Multi‐phase method of estimation and adaptation of parameters of electrical battery models

Mathematical modeling of the battery lifetime is an important tool for the design of more efficient batteries, as well as for the optimization of their use. The electrical models class is among the classes of mathematical models used for this purpose, and a fundamental step to their application is the correct estimation of their parameters. This paper performs the mathematical modeling of Lithium‐Ion Polymer batteries lifetime through the electrical model of Tremblay, in which a multi‐phase method of estimation and adaptation of parameters is proposed, divided into three phases: discovery, learning, and inference. The multi‐phase method is based on two Artificial Intelligence techniques: genetic algorithms and artificial neural networks. The proposed method is validated by the simulation and experimental studies. From the results, it is concluded that the application of the multi‐phase method improves the effective accuracy of the Tremblay model, when it comes to adapt its parameters to the battery during runtime. For constant discharge currents, the average error reduction was 79%, when compared to the best set of parameters obtained by GA without the adaptation process. For variable current discharge curves, the method was able to reduce the error more than 35%. This method can be applied to other battery lifetime prediction models.     

A new integrated single‐diode solar cell model for photovoltaic power prediction with experimental validation under real outdoor conditions

The output power prediction by a photovoltaic (PV) system is an important research area for which different techniques have been used. Solar cell modeling is one of the most used methods for power prediction, the accuracy of which strongly depends on the selection of cell parameters. In this study, a new integrated single‐diode solar cell model based on three, four, and five solar cell parameters is developed for the prediction of PV power generation. The experimental validation of the predicted results is done under outdoor climatic conditions for an Indian location. The predicted power by three models is found close to measured values within 4.29% to 4.76% accuracy range. The comparative power estimation analysis by these models shows that the three‐parameter model gives higher accuracy for low solar irradiance values <150 W/m², the four‐parameter model in the range of 150 to 500 W/m², and the five‐parameter model for >500 W/m². The present model is also compared with other models in literature and is found to be more accurate with less percentage error. The overall results also show that the power produced depends on temperature and solar radiation levels at a particular location. Thus, single solar cell model developed can be used with sufficient accuracy for power forecast of PV systems for any location worldwide. The follow‐up research areas are also identified.     

A new method based on charge parameters to analyse the performance of stand-alone photovoltaic systems

The monitored data in photovoltaic systems are processed to determine overall energy balances which, together with the energy efficiencies and indices of performance, give a good indication of the performance of PV systems. However, the analysis based on energy parameters shows some shortcomings when they are used to analyse the performance of stand-alone photovoltaic (SAPV) systems, especially those without Maximum Power Point Tracker (MPPT). This kind of systems represents a large percentage of the SAPV systems (e.g. demonstration projects, consumer and industrial applications, Solar Home Systems in developing countries, etc.).This paper tries to give an alternative method that manages to analyse this kind of systems in a better way. This method is based on a translation of the energy parameters given by the Joint Research Center and the IEC Standard 61724 into new charge-related parameters. It must be said that charge parameters can be used by themselves to evaluate the system performance. Therefore, it is not necessary to deduce energy parameters using the nominal battery voltage.The monitored data of two SAPV systems without MPPT are used to compare the performance of these systems based on energy parameters with the analysis provided by the new charge parameters. This study will highlight the advantages of the charge parameters method.     

Evaluation of PV output in terms of environmental impact based on mathematical and artificial neural network models

This paper presents different prediction models for a grid‐connected photovoltaic (GCPV) system based on long‐term data sets. A 1.4 kW GCPV installed in Sohar, Oman, where measurements of the electrical and environmental parameters are taken every second for 5 years (from May 2014 to April 2019) to reduce uncertainty and improve the proposed model's accuracy. The highest power and energy measured from the GCPV are 1180 W and 245.8 kWh, respectively. Mathematical regression and cascade‐feed forward propagation (CFNN) models for PV current, voltage, and power were proposed in terms of environmental impact. A NeuroSolutions package was used to propose GCPV models for electrical quantities. An evaluation criterion is defined, in this study, to assess the system's performance. The proposed models show excellent agreement with the measured experimental data. However, CFNN shows a superior accuracy compared with empirical and regression proposed models. The two models have proposed a dispute, but the model relating the solar irradiation and ambient temperature to GCPV current is more accurate. To evaluate and validate the proposed CFNN models, mean square error, MAE, root mean square error, and R² metrics have been used as a criterion and compared with different artificial neural networks models in the literature. The proposed CFNN is found to give the most accurate results in terms of MSE = 0.0007, MAE = 0.4310, and RMSE = 0.0290 and highest accuracy R² = 0.9999, which shows the effectiveness and feasibility of the proposed models.     

