空冷型PEMFC电源系统热耦合实验研究

空冷型质子交换膜燃料电池(PEMFC)电源系统中燃料电池系统和金属储氢器的热耦合管理对系统会产生重要的影响。本文通过实验分别研究将金属储氢器前置和后置这两种与燃料电池系统不同的耦合方式对燃料电池输出性能、单电池电压的均衡性以及风扇功耗的影响。结果表明,这两种不同的耦合方式对燃料电池输出性能、单电池电压的均衡性影响很小,但是对风扇功耗的影响比较明显。这主要是由于储氢器前置时空气先经过储氢器表面冷却再进入电堆,这有利于减少电堆散热所需的空气流量,从而降低风扇功耗。因此储氢器前置有利于降低系统辅助功耗,提高系统效率。     

一种新型进气方式对燃料电池水管理效果的影响北大核心CSCD

为了同时解决燃料电池水管理中的干涸(dehydration)和水淹(flooding)问题,提出了一种主副流道分流式的阴极进气加湿方式,并用数值模拟分析了主副流道合流节点位置变化对燃料电池性能的影响,同时与应用非分流式进气加湿方式的燃料电池性能进行了对比。结果表明,分流式阴极进气方式可以同时降低流道内的干涸和水淹程度,从而提高电池性能;当入口处空气摩尔流量固定时,随着阴极流道上主副流道合流节点沿气体流动方向移动,电池输出电压先上升,达到最大值后,逐渐下降。     

大功率燃料电池建模与电压一致性分析

随着燃料电池堆朝着大功率发展,其工作时单体间的不一致性更加明显,长时间处于恶劣工作条件的单体寿命会明显短于其他单体,并导致电池堆的寿命大幅缩减。为探究不同运行参数对大功率燃料电池单体工作性能一致性的影响规律,首先,建立了包括流体网络模型、燃料电池电压模型和燃料电池热阻模型三个部分的110 kW大功率燃料电池模型。其次,开展燃料电池稳态试验,对所建立的燃料电池模型进行试验验证,仿真与试验结果误差在5%以内。最后,基于模型仿真,以电压最大偏差率为评价指标,分别探究工作电流、冷却水流量和冷却水进口温度三个运行参数对燃料电池电压一致性的影响规律。仿真结果表明,工作电流对燃料电池单体电压一致性的影响程度更大,其次是冷却水进口温度,最后是冷却水流量。本研究有助于大功率燃料电池发动机的结构优化设计以及为热管理控制策略开发提供指导。     

基于PEMFC分布式发电系统的动态特性分析

分析了质子交换膜燃料电池(PEMFC)的机理模型,在此基础上运用MATLAB的Simulink仿真工具,建立了PEMFC发电系统带负载模型。通过仿真,分析了负载对PEMFC电堆的各项动态特性(燃料的流量、效率、输出电压等)的影响,以及DC/DC、负载端的电压响应。仿真结果中负载电压呈三相交流正弦波形,表明搭建的整个PEMFC发电系统是基本正确的,为实现PEMFC并网的实时分析和动态优化提供了理论依据和参考方法。     

固体氧化物燃料电池的神经模糊控制策略研究

固体氧化物燃料电池(Solid Oxide Fuel Cell,SOFC)具有多输入多输出、强耦合的特点,为了使其输出电压稳定设计了高效控制器,采用神经模糊控制方法对其输出电压进行控制。通过机理分析和实验数据拟合方法分别建立SOFC的机理模型和神经网络模型,在此基础上采用模糊控制策略对SOFC的输出电压进行控制,并应用神经模糊控制方法进一步提高了控制精度。通过MATLAB/Simulink仿真实验发现,SOFC神经网络模型得到的预测电压与实际电压之间的误差小于0.008 V,较其机理模型更加准确,所提出的控制策略能有效控制SOFC的输出电压。     

