Proposed Parameter Optimization Research Articles

A Parameter Optimization Design Method for Single-Phase Dual Active Bridge AC-DC Converter

The single-stage dual active bridge (DAB) AC-DC converter has the advantages of high power density, low cost, and simple control; it has a broad potential for application in the field of onboard chargers (OBC). However, the lack of fast and accurate quantitative parameter optimization design methods in single-stage DAB AC-DC converters limits the overall efficiency of the converter. Based on the above problem, in order to improve the overall operating efficiency of the converter by optimizing the parameter transformer ratio and power inductance, this paper proposes a parameter design method considering a multi-timescale strategy by combining the steady-state analysis model of the converter in the line cycle and switching cycle and step-by-step reducing its design space through the constraints on the parameters. The first step is to obtain a safe design space for the parameters under the converter’s transmitted power and current stress constraints. The second step obtains the optimization design space of the parameters under the optimization of conduction loss and switching loss of the converter. Finally, the optimal parameters are determined by the loss analysis model. The proposed parameter optimization method entirely takes into account the steady-state characteristics of the DAB AC-DC converter during the line cycle, and the step-by-step constraints greatly accelerate the parameter design process. In addition, the proposed parameter optimization design method applies to all types of single-stage DAB AC-DC converters, which can be well applied to engineering practice.

Parameter Optimization of Magnetic Components for Phase-Shifted Full-Bridge Converters Using a Digital Twin

Due to their significant performance, Phase-Shifted Full-Bridge Converters (PSFBCs) have gained widespread adoption in medium- and high-power applications. The performance of a PSFBC is greatly influenced by its magnetic components, namely the transformer and resonance inductor. To address these challenges, this paper proposes a parameter optimization of magnetic components for PSFBCs, specifically the transformer turns ratio and resonance inductor value, to enhance conversion efficiency and minimize operational loss. A digital twin of PSFBCs enabling a more accurate loss estimation is proposed to achieve this objective. The proposed loss estimation method precisely calculates the effective and circulation intervals and the corresponding current points of the primary-side transformer current, resulting in improved accuracy. By leveraging the digital twin, the effects of transformer turns ratio and resonant inductor value on the conversion efficiency of a PSFBC can be efficiently simulated. This facilitates the parameter optimization of magnetic components, thereby minimizing operational losses across different application scenarios. This paper also designs and implements a PSFBC prototype with a rated input voltage of 380 V, output voltage of 24 V, and output current of 20 A. The experimental results show the influences of transformer turns ratio and resonant inductor value on the PSFBC and validate the proposed digital twin. The proposed parameter optimization of magnetic components is further evaluated across two application scenarios with varying utilization rates. The simulation results indicate a reduction of approximately 14% in operational loss per hour after applying the parameter optimization of magnetic components for the PSFBC used as a battery charger. The results demonstrate the effectiveness and practicality of the proposed digital twin in designing PSFBCs.

Stability analysis and control parameter optimization of multi-VSG parallel grid-connected system

The virtual synchronous generator (VSG) is emerging as an attractive solution for controlling the grid-connected inverter when the renewable energy has a high penetration level into the grid. This study delves into the stability analysis and parameter optimization of multi-VSG parallel grid-connected systems. Consequently, a small-signal model (SSM) for the multi-VSG parallel grid-connected system is developed to facilitate system analysis. Utilizing this model, the feasible parameter regions are determined by applying the Routh-Hurwitz criterion, enabling the identification of key factors contributing to the system's oscillation mode. Subsequently, a time-domain model is employed to ascertain the dynamic feasible region index, which aids in delineating the scope of parameter optimization. Furthermore, the impact of deviation angle, damping, and eigenvalues on system stability is incorporated into the construction of the objective function. To optimize system parameters within the feasible region, the simulated annealing-particle swarm optimization algorithm (SA-PSO) is utilized. The effectiveness of the theoretical analysis and the proposed parameter optimization method is demonstrated through simulations.