Comparison of two PV array models for the simulation of PV systems using five different algorithms for the parameters identification

Simulation is of primal importance in the prediction of the produced power and automatic fault detection in PV grid-connected systems (PVGCS). The accuracy of simulation results depends on the models used for main components of the PV system, especially for the PV module. The present paper compares two PV array models, the five-parameter model (5PM) and the Sandia Array Performance Model (SAPM). Five different algorithms are used for estimating the unknown parameters of both PV models in order to see how they affect the accuracy of simulations in reproducing the outdoor behavior of three PVGCS. The arrays of the PVGCS are of three different PV module technologies: Crystalline silicon (c-Si), amorphous silicon (a-Si:H) and micromorph silicon (a-Si:H/μc-Si:H).The accuracy of PV module models based on the five algorithms is evaluated by means of the Route Mean Square Error (RMSE) and the Normalized Mean Absolute Error (NMAE), calculated for different weather conditions (clear sky, semi-cloudy and cloudy days). For both models considered in this study, the best accuracy is obtained from simulations using the estimated values of unknown parameters delivered by the ABC algorithm. Where, the maximum error values of RMSE and NMAE stay below 6.61% and 2.66% respectively.     

A review on modeling of solar photovoltaic systems using artificial neural networks,fuzzy logic,genetic algorithm and hybrid models

The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.     

基于改进粒子群算法的风力机选型应用研究

风力机的选型是风电场建设的重要内容,它对风电场建设造价、投产后的发电量以及运行维护成本等有直接影响。文章在给定风资源的情况下,综合考虑风电场的容量系数和实际发电量,以风力机性能指数作为选型的依据,针对采用常规方法进行风力机参数线性化求解的缺陷,采用智能化的改进粒子群算法对风力机参数进行寻优。与常规计算方法相比,该方法寻得的风力机性能指数更优。结合具体实例计算候选机型的风速加权标准差,选出最优风力机。该研究结果为风电场的风力机选型提供了一种有效可行的方法,具有一定的应用参考价值。     

计及反射辐射的分布式光伏阵列建模研究

针对分布式光伏阵列传统建模方法不能考虑阵列自身安装特点及周边复杂太阳辐射环境,从而导致模型不准确的缺陷,文章提出了一种计及反射辐射的光伏阵列建模方法。利用气象站采集数据,通过研究建筑物立面对光伏阵列产生的反射辐射,建立了反射辐射模型。同时,根据太阳位置、建筑物以及光伏阵列相对位置关系,分析建筑物对光伏电站的遮挡影响,建立了复杂环境下光伏阵列辐射模型。以光伏组件的输出数学模型为基础,结合光伏阵列的安装方式,通过叠加法得到光伏阵列输出模型,并通过实验验证了模型精度。     

Reliable and efficient approach for mitigating the shading effect on photovoltaic module based on Modified Artificial Bee Colony algorithm

The operation and performance of a photovoltaic system (PV) are affected by some factors such as; solar radiation, ambient temperature, PV array configuration and shadow which may be either completely or partially. The partially shadow is caused by clouds, trees due to wind, neighboring buildings and utilities. The shadow effect causes the multiple local maximum power points in the PV module voltage-power characteristics and only one Global Maximum Power Point (GMPP); additionally the shadowing causes high power loss in the shaded cells and produces hot spot. In this paper a new optimization approach based on proposed Modified Artificial Bee Colony (MABC) algorithm is used to solve a proposed constrained objective function of PV module power loss and mitigate the shading effect. The proposed MABC is compared with GA, PSO and ABC. The obtained results proved that the MABC is the most efficient algorithm in solving the objective function that mitigating the power loss in the PV module under partially shading effect.     

SOC estimation method of power battery based on BP artificial neural network

电池剩余电量（SOC）的估算是电池管理系统中的关键技术之一,在众多估算方法中,神经网络在估算的准确性及鲁棒性上具有明显优势。庞大的数据量是获得SOC精确值的重要因素。针对以上问题,研究提出了基于BP人工神经网络的动力电池SOC估算方法,以某型号整包电池作为实验对象,通过对电池电压、电流、内阻及温度的数据采集,获得海量数据。建立电池的等效电路模型,考虑电池极化、充放电倍率及温度的影响对初始数据进行修正。基于MATLAB平台建立BP人工神经网络模型,数据修正后用于网络模型的训练,并验证了模型的可行性。将模型用于实验数据的预测,通过函数拟合实现了SOC的估算。最后,通过对比SOC的预测值与实际测量值,最终证明建立的人工神经网络模型对SOC估算的有效性。     

Influence of annealing in different chlorides on the photovoltaic parameters of CdS/CdTe solar cells

This paper investigates CdS/CdTe solar cells treated with various chloride solutions and then annealed in the air before the deposition of the back contact. It was found that the cells with higher efficiency were fabricated using CdCl₂ solution treatment and thermal annealing. Such cells are further examined by measuring their current density vs. voltage (J–V) characteristics at various temperatures, quantum efficiency (QE), and capacitance vs. voltage (C–V) characteristics. In addition, the uniformity of the QE across the cells was examined by using the laser beam induced current (LBIC) technique.     

