Adaptive inverse control of air supply flow for proton exchange membrane fuel cell systems

To prevent the oxygen starvation and improve the system output performance, an adaptive inverse control （AIC） strategy is developed to regulate the air supply flow of a proton exchange membrane fuel cell （PEMFC） system in this paper. The PEMFC stack and the air supply system including a compressor and a supply manifold are modeled for the purpose of performance analysis and controller design. A recurrent fuzzy neural network （RFNN） is utilized to identify the inverse model of the controlled system and generates a suitable control input during the abrupt step change of external disturbances. Compared with the PI controller, numerical simulations are performed to validate the effectiveness and advantages of the proposed AIC strategy.

Modeling of Fuel Elements Cycling System in Pebble Bed Reactor Based on Timed Places Control Petri Nets

Pebble bed reactors use cycling scheme of spherical fuel elements relying on fuel elements cycling system (FECS). The structure and control logic of FECS are very complex. Each control link has strict requirements on time and sequence. This increases the difficulties of description and analysis. In this paper, timed places control Petri nets (TPCPN) is applied for the modeling of FECS. On this basis the simulation of two important processes, namely uploading fuel elements into the core for the first time and emptying the core is finished by simulation software Arena. The results show that as TPCPN is able to describe different kinds of logic relationship and has time properties and control properties, it’s very suitable for the modeling and analysis of FECS.

Intelligent Control for Improvements in PEM Fuel Cell Flow Performance

The performance of fuel cells and the vehicle applications they are embedded into depends on a delicate balance of the correct temperature, humidity, reactant pressure, purity and flow rate. This paper successfully investigates the problem related to flow control with implementation on a single cell membrane electrode assembly （MEA）. This paper presents a systematic approach for performing system identification using recursive least squares identification to account for the non-linear parameters of the fuel cell. Then, it presents a fuzzy controller with a simplified rule base validated against real time results with the existing flow controller which calculates the flow required from the stoichiometry value.

Air flow control based on optimal oxygen excess ratio in fuel cells for vehicles

Air flow control is one of the most important control methods for maintaining the stability and reliability of a fuel cell system, which can avoid oxygen starvation or oxygen saturation. The oxygen excess ratio （OER） is often used to indicate the air flow condition. Based on a fuel cell system model for vehicles, OER performance was analyzed for different stack currents and temperatures in this paper, and the results show that the optimal OER was affected weakly by the stack temperature. In order to ensure the system working in optimal OER, a control scheme that includes an optimal OER regulator and a fuzzy control was proposed. According to the stack current, a reference value of air flow rate was obtained with the optimal OER regulator and then the air compressor motor voltage was controlled with the fuzzy controller to adjust the air flow rate provided by the air compressor. Simulation results show that the control method has good dynamic and static characteristics.

Embedded Software Control of a Hybrid Vehicle＇s ECU/FCM System

This paper describes a research project that uses embedded systems design principles to construct and simulate an Engine Control Unit （ECU） for a hybrid car. The ECU is designed to select a fuel type based on the stress level of the simulated engine. The primary goal of the project was to use a robotics kit, connected to sensors, to simulate a hybrid car under certain stress conditions such as hill climbing or full throttle. The project uses the LEGO~ Mindstorms~ NXT robotics kit combined with a Java-based firmware, a pressure sensor to simulate a gas pedal, and a tilt sensor to determine when the car is traveling uphill or downhill. The objective was to develop, through simulation, a framework for adjusting the ratios/proportions of fuel types and mixture under the stress conditions. The expected result was to establish a basis for determining the ideal/optimal fuel-mix-stress ratios on the hybrid car＇s performance. Using the NXT robotics kit abstracted the low level details of the embedded system design, which allowed a focus on the high level design details of the research. Also, using the NXJ Java-based firmware allowed the incorporation of object oriented design principles into the project. The paper outlines the evolution and the compromises made in the choice of hardware and software components, and describes the computations and methodologies used in the project.

DISOPE distributed model predictive control of cascade systems with network communication

A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation （DISOPE） was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.