Defects Detection of Onboard Cable Termination in EMUs Using Partial Discharge Measurement and SDP-DL Framework

Termination is the most important part of on-board cable, which plays a vital role in ensuring the continuous and reliable power supply to EMUs (electrical multiple units). The maintenance time of EMUs is limited, so the time left for PD (partial discharge) measurement of on-board cable termination is very short, leading to the obvious decreasing of detection accuracy. For addressing this issue, this paper proposed an SDP (symmetrized dot pattern)–DL (deep learning) framework to detect the insulation defects using time series PD pulse signal. First, a PD measurement platform and experimental samples were prepared in laboratory to obtain the time series PD pulse signals of four typical insulation defects. Then, the time series PD signals were converted into SDP images using the proposed parameter optimization method. Finally, the SDP-DL framework was proposed, and three specific and typical methods were utilized, i.e., CNN (convolutional neural network), SAE (stacked auto encoder) and DBN (deep belief network). The results show that the performance of SDP-DBN method is the best, and the insulation defects can be detected with accuracy of 96.1%. In addition, the visualization ability of data increases after SDP transformation of the original PD time series signal.

Parameters Design and Optimization of SiC MOSFET Driving Circuit with Consideration of Comprehensive Loss and Voltage Stress

In conventional parameters design, the driving circuit is usually simplified as an RLC second-order circuit, and the switching characteristics are optimized by selecting parameters, but the influence of switching characteristics on the driving circuit is not considered. In this paper, the insight mechanism for the gate-source voltage changed by overshoot and ringing caused by the high switching speed of SiC MOSFET is highlighted, and we propose an optimized design method to obtain optimal parameters of the SiC MOSFET driving circuit with consideration of parasitic parameters. Based on the double-pulse circuit, we evaluated the influence of main parameters on the gate-source voltage, including driving voltage, driving resistance, gate parasitic inductance, and stray inductance of the power circuit. A SiC-based boost PFC is constructed and tested. The test results show that the switching loss can be reduced by 7.282 W by using the proposed parameter optimization method, and the over-voltage stress of SiC MOSFET is avoided.

Capacity and Volume Balance of Buffering Converters for the Marine Pulsed Power System

In marine integrated power systems (IPS), dc/dc converters have been widely used to buffer the power of pulsed loads. However, limited by ship space and safety, its buffering capacity and volume are conflicting and need to be balanced. Existing methods usually oversimplify the switching dynamic process caused by the different switching periods between the pulsed loads and converters, leading to oversizing or underpowering issues of the converts. To overcome this hurdle, this article proposes a millisecond-time-scale-based state-space averaging mode (MTS-SSAM) to analyze the dynamic process by taking pulsed loads period instead of the traditional converter period as the averaging operator. Based on MTS-SSAM, taking the system allowable voltage and current fluctuation rate and converter volume as the objective function, the converters’ parameters are optimized considering the balance between buffering capacity and volume. The proposed parameters optimization method can save 49% converter volume compared with existing methods. The simulation and experimental results are presented to verify the proposed SSAM and optimization method.

Parameters Optimization for a Capacitive Six-Axis Force/Torque Sensor by Using Analytical Method

Parameters optimization is crucial for improving the sensing performance of capacitive six-axis force/torque (F/T) sensors. Based on the analytical model, this article designed a capacitor for sensing the different axis F/T as well as presented a novel performance evaluation function for the parameters optimization. This performance evaluation function can effectively represent the relationship between structural parameters and the sensor sensitivity as well as the isotropy. From the results of the parameters optimization, the sensor is manufactured and the experimental evaluation is analyzed. Comparing with the commercial six-axis F/T capacitive sensor, there is a more superior sensing performance for the developed sensor. This proposed parameters optimization method is expected to improve the sensing performance for the capacitive six-axis F/T sensor and expand its application.

Parameter optimization of moving horizon estimation based on hybrid intelligent algorithm

AbstractIn the application of moving horizon estimation (MHE) algorithm, the window length will affect the estimation accuracy and the computing efficiency. For this kind of problem, a method of parameter optimization is proposed to obtain suitable window length. Firstly, in order to facilitate online solution, the optimization problem involved in the algorithm is transformed into a quadratic programming (QP) problem in matrix form. Secondly, for the time index and the estimated residual index that measure different properties, the normalization idea is adopted to incorporate them into the same dimension to design the fitness function, and a genetic optimization algorithm based on simulated annealing mechanism is given to search for the optimal window length. Finally, the proposed parameter optimization method is verified by two cases. The results show that the parameter optimization method has the advantages of excellent local search ability and sufficient convergence, and the window length obtained by this method can better take into account the two performance indexes of the MHE algorithm and improve the estimation performance.

