenvalue and investigates the stability of system. Simulation results in Simulink/Matlab verify the correctness and effectiveness of proposed method.

Evolution of rough-surface geometry and crystalline structures of aligned TiO2 nanotubes for photoelectrochemical water splitting

Nowadays, increasing awareness of environment and fossil fuels protection stimulates intensive research on clean and renewable sources of energy. Production of hydrogen from water through solar-driven splitting reactions is one of the most promising approaches in the field of photoelectrochemistry (PEC). In this work we have fabricated well-aligned, highly-ordered, smooth-mouth TiO2 nanotube arrays (TNAs) in a two-step anodization process of titanium foil, which were then used as photoelectrodes for PEC water splitting. It demonstrates for the first time correspondence between non-linear component characteristics of multiscale rough surface and crystalline structure of annealed TNAs measured at various fabrication stages and their photoelectrochemical response. The as-anodized TNAs with isotropic surface (deduced from AFM and SEM images) and largest figure of merit (according to their PEC performance) were annealed at 450 °C in air. Scale-invariant descriptors of the surface structure of the deposits involved: fractal dimension, corner frequency, roughness, size of nanostructures and their dominant habits. Moreover, X-ray diffraction data processed using the Rietveld method confirmed co-existence of various oxides, for example: TiO2 in the form of anatase, TiO and Ti3O5 phases in the TNAs under study pointing that previous well-established mechanisms of the TNA growth were to certain degree incomplete.

Application of LQ-based semi-active suspension control in a vehicle

A linear quadratic (LQ) based controller and observer concept for a semi-active full-car model is implemented and compared to a skyhook controller using a real vehicle in this paper. Especially an observer including a new modified road model is presented, which reliably filters low frequency disturbances induced by ascending or descending steep hills. Furthermore, methods for the parametrization of the quadratic cost function are presented and it is shown in experiments for different vehicle masses that ride comfort and road holding can be significantly improved by using semi-active suspension control. In order to take nonlinear component characteristics or suspension friction into account a parametrized nonlinear full-car model is presented, which is used for the determination of the controller cost function weights. The performance evaluation is done using a 4-poster test rig as well as measurements conducted on a real road.

A Robust and Efficient Iterative Scheme for the EMT Simulations of Nonlinear Circuits

This paper presents a robust and efficient iterative scheme for solving nonlinear circuits as a solution method for electromagnetic transient (EMT) simulations. In most EMT simulations, the characteristics of nonlinear components can be represented by piece-wise linear curves. With this assumption, the Newton-Raphson (NR) method shows high efficiency, but it is prone to get into an infinite loop, resulting in nonconvergence. First, the NR method is extended to utilize the information of both axes of the nonlinear characteristics, and this modified method is called a biaxial NR method. The biaxial NR method shows a significantly improved convergence performance with additional computation. Next, an iterative scheme, which combines the standard NR, the biaxial NR, and the Katzenelson method is proposed. It first tries the standard NR that is the most efficient but the least convergent. If it fails to converge, the biaxial NR, which is more convergent but less efficient, is employed. Just in case when the biaxial NR does not converge, finally, the Katzenelson method, whose convergence is mathematically guaranteed but least efficient, is used. In this way, the proposed scheme always converges with a relatively small number of iterations. Illustrative and practical examples are shown to validate the proposed scheme.

Application of LQ based semi-active suspension control in a vehicle

AbstractA linear quadratic (LQ) based controller and observer concept for a semi-active full-car model is implemented and validated using a real vehicle in this paper. Especially methods for the parametrization of the quadratic cost function are presented and it is shown in experiments, that ride comfort and road holding can be significantly improved by using semi-active suspension control. In order to take the nonlinearities like nonlinear component characteristics or suspension friction into account a parametrized nonlinear full-car model is presented, which is used for the determination of the controller cost function weights. The performance evaluation is done using a 4-poster test rig as well as measurements on a real road.

Development of an electrical model for a PV/battery system for performance prediction

This paper presents electrical model of a photovoltaic-battery system. This model helps to understand the behaviour of a solar-battery system under various load and irradiance conditions. And also it assists with investigation of the performance of the system. Hybrid energy systems use different energy sources such as solar and wind with a backup unit such as a battery or a diesel generator. They are an economical option in areas remote from national grid. In this context the performance of the system to supply electric power in an efficient way of operation is important. The problem comes from uncertain renewable energy supply and load and also non-linear characteristics of components in the system. The purpose of study we are involved in this relation is to see the behaviour of a solar-battery system under various load and irradiance conditions and to investigate the performance of the system. As a result an optimum system configuration and a correct and cost effective size of Balance of System (BOS) can be achieved. In this paper the complete electrical circuit of the entire hybrid system, the mathematical model and computational technique are presented.

A two‐phase testing technique for analog fault diagnosis systems

Abstract A two‐phase testing technique is presented for analog circuit fault diagnosis systems. The proposed technique isolates the faulty linear components in the first phase and then using the computed linear component values obtained by phase 1 to determine the input‐output characteristics of nonlinear components during phase 2. The faulty nonlinear components can thus be located. This two‐phase technique takes great advantage of low computational cost since the complex characteristics of the nonlinear components are not directly involved in the calculation.