Optimizing Average Electric Power During the Charging of Lithium-Ion Batteries Through the Taguchi Method

In recent times, lithium-ion batteries have been widely used owing to their high energy density, extended cycle lifespan, and minimal self-discharge rate. The design of high-speed rechargeable lithium-ion batteries faces a significant challenge owing to the need to increase average electric power during charging. This challenge results from the direct influence of the power level on the rate of chemical reactions occurring in the battery electrodes. In this study, the Taguchi optimization method was used to enhance the average electric power during the charging process of lithium-ion batteries. The Taguchi technique is a statistical strategy that facilitates the systematic and efficient evaluation of numerous experimental variables. The proposed method involved varying seven input factors, including positive electrode thickness, positive electrode material, positive electrode active material volume fraction, negative electrode active material volume fraction, separator thickness, positive current collector thickness, and negative current collector thickness. Three levels were assigned to each control factor to identify the optimal conditions and maximize the average electric power during charging. Moreover, a variance assessment analysis was conducted to validate the results obtained from the Taguchi analysis. The results revealed that the Taguchi method was an eff ective approach for optimizing the average electric power during the charging of lithium-ion batteries. This indicates that the positive electrode material, followed by the separator thickness and the negative electrode active material volume fraction, was key factors significantly infl uencing the average electric power during the charging of lithium-ion batteries response. The identification of optimal conditions resulted in the improved performance of lithium-ion batteries, extending their potential in various applications. Particularly, lithium-ion batteries with average electric power of 16 W and 17 W during charging were designed and simulated in the range of 0-12000 s using COMSOL Multiphysics software. This study efficiently employs the Taguchi optimization technique to develop lithium-ion batteries capable of storing a predetermined average electric power during the charging phase. Therefore, this method enables the battery to achieve complete charging within a specific timeframe tailored to a specificapplication. The implementation of this method can save costs, time, and materials compared with other alternative methods, such as the trial-and-error approach.

Probability distribution of wind power volatility based on the moving average method and improved nonparametric kernel density estimation

In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling method is proposed based on the method of moving average and adaptive nonparametric kernel density estimation(NPKDE)method.Firstly,the method of moving average is used to reduce the fluctuation of the sampling wind power component,and the probability characteristics of the modeling are then determined based on the NPKDE.Secondly,the model is improved adaptively,and is then solved by using constraint-order optimization.The simulation results show that this method has a better accuracy and applicability compared with the modeling method based on traditional parameter estimation,and solves the local adaptation problem of traditional NPKDE.

An Improved Non-isolated LED Converter with Power Factor Correction and Average Current Mode Control

A new type of high power LED drivers is proposed by adopting an improved two-stages non-isolated configuration. In order to improve power factor and achieve accurate average current control under universal input voltages ranging from 100 Vrms to 240 Vrms, the power factor correction and average current mode control methods operating in continuous current conduction mode are designed and implemented. With the LUMILEDS emitter type LEDs, a laboratory prototype is built and measured. And from the measured results, it could be concluded that the proposed driver has many better performances such as high power factor, low current harmonic, accurate average current control and switch protection.

A Novel Peak-to-Average Power Ratio Reduction Scheme via Tone Reservation in OFDM Systems

In this paper, a novel signal-to-clipping noise ratio and least squares approximation tone reservation scheme(SCR-LSA TR) is proposed to reduce the peak-to-average power ratio for orthogonal frequency division multiplexing systems. During the SCR procedure, only the element with the maximal amplitude is picked for processing, which not only decreases the algorithm complexity, but also helps to overcome the BER deterioration. With the LSA method, the amplitude of the peak-cancelling signals can approximate to that of the original clipping noise as much as possible. Through the combination of the optimization factor in the LSA method, the classic SCR method can achieve better PAPR reduction with faster convergence. Simulation results show that the proposed SCR-LSA TR scheme has less in-band distortion and smaller out-of-band spectral radiation. The BER of the proposed scheme shows a better performance especially under the 16-QAM over the additive white Gaussian noise channel.