质子交换膜燃料电池双极板的设计与模拟分析

质子交换膜燃料电池是直接将化学能转换为电能的装置,双极板上的流道结构对燃料电池的工作性能具有较大的影响。根据应用要求设计了具有平行流道、蛇形流道及希尔伯特分形流道的双极板结构,模拟计算了氢气在不同类型的流道和气体扩散层中的分布状态,分析了燃料电池的输出电流密度和功率密度随电极间电压的变化特点,比较了不同的流道结构对燃料电池输出电流密度的影响,以及不同的工作温度及气体压强的情况下,燃料电池输出电流密度随温度及压强的变化规律。     

空冷自增湿燃料电池最优控制方法研究

通过对影响质子交换膜燃料电池(PEMFC)输出性能因素的分析,得出PEMFC电堆工作温度、电堆输出电流是影响PEMFC输出性能的主要因素。在输出电流一定的情况下,电堆工作温度是影响PEMFC输出电压的主要因素。为实现对空冷自增湿PEMFC的最优控制,采用实验测试及数据拟合方法,得到PEMFC电堆最优温度与输出电流的函数关系式,通过控制PEMFC电堆工作在最优温度,以实现PEMFC输出电压的最优控制。实验测试表明,该控制方法简单实用、控制效果优越,可为空冷自增湿PEMFC的最优控制提供具有实用价值的控制方法。     

燃料电池功率调节系统的研究

燃料电池电压输出范围比较宽，电压比较低。针对该特点本文设计了DC/DC和DC/AC两级变换的功率调节系统(PCS)。其中DC/DC将燃料电池输出的低压直流电高频变换成高压直流电，变换器为电压单环控制。DC／AC逆变器采用基于电压电流瞬时值反馈的双闭环控制，将高压直流电逆变为正弦交流电。分析了整个功率调节系统的工作原理及逆变器电路参数对稳定性的影响。0．5KVA佯饥实验结果表明整个系统具有电压输入范围宽、变换效率高、输出波形THD小等优点。为开发高效、高功率密度的燃料电池电源系统提供技术基础。     

固体氧化物燃料电池电热冷联供总能系统的分析

提出了一个由固体氧化物燃料电池和吸收式制冷机组成的电热冷联供总能系统,应用MATLAB软件包对总能系统进行了模拟分析,得到了燃料电池的电流密度、燃料流量、输出功率等参数对总能系统的影响,为高温燃料电池电热冷联供总能系统的设计与优化提供参考依据。     

直接碳燃料电池性能研究

直接碳燃料电池(DCFC)勿需碳和氧气气化、重整,而直接通过电化学反应产生电能,效率可达80%,燃料的理论利用率可达100%,是一种高效、清洁的燃料电池.文章所介绍的组装DCFC单体电池,以石墨作阳极,不锈钢作阴极,加湿氧气作氧化剂,采用熔融氢氧化物作电解质,并掺入一定量的催化剂,该电池工作温度为500～700℃.对不同工作温度、不同电解质和不同氧气流量下DCFC的输出性能进行了试验研究.结果表明:随着工作温度的升高,电池输出性能有很大提高;KOH比NaOH的导电性好,电池运行更稳定,更有利于电池的输出;氧气流量为70mL/min,温度为650℃时,该电池的输出性能最佳,最大电流密度、功率密度分别为118mA/cm2和0.054 W/cm2,开路电压达到0.76 V.     

Proton exchange membrane fuel cell degradation prediction based on Adaptive Neuro-Fuzzy Inference Systems

This paper studies the prediction of the output voltage reduction caused by degradation during nominal operating condition of a PEM fuel cell stack. It proposes a methodology based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) which use as input the measures of the fuel cell output voltage during operation. The paper presents the architecture of the ANFIS and studies the selection of its parameters. As the output voltage cannot be represented as a periodical signal, the paper proposes to predict its temporal variation which is then used to construct the prediction of the output voltage. The paper also proposes to split this signal in two components: normal operation and external perturbations. The second component cannot be predicted and then it is not used to train the ANFIS. The performance of the prediction is evaluated on the output voltage of two fuel cells during a long term operation (1000 h). Validation results suggest that the proposed technique is well adapted to predict degradation in fuel cell systems.     

