基于IEHO算法的太阳电池模型参数辨识

针对传统太阳电池模型参数辨识方法存在精度低、收敛速度慢、易陷入局部最优等不足,提出基于改进象群游牧优化(IEHO)算法的太阳电池模型参数辨识方法。引入混沌初始化,改善初始种群质量,增强种群的遍历性;增加快速移动算子,使算法的收敛速度和全局搜索能力有较大提升;引入精英策略,用最优个体替代最差个体,加快算法寻优速度,缩短寻优时间。应用于太阳电池模型的参数辨识中,IEHO算法比其他算法得到的辨识结果更快更好。对不同光照条件下的太阳电池模型进行参数辨识,辨识结果与实测数据拟合度很高,表明IEHO算法能在不同环境下准确有效地进行太阳电池模型的参数辨识。     

基于最小二乘法的PEMFC电堆模型参数辨识

质子交换膜燃料电池(PEMFC)电堆的优化控制需要精确的电堆模型和参数。该文基于半经验机理模型,根据PEMFC电堆输出特性经验公式以及电化学、热力学原理建立电堆输出电压的机理模型,采用线性回归最小二乘法进行模型参数辨识。仿真实验辨识结果的误差分析表明,其电堆模型参数辨识精度满足控制要求,验证了所提方法的有效性和可行性。     

基于ALMBO算法的PEMFC分数阶时域子空间模型研究

针对质子交换膜燃料电池（PEMFC）发电过程复杂难以建模的问题,考虑PEMFC系统的分数阶特性,提出一种基于优化的分数阶时域子空间辨识方法,并建立PEMFC的分数阶状态空间模型。首先,将分数阶微分理论与子空间时域辨识方法相结合,采用Poisson滤波器对输入输出信号进行滤波处理,并引入权重矩阵提高辨识的精度;其次,对Poisson滤波器以及辨识的分数阶阶次寻优,提出一种变异反向学习的自适应帝王蝶优化算法（ALMBO）,在迁移算子中引入变异反向学习策略、并融入自适应权重来提高寻优的精度,防止陷入局部最优解。最后,通过仿真结果验证算法的有效性,所得的PEMFC辨识模型能准确描述PEMFC的动态过程。     

基于全局信息融合粒子群算法的水轮发电机模型参数辨识

针对粒子群(PSO)优化算法辨识发电机模型参数时存在局部最优和后期收敛速度慢很难准确获取具有强泛化能力的模型参数的问题,提出了一种基于多粒子全局信息共享和变权重的全局信息融合PSO算法(GPSO),并通过IEEE3机9节点系统算例验证了该算法的有效性。结果表明,与常规PSO算法相比,该算法具有泛化能力强、辨识精度高和后期收敛速度快的优点。     

基于T-S模型的质子交换膜燃料电池控制建模

对PEMFC非线性复杂被控对象,提出了一种在线辨识模糊预测算法,用模糊聚类和线性辨识方法在线建立PEMFC控制系统的T—S模糊预测模型,仿真实验结果表明了该模糊辨识建模方法具有建模简单、模型精度高等优点,亦证明了该算法的有效性和优越性。研究结果对质子交换膜燃料电池控制系统的建模和控制具有一定的实用价值。     

蛇形流场结构质子交换膜燃料电池的性能研究

建立包括催化层、扩散层、质子膜在内的三维质子交换膜燃料电池模型,通过Fluent软件模拟4种不同结构的蛇形流场,通过对速度、膜中水含量以及功率密度等分析得出蛇形流场的最优结构,并对最优结构进行参数优化。研究表明,4种不同蛇形流场结构中,Multi-serpentine II为最优,随着温度、压强的增加,这种流场结构的燃料电池呈现出良好的性能,从而为质子交换膜燃料电池双极板的设计提供依据。     

基于超平面原型聚类的水轮机调速系统模糊模型辨识

针对水轮机调速系统的辨识难题,提出了1种基于超平面原型聚类的T-S模糊模型辨识方法.基于局部模糊模型线性度的重要性,推导出1种基于超平面的模糊聚类算法.该算法以优化局部模型线性度为目标,进行模糊模型前提结构辨识,能使局部模型具有良好的线性度;它应用变尺度混沌优化方法搜索最优聚类结果,避免陷入局部极小;应用最小二乘法实现模糊模型结论参数辨识.以某水电厂水轮机调速系统为对象,采用该方法建立了T-S模糊模型,并对其进行了辨识和对比试验.结果表明:建立的T-S辨识模型具有较高的辨识精度及较强的泛化能力,提出的模型辨识方法有效可行.     