Average Power Function of Noise and Its Applications in Seasonal Time Series Modeling and Forecasting

This paper presents a new method of detecting multi-periodicities in a seasonal time series. Conventional methods such as the average power spectrum or the autocorrelation function plot have been used in detecting multiple periodicities. However, there are numerous cases where those methods either fail, or lead to incorrectly detected periods. This, in turn in applications, produces improper models and results in larger forecasting errors. There is a strong need for a new approach to detecting multi-periodicities. This paper tends to fill this gap by proposing a new method which relies on a mathematical instrument, called the Average Power Function of Noise (APFN) of a time series. APFN has a prominent property that it has a strict local minimum at each period of the time series. This characteristic helps one in detecting periods in time series. Unlike the power spectrum method where it is assumed that the time series is composed of sinusoidal functions of different frequencies, in APFN it is assumed that the time series is periodic, the unique and a much weaker assumption. Therefore, this new instrument is expected to be more powerful in multi-periodicity detection than both the autocorrelation function plot and the average power spectrum. Properties of APFN and applications of the new method in periodicity detection and in forecasting are presented.

PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

Data transmission through a wireless network has faced various signal problems in the past decades.The orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among others.The main problem faced by 5G wireless OFDM is distortion of transmission signals in the network.This transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various techniques.This study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless communication.Partial transmit sequence(PTS)helps in the fast transfer of data in wireless LTE.PTS is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G networks.Result indicates that the proposed system outperforms other existing techniques.Therefore,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm optimization.Hence,the specified design supports in improving the proposed PAPR reduction architecture.

A PHASE SCRAMBLING SCHEME FOR SUPPRESSION OF PEAK TO AVERAGE POWER RATIO FOR OFDM

The complex-valued modulating vectors for the subcarriers consist of two kinds of components: One is the information-bearing components superposed with pseudo-randomized phases and the other is the suppression components with specified scrambling phases. The pseudo randomized phases are generated according to the predefined polynomial and mapping function whereas the scrambling phases are from a gradient algorithm. The simulation results verify the rationality and validity of the phase scrambling.

Reducing Peak-to-Average Power Ratio of a Turbo Coded OFDM

Peak to Average Power Ratio (PAPR) is defined as the instantaneous power (maximum value) to the average power ratio. PAPR is considered to be a major problem in OFDM systems. This problem can cause radical unexpected behavior of the signal fluctuation. This fluctuation is constituted by a large number of power states. The enormous number of these states leads to an additional complexity of ADCs and DACs. This research addresses the previous problem in OFDM systems utilizing Turbo Codes. μLaCP technique is employed for the purpose of decreasing PAPR. Moreover, our OFDM system was simulated in the presence of an AWGN channel with four types of codes (without the presence of ADCs and DACs). These were constituted of PCCC (typical and new), SCCC, and Convolutional Codes. Our Turbo Coded OFDM exhibited unchanged BER performance before and after the use of μLaCP technique. This was accomplished by modifying our previous PAPR reduction technique without sacrificing greatly its attributes.

A correlation OPTS algorithm for reducing peak to average power ratio of FBMC-OQAM systems

A correlation overlapping partial transmit sequence(C-OPTS) algorithm is proposed to solve the issue of high complexity of overlapping partial transmit sequence(OPTS) algorithm in suppressing the peak to average power ratio(PAPR) of filter bank multicarrier-offset quadrature amplitude modulation(FBMC-OQAM) signals.The V subblocks in partial transmit sequence(PTS) are regrouped into U combinations according to the correlation coefficient p,and overlapping subblocks are allowed between adjacent groups.The search starts from the first group and sets the phase factors of the subsequent groups to 1.When the phase factors of the non-overlapping subblocks in the first group are determined,the subsequent groups are searched in turn to determine their respective phase factors.Starting from the second data block,the data overlapped with it should be taken into account when determining its optimal phase factor vector.Theoretical analysis and simulation results indicate that compared with the OPTS algorithm,the proposed algorithm can significantly reduce the computational complexity at the cost of slight deterioration of PAPR performance.Meanwhile,compared with the even-odd iterative double-layers OPTS(ID-OPTS) algorithm,it can further reduce the complexity and obtain a better PAPR suppression effect.

The Average Power of a System under Random Excitation

The Taylor series expansion method is used to derive an approximation for theaverage total power of a non-linear system subjected to wide-band random excitation with zeromean value. The average dissipating power for non-linear systems subjected to randomexcitation is also discussed. The results of the derivation show that the average total power andthe average dissipating Power of a Systems are independent, of the dissipating force of the systemwhen excited by white noise. On the other hand, the average total energy is show,l to be inverselyproportional to damping,. The total power is dependent on both, the input and dissipating powerof the system.