Neural network model for a commercial PEM fuel cell system

Performance prediction of a commercial proton exchange membrane (PEM) fuel cell system by using artificial neural networks (ANNs) is investigated. Two artificial neural networks including the back-propagation (BP) and radial basis function (RBF) networks are constructed, tested and compared. Experimental data as well as preprocess data are utilized to determine the accuracy and speed of several prediction algorithms. The performance of the BP network is investigated by varying error goals, number of neurons, number of layers and training algorithms. The prediction performance of RBF network is also presented. The simulation results have shown that both the BP and RBF networks can successfully predict the stack voltage and current of a commercial PEM fuel cell system. Speed and accuracy of the prediction algorithms are quite satisfactory for the real-time control of this particular application.     

Dynamic modeling and experimental validation for the electrical coupling in a 5-cell solid oxide fuel cell stack in the perspective of thermal coupling

The electrical coupling in a 5-cell solid oxide fuel cell (SOFC) stack is investigated in this research. The electrical characteristics tests of a single cell and the stack were performed in an electrical furnace. It was found that the single cell with the highest temperature does not give the highest output voltage in the stack test, which is different from the result that the output voltage increases with temperature in the single cell test. A physical interpretation for this phenomenon is given specifically from the standpoint of electrical coupling on the basis of thermal coupling between cells in the stack. Furthermore, a system level electrical coupling dynamic model is developed to characterize the electrical characteristics of the stack by considering the contact resistance between cells. In addition, the electrical coupling dynamic model is calibrated and validated based on the experimental data. The results demonstrate that the electrical coupling dynamic model can depict and predict accurately the electrical characteristics of SOFC stacks. The accurate electrical coupling dynamic model is important for the system level study of SOFCs, such as the optimization of stack structures and the design of peripheral control systems.     

Novel dynamic semiempirical proton exchange membrane fuel cell model incorporating component voltages

This paper introduces a novel dynamic semiempirical model for the proton exchange membrane fuel cell (PEMFC). The proposed model not only considers the stack output voltage but also provides valid waveforms of component voltages, such as the no‐load, activation, ohmic, and concentration voltages of the PEMFC stack system. Experiments under no‐load, ramping load, and dynamic load conditions are performed to obtain various voltage components. According to experimental results, model parameters are optimised using the lightning search algorithm by providing valid theoretical ranges of parameters to the lightning search algorithm code. In addition, the correlation between the vapour and water pressures of the PEMFC is obtained to model the component voltages. Finally, all component voltages and the stack output voltage are validated by using the experimental/theoretical waveforms mentioned in previous research. The proposed model is also compared with a recently developed semiempirical model of PEMFC through particle swarm optimisation. The proposed dynamic model may be used in future in‐depth studies on PEMFC behaviour and in dynamic applications for health monitoring and fault diagnosis.     

Water management fault diagnosis for proton-exchange membrane fuel cells based on deep learning methods

Water management failure is the most common fault in proton-exchange membrane fuel cells (PEMFCs), and it directly affects the durability and stability of fuel cells. This paper proposes a water fault diagnosis method based on 1DCNN-XGB. To promote its commercial applications, the cathode pressure drop, voltage, and current density were used as the characteristic diagnostic variables,which considered variations in the stack load and also facilitated hierarchical fault diagnosis. First, flooding and drying experiments under different current densities were simulated by changing the operating conditions of the stack, and the obtained experimental data were normalized to eliminate feature imbalances. Then, they were reconstructed into a 1D data set as the input of the model. Automatic feature extraction was performed by a 1DCNN, and the extracted feature maps were used as the input of the XGBoost classifier. Finally, the trained model was validated on the test set for fault diagnosis. The experimental results showed that the model accurately and efficiently distinguished normal and different degrees of two typical fault states (flooding and drying) of the stack with an overall accuracy of 98.10%. The comparative experiments revealed that the model was superior to the individual 1DCNN and XGBoost models, showing greater accuracy and better generalization ability.     

