质子交换膜燃料电池及其应用前景

介绍了PEMFC的结构和工作原理的，重点讨论了影响PEMFC性能的各种因素，回顾了国内外PEMFC的研究进展情况，并分析了PEMFC的主要应用领域，最后指出了PEMFC今后的研究方向。     

基于光纤光栅传感器的PEMFC温度监测技术研究

为实现实时原位监测PEMFC的内部温度,利用光纤光栅传感技术,将光纤光栅传感器密封埋入PEMFC阴极流场板,同步在线测量不同工况条件下电池内部温度的变化。结果表明,光纤光栅传感器能实现对PEMFC的多点内温度场实时原位监测,随着电流密度阶跃的增大,PEMFC内部温度也呈现阶跃变化,且阴极大流量供气时有利于降低PEMFC内部的温度。该方法可为PEMFC内部状态参数的原位监测提供技术参考和实施方案。     

船体简谐振动下PEMFC气体扩散-电化学反应的动态响应

针对质子交换膜燃料电池（PEMFC）船舶适用性问题，建立船体简谐振动条件下三维单流道PEMFC计算模型，并以计算流体力学为主要研究方法，仿真研究PEMFC在船体简谐振动情况下的动态性能变化，并通过组分运输过程以及电化学反应对振动条件下PEMFC的动态响应进行内在机制解析。结果表明：简谐振动下PEMFC在经过初始阶段响应后，呈周期性输出规律；船体振动会显著影响PEMFC内部组分传热传质，导致电化学反应无法正常进行，PEMFC性能出现衰变趋势，甚至造成电池饥饿现象；当振幅不变时，振动频率的增大会加剧该现象。     

质子交换膜燃料电池可靠性分析

可靠性是质子交换膜燃料电池(PEMFC)的重要指标，文中定性分析了PEMFC组成元件、装配工艺和工作过程的可靠性。提出了提高PEMFC可靠性的措施和可靠性的设计原则。     

质子交换膜燃料电池(PEMFC)的建模方法研究

质子交换膜燃料电池（PEMFC）是21世纪最有生命力的发电技术之一。参考大量文献，总结出PEMFC建模的基本方法。从电化学、液体动力学角度出发，全面地分析三维PEMFC数学模型，其具普遍意义。并阐明遗传算法、神经模糊控制技术在PEMFC控制方面的应用。     

新型拓展流道PEMFC传质模拟与性能研究

该文提出一种带有拓展区域的新型PEMFC流道，拓展区域长度分别设计为1、2和4 mm。采用COMSOL软件建立三维等温稳态模型并进行数值计算。结果表明：新型拓展流道PEMFC性能均优于传统直流道PEMFC，其最佳拓展长度为2 mm。在高电流密度下，拓展流道使氧气分布更加均匀，提升水的去除能力。当取最佳拓展长度时，增加拓展区域数量能进一步提升燃料电池性能，与传统直流道相比，双拓展区域的流道使PEMFC峰值功率密度提高了18.44%。     

PEMFC的Elman神经网络建模与模糊神经网络控制

从质子交换膜燃料电池（PEMFC）实际应用的角度出发，采用Elman动态神经网络对PEMFC系统进行建模，以实验中采样到的PEMFC系统的工作温度输入输出数据训练网络，并采用动态反向传播学习算法根据误差不断调整网络参数直至达到要求精度。设计了一种适应模糊神经网络控制器，根据经验确定了初始隶属度函数和模糊规则，并采用自适应学习算法不断调整隶属度函数与模糊规则参数，使控制系统获得理想的输出。仿真实验以Elman神经网络模型为参考模型，使用自适应神经网络控制算法取得了较好的控制效果。总之，所设计的控制系统适合于控制PEMFC这样一类复杂非线性系统。     

质子交换膜燃料电池

因其具有独特的优点，质子交换膜燃料电池（PEMFC）的市场前景很好，国际上已经形成了一股研究开发热潮。电催化剂、质子交换膜、双极板、燃料、水管理、热管理是质子交换膜燃料电池的关键技术。文章介绍了PEMFC的特点及开发应用状况，综述了PEMFC的研究进展。     

外界供给参数对PEMFC输出性能及水热平衡的影响

为了改善质子交换膜燃料电池(PEMFC)内部的水热平衡,从而进一步改善PEMFC的输出性能,文章建立了PEMFC的三维模型,通过改变PEMFC的外界供给参数(进气速度、加湿率以及冷却水流速),应用COMSOL模拟仿真得到了PEMFC的极化曲线和功率曲线、流道和气体扩散层(GDL)的水浓度分布情况,以及冷却水流速对PEMFC温度的影响。研究结果表明:随着进气速度和加湿率的逐渐增加,PEMFC的输出性能均逐渐提升,但是,过高的加湿率可能导致电极水淹;随着冷却水流速的增加,PEMFC温度加速下降,膜内温度分布变得更均匀。     