基于自适应协同引导的电池组性能衰退参数辨识北大核心CSCD

老化导致的电池组性能衰退与电池组电荷吞吐能力密切相关,对电池组性能衰退参数的快速精确辨识对提高电池组的服役寿命预测有效性至关重要。然而,既有的电池组性能衰退参数辨识方法仍然存在对大种群规模和高迭代次数的显著依赖,不利于提高电池组性能衰退模型的在线辨识更新适用性。针对此,本文提出了一种基于自适应协同引导的电池组性能衰退参数辨识方法。该方法首先基于自适应协同策略,综合考虑种群差异度和种群适应度的折中,实现种群个体对参数搜索空间的初期全局分布;在此基础上,基于精英引导策略,使种群中的个体在全局精英个体周围局部搜索,实现后期快速收敛至全局最优解。基于实测数据验证的统计结果表明,本文提出方法针对半经验容量衰退模型和内阻增量模型,在小种群规模下的参数辨识效率和精度均得到显著提升,分别在0.6 s和1.1 s内达到0.237%和0.37%的适应度终值,相对于蚁狮算法在辨识效率提高81.35%的同时适应度均值降低了3.8%,相对于灰狼算法在辨识效率提高17.14%的同时最终适应度均值降低了22.11%。     

基于仿射热模型的质子交换膜燃料电池电堆的热管理控制

质子交换膜燃料电池（PEMFC）电堆工作温度对电堆的性能和运行寿命有很大影响.为了实现质子交换膜燃料电池电堆温度的有效控制,根据电堆能量守恒原理建立了电堆动态热管理模型.由于该模型是具有参数不确定和易受外界干扰的非线性模型,为此,采用了线性二次型优化控制和李亚普诺夫函数的递推设计方法设计了具有强鲁棒性的自适应控制器两种控制算法对电堆温度进行控制,数字测试验证了该算法的有效性.图1参11     

基于可拓控制的质子交换膜燃料电池动态特性研究

针对质子交换膜燃料电池在启动和负载变化时,输出响应速度较慢和稳定性较差的问题,提出了一种基于可拓控制的质子交换膜燃料电池动态特性优化方案。基于质子交换膜燃料电池动态模型,结合可拓控制参数整定简单、响应快速、稳定性好的特点,以质子交换膜燃料电池输入端氢气进气量作为参考控制量,设计了可拓控制器,以提高质子交换膜燃料电池的响应速度及输出稳定性。将可拓控制与经典PID的控制效果进行了对比,结果表明,在燃料电池启动和负载变化过程中,可拓控制在燃料电池输出动态稳定性及克服燃料电池响应滞后等方面均优于PID的控制效果。     

An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models

Clean and renewable energy generation and supply has drawn much attention worldwide in recent years, the proton exchange membrane (PEM) fuel cells and solar cells are among the most popular technologies. Accurately modeling the PEM fuel cells as well as solar cells is critical in their applications, and this involves the identification and optimization of model parameters. This is however challenging due to the highly nonlinear and complex nature of the models. In particular for PEM fuel cells, the model has to be optimized under different operation conditions, thus making the solution space extremely complex. In this paper, an improved and simplified teaching-learning based optimization algorithm (STLBO) is proposed to identify and optimize parameters for these two types of cell models. This is achieved by introducing an elite strategy to improve the quality of population and a local search is employed to further enhance the performance of the global best solution. To improve the diversity of the local search a chaotic map is also introduced. Compared with the basic TLBO, the structure of the proposed algorithm is much simplified and the searching ability is significantly enhanced. The performance of the proposed STLBO is firstly tested and verified on two low dimension decomposable problems and twelve large scale benchmark functions, then on the parameter identification of PEM fuel cell as well as solar cell models. Intensive experimental simulations show that the proposed STLBO exhibits excellent performance in terms of the accuracy and speed, in comparison with those reported in the literature.     