UAV Computing-Assisted Search and Rescue Mission Framework for Disaster and Harsh Environment Mitigation

Disasters are crisis circumstances that put human life in jeopardy. During disasters, public communication infrastructure is particularly damaged, obstructing Search And Rescue (SAR) efforts, and it takes significant time and effort to re-establish functioning communication infrastructure. SAR is a critical component of mitigating human and environmental risks in disasters and harsh environments. As a result, there is an urgent need to construct communication networks swiftly to help SAR efforts exchange emergency data. UAV technology has the potential to provide key solutions to mitigate such disaster situations. UAVs can be used to provide an adaptable and reliable emergency communication backbone and to resolve major issues in disasters for SAR operations. In this paper, we evaluate the network performance of UAV-assisted intelligent edge computing to expedite SAR missions and functionality, as this technology can be deployed within a short time and can help to rescue most people during a disaster. We have considered network parameters such as delay, throughput, and traffic sent and received, as well as path loss for the proposed network. It is also demonstrated that with the proposed parameter optimization, network performance improves significantly, eventually leading to far more efficient SAR missions in disasters and harsh environments.

Mathematical model and motion analysis of a wheeled vibro-impact locomotion system

The paper is aimed at investigating the motion conditions of the wheeled vibro-impact locomotion system equipped with the twin crank-slider excitation mechanism and the additional braking mechanisms allowing only one-way rotation of the wheels. The novelty of the present research consists in the improved mathematical model describing the motion conditions of the vibro-impact system and the proposed parameters optimization technique that allows for maximizing the average translational velocity of the wheeled platform. The main idea of this technique is to provide the maximal velocities of internal bodies when they get in contact with the corresponding impact plates. The numerical modeling results describing the dynamic behavior of the vibro-impact system are obtained in Mathematica software and substantiate the correctness of the developed mathematical model and of the proposed parameters optimization technique. The paper can be of significant practical and scientific interest for researchers and engineers studying and improving the vibratory locomotion systems, e.g., for inspecting and cleaning the pipelines.

High-Voltage Isolated Multiple Output Auxiliary Power Supply with Output Voltage Self-Regulation

Power electronic transformers (PETs) have high voltage isolation requirements and a large number of modules, which requires the auxiliary power supply (APS) to achieve high voltage isolation and multiple outputs. This paper proposes a parameter optimization method for the current source APS applied to PETs, enabling the APS to have multiple output voltage self-regulation capabilities. By optimizing the excitation inductance and leakage inductance for the current transformer of the APS, the excitation current plays a regulating role in energy transmission, thus realizing the self-regulation of the output voltages. The proposed method enables the voltage deviation and voltage change of the output modules to be suppressed under the unbalanced loads and large load fluctuations. Moreover, the APS can still operate normally when a short-circuit or an open-circuit fault occurs on the output sides. Therefore, the stability and reliability of the APS are improved, and the design difficulty of the post-stage voltage regulator circuit is also reduced. Finally, the simulation results are given, and a prototype containing three output modules was built to verify the effectiveness of the proposed parameter optimization method.

Optimization of magnetic composite fluid polishing process based on response surface method

ABSTRACT Currently, research on the optimization of key process parameters in the polishing process usually uses orthogonal test method, which needs a lot of experiments. In order to solve this problem, this paper proposes a parameters optimization of magnetic composite fluid polishing process based on response surface method. Firstly, roughness of the polycarbonate microfluidic chip surface polished with magnetic composite fluid was used as the optimization object, while the machining gap, spindle speed and machining time were defined as the key parameters, and experiments were designed and conducted based on response surface method. Subsequently, using the above experimental data, a prediction model for microfluidic chip surface roughness after polishing that depends on key parameters is established. After that, by the application of the prediction model, the key parameters were optimized and verified by experiments. The results indicated that by using the optimized process parameters, the minimum surface roughness of the microfluidic chip reduced from 0.5 μm to 0.0384 μm, while the maximum and minimum errors between measured and predicted results are 0.0052 μm and 0.0007 μm, respectively. The above result has proved the accuracy of the established model and the effectiveness of the proposed parameters optimization method.

