The temperature of proton exchange membrane fuel cell stack and the stoichiometric oxygen in cathode have relationship with the performance and life span of fuel cells closely. The thermal coefficients were taken as i...The temperature of proton exchange membrane fuel cell stack and the stoichiometric oxygen in cathode have relationship with the performance and life span of fuel cells closely. The thermal coefficients were taken as important factors affecting the temperature distribution of fuel cells and components. According to the experimental analysis, when the stoichiometric oxygen in cathode is greater than or equal to 1.8, the stack voltage loss is the least. A novel genetic algorithm was developed to identify and optimize the variables in dynamic thermal model of proton exchange membrane fuel cell stack, making the outputs of temperature model approximate to the actual temperature, and ensuring that the maximal error is less than 1 ℃. At the same time, the optimum region of stoichiometric oxygen is obtained, which is in the range of 1.8-2.2 and accords with the experimental analysis results. The simulation and experimental results show the effectiveness of the proposed algorithm.展开更多
Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index ...Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index of RP method for the three-dimensional finite element model(FEM) has been given.By taking the electric field of aluminum reduction cell(ARC) as the research object,the performance of two classical RP methods,which are Al-NASRA and NGUYEN partition(ANP) algorithm and the multi-level partition(MLP) method,has been analyzed and compared.The comparison results indicate a sound performance of ANP algorithm,but to large-scale models,the computing time of ANP algorithm increases notably.This is because the ANP algorithm determines only one node based on the minimum weight and just adds the elements connected to the node into the sub-region during each iteration.To obtain the satisfied speed and the precision,an improved dynamic self-adaptive ANP(DSA-ANP) algorithm has been proposed.With consideration of model scale,complexity and sub-RP stage,the improved algorithm adaptively determines the number of nodes and selects those nodes with small enough weight,and then dynamically adds these connected elements.The proposed algorithm has been applied to the finite element analysis(FEA) of the electric field simulation of ARC.Compared with the traditional ANP algorithm,the computational efficiency of the proposed algorithm has been shortened approximately from 260 s to 13 s.This proves the superiority of the improved algorithm on computing time performance.展开更多
为研究车用质子交换膜燃料电池的预测和健康管理问题,提出了一种以相对功率损耗率为健康指标、灰狼优化(grey wolf optimizer,GWO)算法与径向基(radial basis function,RBF)神经网络相结合的方法(GWO-RBF),对车用质子交换膜燃料电池的...为研究车用质子交换膜燃料电池的预测和健康管理问题,提出了一种以相对功率损耗率为健康指标、灰狼优化(grey wolf optimizer,GWO)算法与径向基(radial basis function,RBF)神经网络相结合的方法(GWO-RBF),对车用质子交换膜燃料电池的剩余使用寿命进行预测。首先,通过对初始时刻燃料电池极化曲线的分析,构建以相对功率损耗率为健康指标的计算方法,并采用灰色关联度分析方法验证其可行性。然后,应用GWO算法优化的RBF神经网络预测车用质子交换膜燃料电池的剩余使用寿命。最后,采用两组数据集对提出的方法进行了验证分析。结果表明:与其他方法相比,提出的基于GWO-RBF方法的平均绝对百分比误差、均方根误差最小,决定系数最大,相对误差小于1%。可见本文方法能够以较少的数据集、较高的精度预测车用质子交换膜燃料电池的剩余使用寿命。展开更多
基金Project (2003AA517020) supported by the National High-Technology Research Plan of China
文摘The temperature of proton exchange membrane fuel cell stack and the stoichiometric oxygen in cathode have relationship with the performance and life span of fuel cells closely. The thermal coefficients were taken as important factors affecting the temperature distribution of fuel cells and components. According to the experimental analysis, when the stoichiometric oxygen in cathode is greater than or equal to 1.8, the stack voltage loss is the least. A novel genetic algorithm was developed to identify and optimize the variables in dynamic thermal model of proton exchange membrane fuel cell stack, making the outputs of temperature model approximate to the actual temperature, and ensuring that the maximal error is less than 1 ℃. At the same time, the optimum region of stoichiometric oxygen is obtained, which is in the range of 1.8-2.2 and accords with the experimental analysis results. The simulation and experimental results show the effectiveness of the proposed algorithm.
基金Project(61273187)supported by the National Natural Science Foundation of ChinaProject(61321003)supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China
文摘Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index of RP method for the three-dimensional finite element model(FEM) has been given.By taking the electric field of aluminum reduction cell(ARC) as the research object,the performance of two classical RP methods,which are Al-NASRA and NGUYEN partition(ANP) algorithm and the multi-level partition(MLP) method,has been analyzed and compared.The comparison results indicate a sound performance of ANP algorithm,but to large-scale models,the computing time of ANP algorithm increases notably.This is because the ANP algorithm determines only one node based on the minimum weight and just adds the elements connected to the node into the sub-region during each iteration.To obtain the satisfied speed and the precision,an improved dynamic self-adaptive ANP(DSA-ANP) algorithm has been proposed.With consideration of model scale,complexity and sub-RP stage,the improved algorithm adaptively determines the number of nodes and selects those nodes with small enough weight,and then dynamically adds these connected elements.The proposed algorithm has been applied to the finite element analysis(FEA) of the electric field simulation of ARC.Compared with the traditional ANP algorithm,the computational efficiency of the proposed algorithm has been shortened approximately from 260 s to 13 s.This proves the superiority of the improved algorithm on computing time performance.
文摘为研究车用质子交换膜燃料电池的预测和健康管理问题,提出了一种以相对功率损耗率为健康指标、灰狼优化(grey wolf optimizer,GWO)算法与径向基(radial basis function,RBF)神经网络相结合的方法(GWO-RBF),对车用质子交换膜燃料电池的剩余使用寿命进行预测。首先,通过对初始时刻燃料电池极化曲线的分析,构建以相对功率损耗率为健康指标的计算方法,并采用灰色关联度分析方法验证其可行性。然后,应用GWO算法优化的RBF神经网络预测车用质子交换膜燃料电池的剩余使用寿命。最后,采用两组数据集对提出的方法进行了验证分析。结果表明:与其他方法相比,提出的基于GWO-RBF方法的平均绝对百分比误差、均方根误差最小,决定系数最大,相对误差小于1%。可见本文方法能够以较少的数据集、较高的精度预测车用质子交换膜燃料电池的剩余使用寿命。
