Reliability Reallocation for Fuel Cell Vehicles Based on Genetic Algorithm

A fuel cell vehicle (FCV) is a type of alternative energy vehicle that could help resolve the energy crisis, mitigate environmental problems, and contribute to sustainable development. Developing an FCV with high reliability is an important goal for automobile factories and research institutions. Other key factors required by FCVs include mass production and customer approval. An FCV is a complex mechanism composed of many subsystems. During the development of the overall vehicle, steps should be taken to ensure that every subsystem is reliable. However, such development must also consider costs, which must be kept as low as possible. To ensure the reliability of FCV while operating under conditions that demand minimal cost, a genetic algorithm is employed to reallocate the reliability of the overall vehicle system. First, the growth factor of the reliability–feasibility of each subsystem is determined according to the complexity, importance, and technological level of the FCV subsystems. The FCV cost model is then established on the basis of such parameters as subsystem cost, reliability–feasibility growth factor, initial reliability, limit reliability, and so on. A genetic algorithm is then used to compute for the reliability of FCV subsystems. The rationality of reliability reallocation is verified according to the subsystem importance coefficient. This method considers the benefits for both enterprises and customers by applying principles of engineering and conducting a reliability study. Copyright © 2014 John Wiley & Sons, Ltd.     

A discrete hybrid differential evolution algorithm for solving integer programming problems

Differential evolution (DE) is one of the most prominent new evolutionary algorithms for solving real-valued optimization problems. In this article, a discrete hybrid differential evolution algorithm is developed for solving global numerical optimization problems with discrete variables. Orthogonal crossover is combined with DE crossover to achieve crossover operation, and the simplified quadratic interpolation (SQI) method is employed to improve the algorithm's local search ability. A mixed truncation procedure is incorporated in the operations of DE mutation and SQI to ensure that the integer restriction is satisfied. Numerical experiments on 40 test problems including seventeen large-scale problems with up to 200 variables have demonstrated the applicability and efficiency of the proposed method.     

A new modified differential evolution algorithm scheme-based linear frequency modulation radar signal de-noising

The main intention of this study was to investigate the development of a new optimization technique based on the differential evolution (DE) algorithm, for the purpose of linear frequency modulation radar signal de-noising. As the standard DE algorithm is a fixed length optimizer, it is not suitable for solving signal de-noising problems that call for variability. A modified crossover scheme called rand-length crossover was designed to fit the proposed variable-length DE, and the new DE algorithm is referred to as the random variable-length crossover differential evolution (rvlx-DE) algorithm. The measurement results demonstrate a highly efficient capability for target detection in terms of frequency response and peak forming that was isolated from noise distortion. The modified method showed significant improvements in performance over traditional de-noising techniques.     

基于差分进化的时变信道最大似然估计算法

研究了隧道环境下的通信信道估计。针对隧道环境的地铁列车与轨旁设备之间无线通信中无线传输信道快速变化的特点,提出了一种采用元胞差分进化(DE)方法实时获取时变信道的有效信道长度的新型最大似然(ML)信道估计算法——DE-ML算法。仿真结果表明该算法在使用较少导频信息的情况下,通过差分进化方法有效估计跟踪有效信道长度,其估计性能优于最小二乘(LS)、线性最小均方误差(LMMSE)、传统ML等经典信道估计算法。该算法能在提高系统传输效率的同时显著提高算法的估计精度,尤其在高速移动情况下也具有了非常良好的性能。     

直接乙醇燃料电池阳极电催化剂的研究进展

介绍了直接乙醇燃料电池(DEFC)具有无毒,来源丰富的优点,分析了DEFC在Pt上的电催化氧化机理,讨论了DEFC的阳极电催化剂的重要作用;探讨了具有高电催化活性的新型Pt基催化剂、新型非贵金属催化剂、新型催化剂载体、新型的催化剂制备方法等的研究现状;指明了阳极催化剂将是今后DEFC研究和发展的重要方向之一。     

Optimization of the p-xylene oxidation process by a multi-objective differential evolution algorithm with adaptive parameters co-derived with the population-based incremental learning algorithm

Different operating conditions of p-xylene oxidation have different influences on the product, purified terephthalic acid. It is necessary to obtain the optimal combination of reaction conditions to ensure the quality of the products, cut down on consumption and increase revenues. A multi-objective differential evolution (MODE) algorithm co-evolved with the population-based incremental learning (PBIL) algorithm, called PBMODE, is proposed. The PBMODE algorithm was designed as a co-evolutionary system. Each individual has its own parameter individual, which is co-evolved by PBIL. PBIL uses statistical analysis to build a model based on the corresponding symbiotic individuals of the superior original individuals during the main evolutionary process. The results of simulations and statistical analysis indicate that the overall performance of the PBMODE algorithm is better than that of the compared algorithms and it can be used to optimize the operating conditions of the p-xylene oxidation process effectively and efficiently.     