High efficiency orientated milling parameter optimization with tool wear monitoring in roughing operation

High efficiency milling is one of the main goals of CNC machining. Although roughing operation has low requirement for machining accuracy, the milling parameter selection usually need to consider the cutting force, milling stability, spindle torque and power. Besides, the tool wear in difficult-to-cut material machining is another very prominent problem. Therefore, how to achieve the high efficient milling parameters under the multi-constraint condition has been a severe challenge in the field of CNC machining. In this paper, a milling parameter optimization method for efficient rough machining is proposed by combining the off-line optimization and real-time monitoring. Firstly, a mathematical model of the optimization problem is established, which takes the machining efficiency as the objective, the spindle speed, radial and axial depth of cuts as the variables, and considers the multi-constraint, such as the basic parameter feasible region, cutting force, stability, spindle torque and power. Secondly, the quantitative relationship between the spindle three-phase current and milling force is established to realize the real-time estimation of cutting force coefficients. Furthermore, a numerical optimization method based on the random vector search is proposed, and the overall optimization procedure is given. Finally, a series of experimental verifications on the titanium alloy Ti5Al5Mo5VCrFe machining has been carried out, and the case study results show that the proposed parameter optimization method can greatly improve the machining efficiency, which proves the validity of the method.

Optimization for the Assessment of Spudcan Peak Resistance in Clay–Sand–Clay Deposits

Clay–sand–clay deposits are commonly encountered in the offshore field. For spudcan installation in this soil stratigraphy, the potential for punch-through exists, with the peak penetration resistance formed within the interbedded sand layer. Therefore, a careful assessment of the penetration resistance profile has to be performed. Based on the recently proposed failure-stress-dependent model, this paper presents a modified predictive model for estimating the peak resistance. The modified model incorporates the bearing capacity depth factor and the protruded soil plug in the bottom clay layer into the formulation. It is proven that the modified predictive model provides improved deterministic estimations for the peak resistances measured in centrifuge tests. Based on the modified predictive model, a parameter optimization technique is utilized to optimize the prediction of peak resistance using penetration resistances observed beforehand. A detailed application procedure is proposed and applied to the centrifuge tests accumulated from existing publications, with further improvement on the predictions demonstrated. The proposed parameter optimization procedure combined with the modified predictive model provides an approach to perform real-time optimization for assessing spudcan peak resistance in clay–sand–clay deposits.

Parameter optimization of orthogonal discrete Hermite transform formed using eigenvectors of a symmetric tridiagonal matrix

Basis functions of the Discrete Hermite transform (DHT) can be formed as the set of eigenvectors of a symmetric tridiagonal matrix which commutes with the centered discrete Fourier transform matrix. In this paper, we consider the optimization of the associated time-axis scaling factor, which is of crucial importance in the DHT-based applications. The parameter optimization is performed to enhance the signal representation by matching the smallest possible number of basis functions with the time-domain signal waveform, therefore minimizing the number of Hermite coefficients with significant or non-zero values. Such highly concentrated signal representation is particularly amenable for signal compression, filtering, and denoising, while the coefficients of the optimized transform can be exploited as features in signal classification and other machine learning applications. The proposed parameter optimization approach is verified on numerical examples, including the experiments with QRS complexes, specific segments of electrocardiogram (ECG) signals being particularly important in biomedical applications.