Recent Progress in Plasma Control Studies on the Improvement of Plasma Performance in Heliotron J

Recent progress in plasma control studies on the improvement of plasma performance in Heliotron J is reviewed. The supersonic molecular beam injection （SMBI） fueling is successfully applied to Heliotron J plasma. A supersonic H2-beam is effectively injected to increase fueling efficiency and generate a peaked density profile. Local fueling with a short-pulsed SMBI can increase the core plasma density and avoid the degradation arising from edge cooling. Second harmonic electron cyclotron current drive （ECCD） experiments were conducted by launching a focused Gaussian beam with a parallel refractive index of -0.05 ≤ Nil 〈 0.6. Results show that the electron cyclotron （EC） driven current is determined not only by Nil but also by local magnetic field （B） structure where the EC power is deposited. Detailed analysis of the observed NI and B dependences is in progress with a ray-tracing simulation using the TRAVIS code. Fast ion velocity distribution was investigated using fast protons generated by ion cyclotron resonant frequency （ICRF） minority heating. For the standard configuration in Heliotron J, charge ex- change neutral particle analysis （CX-NPA） measurements show higher effective temperature of fast minority protons in the on-axis resonance case compared to that in the HFS （high field side） off-axis resonance case. However, the increase in bulk ion temperature in the HFS resonance case is larger than that in the on-axis resonance.

Performance analysis and fuzzy neural networks modeling of direct methanol fuel cell

This paper introduces the effects of cell operating temperature, methanol concentration and airflow rate, respectively, on the performance of direct methanol fuel cell （DMFC）. A novel method based on fuzzy neural networks identification technique is proposed to establish the performance model of DMFC. Three dynamic performance models of DMFC under the influences of cell operating temperature, methanol concentration, and airflow rate are identified and established separately. Simulation results show that modeling using fuzzy neural networks identification is satisfactory with high accuracy. It is applicable to DMFC control systems.

直升机发动机燃油控制机械备份的操纵特性

针对典型的采用自动控制+机械备份操纵的直升机发动机燃油控制系统,分析了不同控制模式下机械备份操纵的油门响应特性。依据发动机手册图示的燃油控制系统原理,推导了机械备份操纵的油门响应特性函数,并与试验数据进行了对比验证。机械备份操纵特性分析表明:手动控制模式下的机械备份操纵的油门响应与自动控制失效时锁定的位置相关;自动控制有效时进行的机械备份操纵是一种混合模式操纵,具有与手动控制模式不同的油门响应特性。

PEMFC空气系统流量和压力协同控制策略设计

为提高质子交换膜燃料电池(PEMFC)空气系统的动态响应性能,避免局部缺气而导致其输出性能降低等问题的出现,针对空气系统存在流量和压力耦合的问题,分别采用PID、前馈解耦控制和模型预测控制(MPC)对空气系统进行控制,通过仿真和台架测试,验证控制算法的有效性。结果表明,两种控制算法相比较PID均具有较好的控制效果,空气流量和空气压力均能快速跟随设定值,PEMFC发动机的输出性能平稳。

耦合太阳能的SOFC-MGT冷热电联供系统动态响应特性研究

为了研究输入侧能量波动对分布式系统的影响,建立了耦合太阳能的固体氧化物燃料电池(SOFC)-微型燃气轮机(MGT)的冷、热、电联产(CCHP)系统动态模型。分析了太阳能直射辐射波动对系统关键参数的影响规律,研究了外界电负荷波动对系统性能的影响。结果表明:与未耦合太阳能的CCHP系统相比,新系统利用太阳能预热空气,制冷量和供热量分别增加了17%和9%,同时燃料量减少了1%;槽式太阳能集热器(PTC)的加入使系统发电效率随电负荷变化更加平缓,同时使最大发电效率点右移,有利于增大系统功率。