Analytical model for predicting the performance of photovoltaic array coupled with a wind turbine in a stand-alone renewable energy system based on hydrogen

We present the results of an analysis of the performance of a photovoltaic array that complement the power output of a wind turbine generator in a stand-alone renewable energy system based on hydrogen production for long-term energy storage. The procedure for estimating hourly solar radiation, for a clear sunny day, from the daily average solar insolation is also given. The photovoltaic array power output and its effective contribution to the load as well as to the energy storage have been determined by using the solar radiation usability concept. The excess and deficit of electrical energy produced from the renewable energy sources, with respect to the load, govern the effective energy management of the system and dictate the operation of an electrolyser and a fuel cell generator. This performance analysis is necessary to determine the effective contribution from the photovoltaic array and the wind turbine generator and their contribution to the load as well as for energy storage.     

基于人工神经网络的供热管网终端温度预测模型

从供热管网终端泄漏诊断需要出发，引出求解正常供热时管网系统的终端温度。绕过传统的通过求解微分方程确定温度的方法，利用人工神经网络来预测管网系统的终端温度，从而为供热管网终端泄漏诊断提供参考依据。在应用人工神经网络时，采用基于相关分析法的节点删除法来优化网络结构提高网络性能。仿真分析表明基于这种结构优化的人工神经网络模型对供热管网终端温度预测较为准确，对泄漏诊断有一定帮助。     

基于威布尔分布的风速概率分布参数估计方法

准确地描述风速特性,直接影响风电场风能资源评估的结果.文章介绍了基于威布尔分布的平均风速及最大风速估计法、矩估计法、最小二乘估计法和极大似然估计法等4种风速概率分布参数估计的方法.通过对乌兰察布地区测风塔实际数据的分析,比较了4种方法的参数估计结果,得到以下结论:在风能资源较丰富地区,平均风速及最大风速估计法的风速拟合效果波动较大,对平均风能密度估计误差较大;矩估计法、最小二乘估计法和极大似然估计法拟合效果良好.     

The optimal parameters estimation for rectangular cylinders installed transversely in the flow channel of PEMFC from a three-dimensional PEMFC model and the Taguchi method

The study first applies a three-dimensional model to analyze the cell performance of PEMFCs using rectangular cylinders with various numbers transversely inserted at the axis in the channel, and finds the higher performance with reasonable pressure drop. The Taguchi optimization methodology is then combined with the three-dimensional PEMFC model to determine the optimal combination of five primary operating parameters for the best arrangement of the rectangular cylinders in the channel. The results indicate that the optimal combination factor is a cell temperature of 313 K, an anode humidification temperature of 333 K, a cathode humidification temperature of 333 K, a hydrogen stoichiometric flow ratio of 1.9, and an oxygen stoichiometric flow ratio of 2.7. This study also examines the pressure drop for the channels with rectangular cylinders transversely inserted. Using experimental data verifies the numerical results of the flow field design with rectangular cylinders.     

Modeling the effect of non-linear process parameters on the prediction of hydrogen production by steam reforming of bio-oil and glycerol using artificial neural network

Biomass-derived substrates such as bio-oil and glycerol are gaining wide acceptability as feedstocks to produce hydrogen using a steam reforming process. The wide acceptability can be attributed to a huge amount of glycerol and bio-oil obtained as by-products of biodiesel production and pyrolysis processes. Several parameters have been reported to affect the production of hydrogen by biomass steam reforming. This study investigates the effect of non-linear process parameters on the prediction of hydrogen production by biomass (bio-oil and glycerol) steam reforming using artificial neural network (ANN) modeling technique. Twenty different multilayer ANN model architectures were tested using datasets obtained from the bio-oil and glycerol steam reforming. Two algorithms namely Levenberg-Marquardt and Bayesian regularization were employed for the training of the ANNs. An optimized network configuration consisting of 3 input layer 14 hidden neurons, 1 output layer, and 3 input layer, 5 hidden neurons, and 1 output layer were obtained for the Levenberg-Marquardt and Bayesian regularization trained network, respectively for hydrogen production by bio-oil steam reforming. While an optimized network configuration consisting of 5 input nodes, 9 hidden neurons, 1 output node, and 5 input nodes, 8 hidden neurons, and 1 output node were obtained for Levenberg-Marquardt and Bayesian regularization trained network, respectively for hydrogen production by glycerol steam reforming. Based on the optimized network, the predicted hydrogen production from the bio-oil and glycerol steam agreed with the actual values with the coefficient of determination (R²) > 0.9. A low mean square error of 3.024 × 10⁻²⁴ and 6.22 × 10⁻¹⁵ for the optimized for Levenberg-Marquardt and Bayesian regularization-trained ANN, respectively. The neural network analyses of the two processes showed that reaction temperature and glycerol-to-water molar ratio were the most relevant factors that influenced the production of hydrogen by bio-oil and glycerol steam reforming, respectively. This study has demonstrated the robustness of the ANN as a technique for investigating the effect of non-linear process parameters on hydrogen production by bio-oil and glycerol steam reforming.     