不同型式流场的PEMFC内部传质分析

对采用不同型式流场的PEMFC进行建模,并用控制容积法对控制方程进行离散,求解得到PEMFC内部各物理量的分布以及综合水拖带系数、质子交换膜平均电导率等。分析了采用交趾型流场和常规流场时PEMFC的内部传质以及阴极性能、电池性能和膜性能,结果认为采用交趾型流场时,PEMFC阴极性能高于采用常规流场的PEMFC阴极性能,但质子交换膜的平均电导率低于采用常规流场时。在没有液态水产生时常规流场PEMFC性能高于交趾型流场PEMFC。     

Road testing of a three-wheeler driven by a 5 kW PEM fuel cell in the absence and presence of batteries

The road testing and demonstration of a three-wheeler vehicle driven by a 5 kW proton exchange membrane fuel cell (PEMFC) was carried out in the absence and presence of lead acid batteries. Prior to integrating the PEMFC module and batteries in the three-wheeler, they were tested and demonstrated separately. The PEMFC module had a very fast response as the load was manually or, especially, automatically changed and it could supply a continuous power when the reactant was supplied continuously. In contrast, the 5 kW lead acid batteries alone could supply power for no longer than 300 s. In the presence of both the PEMFC module and batteries, when the drawing power was in the range of the PEMFC module capacity the propulsion motor gained its energy from the PEMFC module only, whilst the stack power output at all conditions was greater than the setting power of approximately 400 W. After integrating the PEMFC module and batteries into the three-wheeler, both energy sources were found to power the vehicle effectively. The motor power as well as the stack power changed as a linear proportion to the throttle. The motor consumed more power in case of high speed driving, take off or hill climbing, while it used only 0.354 kW in the absence of throttle. The hybrid system can achieve a maximum speed in this three-wheeler of around 24.9 km/h with a hydrogen consumption of 11 g H₂/km (71 g H₂/kWh) and an operating cost of 1.99 USD/km. The thermodynamic efficiency of the vehicle was 42.9%.     

Speed controlling of the PEM fuel cell powered BLDC motor with FOPI optimized by MSA

In this study, Brushless DC (BLDC) motor, which is commonly used as a drive element in the unmanned aerial vehicle (UAV), electric vehicles, and mobile robots today, is powered by hydrogen technologies as environmentally friendly and controlled by a fractional-order PI (FOPI) controller structure. Proton Exchange Membrane (PEM) electrolyzer, PEM Fuel Cell (PEMFC), storage tank, BLDC motor, and motor driver system are modeled and integrated into the Simulink environment in MATLAB. PEM electrolyzer that is energized from the AC grid via the AC/DC converter generates the hydrogen. This generated hydrogen is stored in the storage tank and used by PEMFC to energize to the BLDC motor. The model of the BLDC motor is controlled by using a closed-loop FOPI controller for the variable speed and torque reference values. Parameters of the FOPI are determined by Moth Swarm Algorithm (MSA) optimization method. It is observed from the results that the PEMFC powered FOPI controlled BLDC motor operates stably at high performance for different speed and torque values as expected from the modern drive systems. Furthermore, it is seen that the required energy for the BLDC motor is provided by the PEMFC-PEM electrolyzer system without interruption and the FOPI controlled BLDC motor successfully follows the reference speed values for the different torque values.     

A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction

Prognostics and health management of proton exchange membrane fuel cell (PEMFC) systems have driven increasing research attention in recent years as the durability of PEMFC stack remains as a technical barrier for its large-scale commercialization. To monitor the health state during PEMFC operation, digital twin (DT), as a smart manufacturing technique, is applied in this paper to establish an ensemble remaining useful life prediction system. A data-driven DT is constructed to integrate the physical knowledge of the system and a deep transfer learning model based on stacked denoising autoencoder is used to update the DT with online measurement. A case study with experimental PEMFC degradation data is presented where the proposed data-driven DT prognostics method has applied and reached a high prediction accuracy. Furthermore, the predicted results are proved to be less affected even with limited measurement data.     

Performance investigation on a coaxial-nozzle ejector for PEMFC hydrogen recirculation system

The ejector driven by the high-pressure gas potential energy from the hydrogen storage tank can reliably recirculate the unconsumed hydrogen in the proton exchange membrane fuel cell (PEMFC) system. However, the fixed-geometry ejector cannot maintain consistently high performance among the whole power output range in the PEMFC system due to its shortage of limited operating range. In this paper, a coaxial two-nozzle ejector, satisfying the requirements of the PEMFC system under different power outputs, is developed for hydrogen recirculation. The proposed ejector is investigated numerically based on an experimentally verified simulation model to reveal the flow distribution and predict its performance. The simulation results show that the proposed ejector can work in a wide power range of 17.90–84.00 kW within a suitable supply hydrogen pressure range of 4–7 bar. More importantly, the ejector can not only maintain a recirculation ratio above 0.9 in the wide output power range but a high recirculation ratio greater than 2.0 in the low power output. The proposed ejector broadens the working range of a single ejector used in the PEMFC system, which significantly promotes the development of fuel cell being widely adopted in automobiles.     