直接甲醇燃料电池用磺化聚醚醚酮质子交换膜

在回顾近年来直接甲醇燃料电池用磺化聚醚醚酮(SPEEK)质子交换膜的发展历程基础上,分别综述了制膜材料SPEEK的合成和SPEEK质子交换膜的制备研究进展,重点总结了SPEEK质子交换膜的电导率和阻醇性能及其稳定性的影响因素和影响规律,其中包括制膜材料和溶剂以及工艺、SPEEK的共混改性、SPEEK的填充改性或多层复合结构的影响,进而分析了高性能SPEEK质子交换膜的开发研究前景.     

Decomposition-based multi-objective differential evolution particle swarm optimization for the design of a tubular permanent magnet linear synchronous motor

This article proposes a decomposition-based multi-objective differential evolution particle swarm optimization (DMDEPSO) algorithm for the design of a tubular permanent magnet linear synchronous motor (TPMLSM) which takes into account multiple conflicting objectives. In the optimization process, the objectives are evaluated by an artificial neural network response surface (ANNRS), which is trained by the samples of the TPMSLM whose performances are calculated by finite element analysis (FEA). DMDEPSO which hybridizes differential evolution (DE) and particle swarm optimization (PSO) together, first decomposes the multi-objective optimization problem into a number of single-objective optimization subproblems, each of which is associated with a Pareto optimal solution, and then optimizes these subproblems simultaneously. PSO updates the position of each particle (solution) according to the best information about itself and its neighbourhood. If any particle stagnates continuously, DE relocates its position by using two different particles randomly selected from the whole swarm. Finally, based on the DMDEPSO, optimization is gradually carried out to maximize the thrust of TPMLSM and minimize the ripple, permanent magnet volume, and winding volume simultaneously. The result shows that the optimized TPMLSM meets or exceeds the performance requirements. In addition, comparisons with chosen algorithms illustrate the effectiveness of DMDEPSO to find the Pareto optimal solutions for the TPMLSM optimization problem.     

Modified differential evolution algorithm for simple assembly line balancing with a limit on the number of machine types

This article proposes the differential evolution algorithm (DE) and the modified differential evolution algorithm (DE-C) to solve a simple assembly line balancing problem type 1 (SALBP-1) and SALBP-1 when the maximum number of machine types in a workstation is considered (SALBP-1M). The proposed algorithms are tested and compared with existing effective heuristics using various sets of test instances found in the literature. The computational results show that the proposed heuristics is one of the best methods, compared with the other approaches.     

A hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search for job shop scheduling problems

Job shop scheduling problem (JSSP) is a typical NP-hard problem. In order to improve the solving efficiency for JSSP, a hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search is proposed in this paper, which combines the merits of Estimation of distribution algorithm and Differential evolution (DE). Meanwhile, to strengthen the searching ability of the proposed algorithm, a chaotic strategy is introduced to update the parameters of DE. Two mutation operators are adopted. A neighbourhood search (NS) algorithm based on blocks on critical path is used to further improve the solution quality. Finally, the parametric sensitivity of the proposed algorithm has been analysed based on the Taguchi method of design of experiment. The proposed algorithm was tested through a set of typical benchmark problems of JSSP. The results demonstrated the effectiveness of the proposed algorithm for solving JSSP.     

运用改进的遗传算法进行框架结构损伤检测

运用一种改进的遗传算法来进行结构的损伤检测研究。该方法在传统遗传算法的变异算子里引入了一种被称为零变异率因子的参数，使得种群中时刻保持一定数量的零值元素，即相当于用结构的损伤只是发生在局部这个信息约束了传统的遗传算法，从而使得检测的结果更加准确。通过对某一框架模型试验数据的损伤检测研究，证明了该方法应用于实际工程结构的可行性，同时阐明了实际应用中的若干重要问题。     

A calibration framework for the microparameters of the DEM model using the improved PSO algorithm

The discrete element method (DEM) is commonly used for simulating the mechanical characteristics of rock materials; however, constructing a DEM model requires the specification of a number of microparameters. In this paper, to obtain the microparameters of the DEM model, the improved particle swarm optimization (PSO) calibration method was presented. Based on numerical simulation examples, the new approach is considered valid for calibrating the microparameters of the DEM model. Moreover, it is concluded that different sets of microparameters can be determined when few macroparameters are used, which indicates that the empirical formula between microparameters and macroparameters is not reliable. From the analysis of the numerical simulation results, it is suggested that more macroparameters should be used to calibrate the microparameters of the DEM model, and the corresponding numerical simulation results could be more reliable; otherwise, the generated numerical model may not accurately simulate the mechanical characteristics of rock materials.     