Effects of testing configurations and cell geometries on the performance of a SOFC: A modeling approach

A two-dimensional electrochemical model including mass and charge transfer has been developed to illustrate the effects of testing configurations and cell geometries on SOFC’s performance. The model is favorably validated by the well agreeable experimental results. It is shown that the separation of anode and cathode overpotential by three-electrode method is essentially impractical for anode-supported SOFCs. It is also found that testing configuration has little effect on cell performance due to the high electrical conductivity in anode. Furthermore, because the effective resistance of electrolyte is lower for asymmetric cells than for symmetric cells, cell performance of a symmetric cell is smaller than that of an asymmetric cell. Discrepancy in cell performance is shown to increase with increasing the thickness of electrolyte or decreasing the cathode radii.     

The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data

The main objective of present study is to predict daily global solar radiation (GSR) on a horizontal surface, based on meteorological variables, using different artificial neural network (ANN) techniques. Daily mean air temperature, relative humidity, sunshine hours, evaporation, and wind speed values between 2002 and 2006 for Dezful city in Iran (32°16′N, 48°25′E), are used in this study. In order to consider the effect of each meteorological variable on daily GSR prediction, six following combinations of input variables are considered:

(I)

Day of the year, daily mean air temperature and relative humidity as inputs and daily GSR as output.

(II)

Day of the year, daily mean air temperature and sunshine hours as inputs and daily GSR as output.

(III)

Day of the year, daily mean air temperature, relative humidity and sunshine hours as inputs and daily GSR as output.

(IV)

Day of the year, daily mean air temperature, relative humidity, sunshine hours and evaporation as inputs and daily GSR as output.

(V)

Day of the year, daily mean air temperature, relative humidity, sunshine hours and wind speed as inputs and daily GSR as output.

(VI)

Day of the year, daily mean air temperature, relative humidity, sunshine hours, evaporation and wind speed as inputs and daily GSR as output.

Multi-layer perceptron (MLP) and radial basis function (RBF) neural networks are applied for daily GSR modeling based on six proposed combinations.The measured data between 2002 and 2005 are used to train the neural networks while the data for 214 days from 2006 are used as testing data.The comparison of obtained results from ANNs and different conventional GSR prediction (CGSRP) models shows very good improvements (i.e. the predicted values of best ANN model (MLP-V) has a mean absolute percentage error (MAPE) about 5.21% versus 10.02% for best CGSRP model (CGSRP 5)).     

深井地埋管换热器配置参数模拟计算研究

针对深井地埋管换热系统运行原理,根据地埋管换热器热阻-热容优化模型,建立深井地埋管井孔内、外非稳态柱坐标传热模型。基于环渤海湾盆地埋深1 000～2 000 m热储层水文地质条件,采用双连续介质空间耦合有限元数值计算方法,分析深井地埋管典型配置参数取值对于地埋管换热性能的影响程度。研究结果表明：深井地埋管换热性能随着系统运行时间的推移出现衰减趋势,至供暖季末期(120.0 d)深井地埋管换热量下降20%左右;当深井地埋管循环水量由10增大到60 m³/h时,深井地埋管平均换热量提高150.80 kW,同时循环水泵耗功率也相应提高26.00 kW;深井地埋管埋深由1 600提高到2 400 m时,平均换热量提高113%,耗功率提高50%;当进水套管直径由168提高到299 mm时,平均换热量提高10%,耗功率降低27%。     

A semi-empirical model considering the influence of operating parameters on performance for a direct methanol fuel cell

This research focuses on modeling the relationships between operating parameters and performance measures for a single stack direct methanol fuel cell (DMFC). Four operating parameters, including temperature, methanol concentration, and methanol and air flow rates, are considered in this work. Performance of the DMFC is described by the relationship between current density and voltage. The open circuit voltage and voltage drop in the closed circuit due to resistance, activation, and concentration polarization are influenced by the operating parameters. To consider both modeling accuracy and simplicity, a semi-empirical model is developed in this work by integrating theoretical and approximation models. Experiments were designed and conducted to collect the required data and to obtain the coefficients in the semi-empirical model. The error analysis indicates that our semi-empirical model is effective for predicating the DMFC's performance. The influence of the four operating parameters on the DMFC's performance is also analyzed based on this semi-empirical model. Possible applications of the semi-empirical model in the optimal control of fuel cell systems are also discussed.