Joint Optimization of Peak-to-Average Power Ratio and Spectral Leakage in 5G Multicarrier Waveforms

High peak-to-average-power ratio(PAPR) and spectral leakage are two main drawbacks of multicarrier systems, such as in orthogonal frequency division multiplexing(OFDM), in future 5G wireless communications. For generating optimized wave forms for 5G communications, this paper proposes an iterative scheme combining time-domain N-continuous OFDM(TD-NC-OFDM) and serial peak cancellation(SPC). Based on the theory of projection onto convex sets(POCS), the effectiveness of the proposed scheme is proved, and the optimized time-frequency domain multicarrier waveforms are analyzed in terms of a balanced tradeoff between out-of-band spectral leakage and PAPR. Both theoretical analysis and simulation results show that the proposed scheme can jointly optimize both the PAPR and out-ofband radiation, with moderate computational complexity.

Simulation on Peak-to-Average Power Ratio for Orthogonal Frequency Division Multiplexing

Clipping is a simple and convenient PAPR （peak-to-average power ratio） reduction method for high speed OFDM （orthogonal frequency division multiplexing） communication system. In this paper, we propose a new PAPR reduction method for Wireless-MAN（metropolitan area network）-OFDM system based on IEEE 802.16, which is over-sampling clipping arithmetic. Simulation and performance of the over-samples clipping＇s PAPR reduction capability, BER effect is given. The simulation indicates that the PAPR of at least 99.9% OFDM symbol is below 6dB after 2 Nyquist rate clipping, and the performance of BER has 1dB SNR（signal noise ratio） loss. The results prove that this method has better capacity to reducing PAPR. So it can be well used in WMAN-OFDM system.

Average modeling of Single Stage Flyback PFC ＋ Flyback DC／DC converter

With the use of this novel average model for Single Stage Flyback PFC+Flyback DC/DC converter, voltage control mode, peak current control mode and average current control mode can be simulated easily by changing the model's parameters. It can be used to do various analysis not only for small signal and static behavior but also for large signal and dynamic behavior of the converter. By using this average model the simulation speed can be improved by 2 orders of magnitude above that obtained by using the conventional switched model. It can be applied to optimize the trade\|off between high power factor, voltage stress, current stress and good output performance while designing this kind of single stage PFC converter. A 60W single stage power factor corrector was built to verify the proposed model. The modeling principle can be applied to other Single Stage PFC topologies.

Randomly Weighted Averages on Order Statistics

We study a well-known problem concerning a random variable uniformly distributed between two independent random variables. Two different extensions, randomly weighted average on independent random variables and randomly weighted average on order statistics, have been introduced for this problem. For the second method, two-sided power random variables have been defined. By using classic method and power technical method, we study some properties for these random variables.

Average cluster sizes and cluster size distributions of superfine nickel particles in light media

Effects of shear rates on average cluster sizes (ACSs) and cluster size distributions (CSDs) in uni- and bi-systems of partly charged superfine nickel particles were investigated by Brownian dynamics, and clustering properties in these systems were compared with those in non-polar systems. The results show that the ACSs in bi-polar systems are larger than those in the non-polar systems. In uni-polar systems the behavior of clustering property differs: at the lower ionic concentration (10%), repulsive force is not strong enough to break clusters, but may greatly weaken them. The clusters are eventually cracked into smaller ones only when concentration of uni-polar charged particles is large enough. In this work, the ionic concentration is 20%. The relationship between ACS and shear rates follows power law in a exponent range of 0.176-0.276. This range is in a good agreement with the range of experimental data, but it is biased towards the lower limit slightly.