Study of cell voltage uniformity of proton exchange membrane fuel cell stack with an optimized artificial neural network model

The cell voltage uniformity of the proton exchange membrane fuel cell stack, which may consist of tens or hundreds of cells in series, plays a significant role in the stack's lifetime and performance. But it is challenging to predict the multi-cell voltages and the uniformity with a physics-based model due to complex stack geometry and huge computation efforts. In this work, we develop an artificial neural network model to estimate the steady-state cell voltage distributions of a 60 kW 140-cell stack. The optimized and well-trained model can efficiently reproduce the 140-cell voltages at different operating conditions with the error of less than 2 mV. The increased cathode gas pressure improves the cell voltage consistency and stack performance, while the voltage uniformity worsens with ascending load current. The efficient model prediction of cell voltages is beneficial for accurate evaluation of fuel cell performance, health state, and reliability.     

A novel online degradation model for proton exchange membrane fuel cell based on online transfer learning

A novel online degradation prediction model is proposed to prognosticate the future degradation trend (FDT) of proton exchange membrane fuel cell (PEMFC) stack in this paper. In order to overcome the fact that existing FDT prediction methods of PEMFC stack based on data-driven model rely on the assumption that the operating conditions of the training data and testing data need to be consistent, an end-to-end prediction algorithm based on the combination of transfer learning and transformer neural network, referred to as TLTNN, is proposed to predict the FDT of PEMFC stack. Besides, in order to demonstrate the effectiveness and superiority of the proposed method in prognostics tasks of PEMFC stack FDT, the prediction performance is validated on the PEMFC test system. The results show that the RMSE, MAE and MAPE values of the predicted degradation voltage are 0.00598 V, 0.004842 V and 0.1518%, respectively, which indicates that the proposed TLTNN strategy based on transfer online learning can be used to predict the degradation voltage of PEMFC stack and the superiority of the proposed method is better, thus solving the problem that the distribution of training and test data must be the same in traditional machine learning models.     

基于改进递归深度信念网络的CSP电站短期出力预测

以预测CSP电站短期出力为目的,首先引入自适应思想对递归深度信念网络的训练算法进行改进,并建立直接法向辐射的短期预测模型。随后提出一种结合静态模型的CSP电站短期出力预测方法。最后进行性能检验,验证了改进递归深度信念网络的可行性,以及CSP电站短期出力预测方法的有效性。研究结果表明：建立的改进递归深度信念网络可提升预测准确性和收敛速度;提出的CSP电站短期出力预测方法可较为准确地预测其短期出力情况。     

Modeling of the Ballard-Mark-V proton exchange membrane fuel cell with power converters for applications in autonomous underwater vehicles

This paper studies the transient response of the output voltages of a Ballard-Mark-V 35-cell 5 kW proton exchange membrane fuel cell (PEMFC) stack with power conversion for applications in autonomous underwater vehicles (AUVs) under load changes. Four types of pulse-width modulated (PWM) dc-dc power converters are employed to connect to the studied fuel cell in series for converting the unregulated fuel cell stack voltage into the desired voltage levels. The fuel cell model in this paper consists of the double-layer charging effect, gases diffusion in the electrodes, and the thermodynamic characteristic; PWM dc-dc converters are assumed to operate in continuous-conduction mode with a voltage-mode control compensator. The models of the study's fuel cell and PWM dc-dc converters have been implemented in a Matlab/SIMULINKTM environment. The results show that the output voltages of the studied PEMFC connected with PWM dc-dc converters during a load change are stable. Moreover, the model can predict the transient response of hydrogen/oxygen out flow rates and cathode and anode channel temperatures/pressures under sudden change in load current.