Modeling and thermal management of proton exchange membrane fuel cell for fuel cell/battery hybrid automotive vehicle

The proton exchange membrane fuel cell (PEMFC) stack is a key component in the fuel cell/battery hybrid vehicle. Thermal management and optimized control of the PEMFC under real driving cycle remains a challenging issue. This paper presents a new hybrid vehicle model, including simulations of diver behavior, vehicle dynamic, vehicle control unit, energy control unit, PEMFC stack, cooling system, battery, DC/DC converter, and motor. The stack model had been validated against experimental results. The aim is to model and analyze the characteristics of the 30 kW PEMFC stack regulated by its cooling system under actual driving conditions. Under actual driving cycles (0–65 kW/h), 33%–50% of the total energy becomes stack heat; the heat dissipation requirements of the PEMFC stack are high and increase at high speed and acceleration. A PID control is proposed; the cooling water flow rate is adjusted; the control succeeded in stabilizing the stack temperature at 350 K at actual driving conditions. Constant and relative lower inlet cooling water temperature (340 K) improves the regulation ability of the PID control. The hybrid vehicle model can provide a theoretical basis for the thermal management of the PEMFC stack in complex vehicle driving conditions.     

Design and control of a PEMFC powered electric wheelchair

This paper proposes the design and control of a fuel-cell powered wheelchair. Electric wheelchairs can improve moving ability for people with walking problems. However, their traveling distances are limited by the capacity of their batteries. We designed a fuel-cell powered electric wheelchair that can be continuously operated, thereby extending the moving range. The system consisted of three subsystems: a commercial electric wheelchair, a proton exchange membrane fuel cell (PEMFC), and two secondary battery sets. The study was carried out in three parts, investigating the fuel-cell control, power management, and system integration. First, we designed multivariable robust controllers for a 500 W PEMFC system to charge the battery sets by constant voltage/current. Second, we designed a serial power management system, where the wheelchair motors were directly driven by the secondary battery sets, which in turn were charged by the PEMFC when their capacities dropped below a certain level. Lastly, we integrated the three subsystems and verified the system performance by experiments. The results confirmed the effectiveness of the PEMFC system as a way to extend the traveling distance of a motorized wheelchair.     

The study on the power management system in a fuel cell hybrid vehicle

This paper presents a model of a hybrid electric vehicle, based on a primary proton exchange membrane fuel cell (PEMFC) and an auxiliary Li-ion battery, and its dynamics and overall performance. The power voltage from the fuel cell is regulated by a DC/DC converter before integrating with the Li-ion battery, which provides energy to the drive motor. The driving force for propelling the wheels comes from a permanent magnet synchronous motor (PMSM); where the power passes through the transmission, shaft, and the differential.     

A novel long short-term memory networks-based data-driven prognostic strategy for proton exchange membrane fuel cells

Proton exchange membrane fuel cell (PEMFC) long-term prognostic facilitates reducing the time/cost of the durability tests and is a critical starting point for control/maintenance suggestions. Long short-term memory (LSTM) recurrent neural networks have excellent time series processing capabilities and are proved to be useful for the short-term prognostic of PEMFC. However, LSTM prognostic models usually suffer from accumulated errors and model recognition uncertainties, which make it difficult to break the historical degradation data limitations, resulting in unsatisfactory long-term prediction performance. To tackle the problem, this paper proposes a novel model named navigation sequence driven LSTM (NSD-LSTM) for long-term prognostic. In the strategy, a navigation sequence is firstly generated by using an autoregressive integrated moving average model with exogenous variables. The sequence is then fed iteratively into LSTM in the implementation stage to achieve long-term perdition. The proposed strategy is evaluated using the aging experimental data of two types of PEMFC under different operating conditions. The long-term prognostic performance of the proposed model and other two state-of-the-art prognostic models, namely, nonlinear autoregressive exogenous and echo state network, are evaluated through comparison experiments. The simulation and experimental results show that the proposed prognostic strategy has better long-term degradation trend prediction consistency and remaining useful life estimation robustness.     

Aging prognosis model of proton exchange membrane fuel cell in different operating conditions

The aging prognosis model of Proton Exchange Membrane Fuel Cell (PEMFC) can predict the aging state of PEMFC to develop an effective prognostic maintenance plan. This paper proposes an aging prognosis model of PEMFC in different operating conditions based on the Backpropagation (BP) neural network and evolutionary algorithm. The influence of PEMFC current, hydrogen pressure, temperature, and relative humidity on the aging of PEMFC can be considered by the proposed method. Firstly, the aging prognosis model of PEMFC is built by the BP neural network. Then, the evolutionary algorithm including Mind Evolutionary Algorithm (MEA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) is used to optimize the parameters of the established aging prognosis model of PEMFC. Finally, the accuracy of the proposed aging prognosis model is validated by 3 PEMFC aging experiments in different operating conditions. The results show that MEA, GA, and PSO can greatly improve the accuracy of the aging prognosis model of PEMFC. The MEA improves the accuracy by 10 times, while the computing time increases by 0.085s. The proposed MEA-BP that has a very short computing time can be applied to online applications.