基于调和概率语言术语集的Power平均算子

实践发现现存概率语言Power平均算子在应用中存在概率信息丢失、计算过于繁琐等问题,针对这些问题,提出一种基于调和概率语言术语集的Power平均算子。结合新的基本运算法则与新的支撑度,给出新的概率语言Power平均算子、概率语言Power几何平均算子、概率语言Power加权平均算子、概率语言Power加权几何平均算子,同时论证分析了这几类新型算子的性质。最后通过实验数据,将提出的算子与现存算子进行对比分析,验证了所提算子的有效性与可行性。

A Low Complexity Linear Moving Average Filtering Technique for PAR Reduction in OFDM Systems

In this paper, a linear moving average recursive filtering technique is proposed to reduce the peak-to-average power ratio (PAR) of orthogonal frequency division multiplexing (OFDM) signals. The proposed low complexity technique is analyzed in an oversampled OFDM system and a simple distribution approximation of the oversampled and linearly filtered OFDM signals is also proposed. Corresponding time domain linear equalizers are developed to recover originally transmitted data symbols. Through extensive computer simulations, effects of the new filtering technique on the oversampled OFDM peak-to-average power ratio (PAR), power spectral density (PSD) and corresponding linear equalizers on the frequency selective Rayleigh fading channel transmission symbol-error-rate (SER) performance are investigated. The newly proposed recursive filtering scheme results in attractive PAR reduction, requires no extra fast Fourier transform/inverse fast Fourier transform (FFT/IFFT) operations, refrains from transmitting any side information, and reduces out-of-band radiation. Also, corresponding linear receivers are shown to perform very close to their frequency domain counterparts.

Reliability-BasedModel for Incomplete Preventive ReplacementMaintenance of Photovoltaic Power Systems

At present,the operation and maintenance of photovoltaic power generation systems mainly comprise regular maintenance,breakdown maintenance,and condition-based maintenance,which is very likely to lead to over-or under-repair of equipment.Therefore,a preventive maintenance and replacement strategy for PV power generation systems based on reliability as a constraint is proposed.First,a hybrid failure function with a decreasing service age factor and an increasing failure rate factor is introduced to describe the deterioration of PV power generation equipment,and the equipment is replaced when its reliability drops to the replacement threshold in the last cycle.Then,based on the reliability as a constraint,the average maintenance cost and availability of the equipment are considered,and the non-periodic incomplete maintenance model of the PV power generation system is established to obtain the optimal number of repairs,each maintenance cycle and the replacement cycle of the PV power generation system components.Next,the inverter of a PV power plant is used as a research object.The model in this paper is compared and analyzed with the equal cycle maintenance model without considering reliability and the maintenance model without considering the equipment replacement threshold,Through model comparison,when the optimal maintenance strategy is(0.80,4),the average maintenance cost of this paper’s model are decreased by 20.3%and 5.54%and the availability is increased by 0.2395% and 0.0337%,respectively,compared with the equal-cycle maintenance model without considering the reliability constraint and the maintenance model without considering the equipment replacement threshold.Therefore,this maintenance model can ensure the high reliability of PV plant operation while increasing the equipment availability to improve the system economy.

直觉模糊Power交叉影响算子及其应用

将Power算子推广到直觉模糊环境中,提出了直觉模糊Power交叉影响平均算子,考虑了不同直觉模糊集的隶属度与非隶属度之间可能存在的交叉影响,根据数学归纳法得到了其具体计算公式,并研究了其性质。实例说明了新的集成算子在多属性群决策应用中的有效性。将提出的算子与现存的直觉模糊Power平均算子做了稳定性比较。

User Association and Power Allocation for UAV-Assisted Networks: A Distributed Reinforcement Learning Approach

Unmanned aerial vehicles(UAVs)can be employed as aerial base stations(BSs)due to their high mobility and flexible deployment.This paper focuses on a UAV-assisted wireless network,where users can be scheduled to get access to either an aerial BS or a terrestrial BS for uplink transmission.In contrast to state-of-the-art designs focusing on the instantaneous cost of the network,this paper aims at minimizing the long-term average transmit power consumed by the users by dynamically optimizing user association and power allocation in each time slot.Such a joint user association scheduling and power allocation problem can be formulated as a Markov decision process(MDP).Unfortunately,solving such an MDP problem with the conventional relative value iteration(RVI)can suffer from the curses of dimensionality,in the presence of a large number of users.As a countermeasure,we propose a distributed RVI algorithm to reduce the dimension of the MDP problem,such that the original problem can be decoupled into multiple solvable small-scale MDP problems.Simulation results reveal that the proposed algorithm can yield lower longterm average transmit power consumption than both the conventional RVI algorithm and a baseline algorithm with myopic policies.