Efficiency Optimization Design of L-LLC Resonant Bidirectional DC-DC Converter

The new type of L-LLC resonant bidirectional DC-DC converter (L-LLC-BDC) has merits of high efficiency, high-power density and wide gain and power ranges, and it is suitable for energy interface between energy storage systems and DC micro grid. However, the resonances are sensitive to the parasitic parameters, which will deteriorate the efficiency. This paper investigates the intrinsic mechanism of parasitic parameters on the L-LLC-BDC operating principle and working characteristics based on the analysis of working modes and resonance tank. By taking the oscillation of parasitic parameters produced in the stage for the freewheeling stage into consideration, a parameter optimization method is proposed to reduce the resonant current oscillation while maintaining the characteristic of the natural soft switching. The experiment results not only validated the proposed parameter optimization design method, but also testified to the improvement of the efficiency through the minimization of the conduction and switching loss.

An Optimization Algorithm for Exponential Curve Model of Single Pile Bearing Capacity

The ultimate bearing capacity of single pile is very important to engineering safety, so correctly predicting its value becomes an important part of engineering safety. Based on the traditional exponential curve model, a parameter optimization algorithm of the exponential curve model of single pile bearing capacity, which combines the golden section method and the linear least square method, is proposed. In order to verify the reliability of the proposed optimization algorithm, the measured data of the building engineering in the literature were optimized and calculated. Through comparison, it is found that the optimization algorithm is closer to the measured value than the traditional exponential curve model algorithm, which can better guide the engineering practice, and verify the effectiveness and superiority of the proposed parameter optimization method.

Adaptive Cruise Control with Timed Automata

Abstract An adaptive cruise control (ACC) system maintains the vehicle at the given target speed when there is no leading vehicle in the sensor range. On the other hand, in the presence of a leading vehicle, the system maintains a safe distance between the vehicles while driving as close as possible to the target speed. For such an automated system, besides meeting safety requirements, it is also important to provide a comfortable drive. In this paper, we develop a formal model for adaptive cruise control system based on timed automata and express specifications in temporal logics. The proposed model supports different acceleration levels. Parametric constraints govern the transitions to the states associated with acceleration levels. The proposed parameter optimization methods generate parameter valuations for particular driving styles while guaranteeing safety and the specifications over the target speed. Therefore, the resulting system is guaranteed to satisfy the requirements while the driver comfort is optimized. The models and the synthesis approach are illustrated with examples.

Parameter optimization framework on wave gradients of Wave-CAIPI imaging.

To propose a parameter optimization framework on wave gradients of Wave-CAIPI imaging for decreasing g-factor penalty and reducing reconstruction artifacts. The influences of parameters on g-factor are theoretically analyzed. The average g-factor is chosen as a metric for parameter optimization, and then a fast calculation method is proposed to approximately and ultra-fast calculate the average g-factor. Based on this, a set of points in the function of the average g-factor with respect to the wave gradient parameters is calculated, and the optimal wave gradient parameters are found according to these points. In vivo human brain experiments were performed on 3T MR scanners for the comparison experiments. The results show that the proposed parameter optimization framework is able to efficiently obtain optimal wave gradient parameters, which can achieve decreased g-factor penalty and less artifacts of reconstructions than the empirical parameters. The proposed parameter optimization framework is computationally efficient and can optimize the wave gradient parameters of Wave-CAIPI imaging for better image quality than before.

Structural Parameters Optimization of Elastic Cell in a Near-Bit Drilling Engineering Parameters Measurement Sub.

The downhole engineering parameters measurement sub is a key component of the rotary steerable drilling system. To enable a measurement sub to serve reliably under downhole complex conditions, the structural parameters optimization of its key but weak elastic cell is systematically studied. First, the multiple relations among measurement sensitivities, structural stiffnesses, and strength during structural parameters design are summarized. Second, the selection of the structural parameters of the elastic cell is characterized as a multi-objective optimization model, which is solved using the non-dominated sorting genetic algorithm II (NSGA-II). Furthermore, the finite element method (FEM) is used to verify the measurement performance and static strength of the proposed structure. Finally, transient dynamics analysis is applied to investigate the dynamic strength of the designed structure. The results show that the proposed parameters optimization strategy can quickly obtain the database for the structural parameters design of an elastic cell. The static analysis results based on the FEM further verify the effectiveness of the proposed method. Transient dynamic analysis also reveals the relative rigor of the proposed methodology framework to some extent. This work has practical significance for improving the drilling efficiency and reducing drilling risks. In addition, this proposed methodology has good extensibility.