Multiple model predictive control for a hybrid proton exchange membrane fuel cell system

This paper presents a hierarchical predictive control strategy to optimize both power utilization and oxygen control simultaneously for a hybrid proton exchange membrane fuel cell/ultracapacitor system. The control employs fuzzy clustering-based modeling, constrained model predictive control, and adaptive switching among multiple models. The strategy has three major advantages. First, by employing multiple piecewise linear models of the nonlinear system, we are able to use linear models in the model predictive control, which significantly simplifies implementation and can handle multiple constraints. Second, the control algorithm is able to perform global optimization for both the power allocation and oxygen control. As a result, we can achieve the optimization from the entire system viewpoint, and a good tradeoff between transient performance of the fuel cell and the ultracapacitor can be obtained. Third, models of the hybrid system are identified using real-world data from the hybrid fuel cell system, and models are updated online. Therefore, the modeling mismatch is minimized and high control accuracy is achieved. Study results demonstrate that the control strategy is able to appropriately split power between fuel cell and ultracapacitor, avoid oxygen starvation, and so enhance the transient performance and extend the operating life of the hybrid system.     

Cuckoo search algorithm with explosion operator for modeling proton exchange membrane fuel cells

Developing an accurate model is important to design and simulation of the fuel cell systems. In this work, we propose a cuckoo search algorithm with explosion operator (CS-EO) to estimate the model parameters of the proton exchange membrane fuel cells (PEMFCs). In CS-EO, the adaptive strategy of step size is adopted to enhance search ability. And the explosion operator is employed to avoid being trapped into local optima. Numerical experiments conduct on some benchmark functions indicate that CS-EO has better performance both in convergence and accuracy. The CS-EO is also applied to estimate the PEMFC model parameters and the satisfactory results reveal its effectiveness.     

Parameter estimation of fuel cell polarization curve using BMO algorithm

Polarization curves of proton exchange membrane fuel cells (PEMFCs) are affected by various parameters. The relative importance and effect of each parameter on the polarization curve is different. This paper studies estimation of parameters with the most influence on the electrochemical model. In order to evaluate the obtained results, the model accuracy is compared with that model in which all the parameters are estimated. Because PEMFCs parameter estimation is a complex optimization problem, a recently invented nature-inspired algorithm, bird mating optimizer (BMO), is proposed. For this aim, two real systems, the SR-12 Modular PEM Generator and the Ballard Mark V FC, are considered. The obtained results show that when the whole parameters are estimated, the accuracy of the model increases. Also, BMO algorithm yields better results than the other studied methods in terms of precision and robustness.     

A new parameter extraction method for accurate modeling of PEM fuel cells

In this paper, a new parameter extraction method for accurate modeling of proton exchange membrane (PEM) fuel cell systems is presented. The main difficulty in obtaining an accurate PEM fuel cell dynamical model is the lack of manufacturer information about the exact values of the parameters needed for the model. In order to obtain a realistic dynamic model of the PEM system, the electrochemical considerations of the system are incorporated into the model. Although many models have been reported in the literature, the parameter extraction issue has been neglected. However, model parameters must be precisely identified in order to obtain accurate simulation results. The main contribution of the present work is the application of the simulated annealing (SA) optimization algorithm as a method for identification of PEM fuel cell model parameter identification. The major advantage of SA is its ability to avoid becoming trapped in local minimum, as well as its flexibility and robustness. The parameter extraction and performance validation are carried out by comparing experimental and simulated results. The good agreement observed confirms the usefulness of the proposed extraction approach together with adopted PEM fuel cell model as an efficient tool to help design of power fuel cell power systems. Copyright © 2009 John Wiley & Sons, Ltd.     