Preparation of the acidic PVA/MMT nanocomposite polymer membrane for the direct methanol fuel cell (DMFC)

A novel nanocomposite polymer electrolyte membrane composed of PVA polymer matrix and nanosized Montmorillonite (MMT) filler, was prepared by a solution casting method. The characteristic properties of the PVA/MMT nanocomposite polymer membrane were investigated using thermal gravimetric analysis (TGA), differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), atomic force microscopy (AFM), micro-Raman spectroscopy, and the AC impedance method. The PVA polymer directly blended with nanosized MMT filler (2-20 wt.%) showed good ionic conductivity, thermal, and mechanical properties. The highest ionic conductivity value for the acidic PVA/10 wt.%MMT nanocomposite polymer membrane was around 0.0368 S cm^− 1 at 30 °C. The methanol permeability (P) value was 3-4 × 10^− 6 cm² s^− 1. It was revealed that the addition of nanosized MMT fillers into the PVA matrix could markedly improve the electrochemical properties of the PVA/MMT nanocomposite membrane. In fact, the PVA/MMT nanocomposite polymer membrane appears to be a good candidate for the DMFC applications.     

A domain decomposition method for a two‐phase transport model of polymer electrolyte fuel cell containing micro‐porous layer

In this paper, a nonlinear Dirichlet–Robin iteration‐by‐subdomain domain decomposition method is studied for a multidimensional, multiphysics, and multiphase model of polymer electrolyte fuel cell (PEFC) containing micro‐porous layer (MPL). Across the interface of gas diffusion layer and MPL in PEFC, it is well known that the capillary pressure is continuous, whereas liquid saturation is discontinuous, by which the liquid‐water removal in the porous electrodes can be significantly enhanced. We design a type of non‐overlapping domain decomposition method to deal with water transport in such multi‐layer diffusion media, where Kirchhoff transformation and its inverse techniques are employed to conquer the discontinuous and degenerate water diffusivity in the coexisting single‐phase and two‐phase regions. In addition, the conservation equations of mass, momentum, charge, and hydrogen and oxygen transport are also solved by the combined finite element–upwind finite volume method (FEM/FVM) to overcome the dominated convection effect in gas channels. Numerical simulations demonstrate that the presented techniques are effective in obtaining a fast and convergent nonlinear iteration for such a 3D PEFC model within around 50 steps, in contrast with the oscillatory and nonconvergent iteration conducted by standard FEM/FVM. A series of numerical convergence tests are also carried out to verify the efficiency and accuracy of the present numerical techniques. Copyright © 2012 John Wiley & Sons, Ltd.     

Syngas production via coal char-CO2 fluidized bed gasification and the effect on the performance of LSCFN//LSGM//LSCFN solid oxide fuel cell

Fluidized bed reactor is widely used in coal char-CO_2 gasification. In this work, the production of syngas by using a fluidized bed gasification technique was first investigated and then the effect of the produced syngas on the performance of the solid oxide fuel cell with a configuration of La_(0.4)Sr_(0.6) Co_(0.2)Fe_(0.7)Nb_(0.1)O_(3-δ)//La_(0.8)Sr_(0.2)Ga_(0.83)Mg_(0.17)O_(3-δ)//La_(0.4)Sr_(0.6) Co_(0.2)Fe_(0.7)Nb_(0.1)O_(3-δ)(LSCFN//LSGM//LSCFN)was studied. During the syngas production, we found that the volume fraction of CO increased with the increment of gasification temperature, and it reached a maximum value of 88.8%, corresponding to a composition of 0.76% H_2, 88.8% CO, and 10.44% CO_2, when the ratio of oxygen mass flow rate to that of coal char(MO2/Mchar) increased to 0.29. In the following utilization of the produced syngas in solid oxide fuel cells, it was found that the increasing CO volume fraction in the syngas results in a gradual increase of the peak power density of the LSCFN//LSGM//LSCFN cell. The maximum peak power density of 410 m W/cm~2 was achieved for the syngas produced at 0.29 of M_(O2)/M_(char). In the stability test, the cell voltage decreased by 4% at a constant current density of 0.475 A/cm~2 after 54 h when fueled with the syngas with the composition of 0.76% H2, 88.8% CO, and 10.44% CO_2.It reveals that a carbon deposition with the content of 13.66% in the anode is attributed to the cell performance degradation.