Parameter identification and state-of-charge estimation for lithium-polymer battery cells using enhanced sunflower optimization algorithm

This paper is concerned with the investigation of accurate parameter identification method and state of charge (SoC) estimation for Lion Lithium battery. The proposed identification method is implemented using an accurate state space model obtained from electric equivalent circuit. The process of parameter identification is expressed as nonlinear optimization problem. An Enhanced sunflower optimization algorithm (ESFOA) is employed to solve such problem. The search space is managed by applying the reduction strategy. This strategy is accomplished with the sunflower optimization algorithm to enhance the solution quality. Three cases studied are considered as single and multi-objective frameworks. In these cases, battery voltage or SoC or combined between them as objective functions are optimized for the three cases studied. Numerical simulations as well as experimental implementation are executed on 40 Ah Kokam Li-Ion Battery to prove the capability of the proposed parameter identification method. The ability of the proposed ESFOA is accomplished with high accuracy is proven compared with Water-Cycle and Whale optimization algorithms for two driving cycle profiles. Added to that, high closeness is achieved compared with the experimental measurements for battery parameters and SoC. The solution quality improvement of the proposed ESFOA is noticed as it achieves the lowest the fitness function levels (in the range 60–90%) of the cases studied compared with the competitive optimization algorithms.     

Hybrid artificial bee colony algorithm for parameter estimation of proton exchange membrane fuel cell

The accurate electrochemical model plays an important role in design and analysis of hydrogen fuel cell systems. For the purpose of estimating parameters of the proton exchange membrane fuel cell (PEMFC) model, and inspired by the foraging behavior of bacteria and bees, a hybrid artificial bee colony (HABC) algorithm is proposed. The HABC uses an improved solution search equation that mimics the chemotactic effect of bacteria to enhance the local search ability. To avoid premature convergence and improve search accuracy, the adaptive Boltzmann selection scheme is adopted, which adjusts selective probabilities in different stages. Performance testing has been conducted on some typical benchmark functions. The results demonstrate that the HABC outperforms other methods (BIPOA, PSOPS and two improved GAs) in both convergence speed and accuracy. The proposed approach is applied to estimate the PEMFC model parameters and the satisfactory model predictive curves are obtained. More experimental results in different search ranges and validate strategies indicate that HABC is an efficient technique for the parameter estimation problem of PEMFC.     

A novel P systems based optimization algorithm for parameter estimation of proton exchange membrane fuel cell model

Accurate kinetic models are of great significance for the simulation and analysis for hydrogen fuel cells. The proton exchange membrane (PEM) fuel cell is a complex nonlinear, multi-variable system. The mathematical modeling of PEM fuel cell usually leads to nonlinear parameter estimation problems which often contain more than one minimum. In this paper, a novel bio-inspired P systems based optimization algorithm, named BIPOA, is proposed to solve PEM fuel cell model parameter estimation problems. In BIPOA, the nested membrane structure and new rules such as adaptive mutation rule, partial migration rule and autophagy rule are combined to improve the algorithm's global search capacities and convergence accuracy. Studies on some benchmark test functions indicate that the BIPOA outperforms the other two methods (PSOPS and GAs) in both convergence speed and accuracy. In addition, experimental results reveal that the model predictive outputs are in better agreement with the actual experimental data. Therefore, the BIPOA is a helpful and reliable technique for estimating the PEM fuel cell model parameters and is available to other complex parameter estimation problems of fuel cell models.     

Multi-objective optimization of gradient porosity of gas diffusion layer and operation parameters in PEMFC based on recombination optimization compromise strategy

Proton exchange membrane fuel cell (PEMFC) is one of the most promising power energy sources in the world, and its mechanism research has become the main starting point to improve the comprehensive performance of fuel cells. The gas diffusion layer (GDL) of a proton exchange membrane fuel cell has a significant impact on the overall performance of the cell as an important component in supporting the catalytic layer, collecting the current, conducting the gas and discharging the reaction product water. In this paper, a three-dimensional two-phase isothermal fuel cell model is established based on COMSOL, the gradient porosity of the GDL, thickness of the GDL, operating voltage and working pressure of proton exchange membrane fuel cell are analyzed, the consistency problem of fuel cell performance improvement and life extension that is easily overlooked in numerous studies is found. On this basis, a neural network proxy model is constructed through a large amount of data, and a multi-objective genetic optimization algorithm based on the compromise strategy of recombination optimization is proposed to optimize the uniformity of fuel cell power and oxygen molar concentration distribution, which improves the performance of the fuel cell by 1.45% compared with the power increase when it is not optimized. At the same time, the uniformity of oxygen distribution is improved 10.28%, which makes the oxygen distribution more uniform, prolongs the life of the fuel cell, and fills the gap in the optimization direction of the comprehensive performance of the fuel cell.