基于模型预测框架的燃料电池汽车混合动力系统能量管理

混合动力系统作为燃料电池汽车的关键组成部分,系统的能耗与寿命优化是推动其进一步商业化发展的关键,而能量管理策略(EMS)在提高燃料电池汽车混合动力系统的效率和燃料经济性方面发挥着重要作用。为有效降低混合动力系统的内部损耗,进一步提高系统经济性,提出了一种基于模型预测框架的混合动力系统能耗优化能量管理控制方法。首先构建包含动力系统内部损耗与氢气消耗的目标函数,然后利用模型预测架构求解目标函数来控制燃料电池的输出电流,以实现合理的能量分配,最后在Matlab中进行仿真,验证所制定方法的合理性和有效性。与应用较为广泛的等效氢耗最小策略(ECMS)相比,结果表明所提方法可以显著降低动力系统的内部损耗和氢气消耗。此外,该策略还可以有效保持蓄电池的荷电状态(SOC),延长动力系统的使用寿命。

基于LabVIEW的燃料电池控制程序设计

燃料电池作为一种新兴的高效、安全、环保的动力源,逐渐在汽车、船舶等领域得到广泛应用。然而燃料电池结构复杂、所包含元器件多,因此要求燃料电池控制系统能够全面监测运行状态并能够对运行过程进行实时控制。该文针对现有燃料电池样机,基于LabVIEW软件搭建燃料电池控制系统,完成上下位机通信程序及软件界面设计。通过仿真调试,所设计的燃料电池控制程序可实现燃料电池的全面状态监测及实时控制,满足燃料电池的控制需要。

车用燃料电池系统控制策略综述

针对车用燃料电池系统控制策略,从系统结构、控制对象、控制目标、控制方法等方面总结了其研究现状和发展趋势。在系统结构设计方面,需要研发高性能关键部件,简化控制系统结构,降低控制难度。在控制对象方面,针对强耦合物理量的解耦控制有待深入研究。在控制目标方面,多目标优化控制策略将是未来的研究重点。在控制方法方面,复合控制策略和基于学习的智能控制策略将是未来的研究方向。

燃料电池氢气流量控制系统变负载匹配设计与仿真

针对车载质子交换膜燃料电池供氢系统负载工况多变时的氢气流量控制问题,提出一种基于比例减压阀与流量控制阀的氢气流量负载匹配控制系统架构与方法。建立了从高压储氢瓶至燃料电池堆的供氢系统整体模型,基于该模型设计了比例减压阀、流量控制阀与氢气循环泵的控制策略,并就某一型号电堆的变负载工况对系统模型与控制策略进行仿真验证。结果表明:电堆阳极压力可快速跟踪给定压力,响应时间约2~3s;氢气流量可适应负载变化需求,其瞬时超调量与压力响应速度成正相关;稳态时氢气利用率维持在95%以上,供氢过量比维持在1.27以上,供气氮含量维持在5%以下,满足电堆性能需求。

某型航空发动机主燃油控制系统频率特性研究

为研究某型航空发动机数控系统主燃油控制系统频率特性,基于力平衡方程和流量连续方程建立元件和系统的动态数学模型。在其稳态工作点附近采用小偏离线性化建立等压差模块、主燃油计量模块、停车控制模块和整个系统的传递函数,并通过Fluent流场仿真重点研究了关键元件的稳态液动力及影响因素。在此基础上建立了主燃油控制系统的Simulink仿真模型,研究了控制系统的频率特性和关键活门固有频率。结果表明:主燃油控制系统的频率特性与实际工程经验一致,稳定裕度较高;系统的固有频率与系统中固有频率最低的元件基本一致;当元件结构确定时,稳态液动力对固有频率的影响较大。研究结果为燃油控制系统频率域优化设计提供了理论支持。

基于LabVIEW的比例压力控制阀自动测试系统

根据某公司比例压力控制阀的出厂测试要求,本文设计了一套比例控制阀自动测试系统。该系统基于LabVIEW编程和PLC控制,可实现共轨管稳定的超高压供给,并自动实现比例压力控制阀的耐久测试、流量滞环测试及压力-流量测试,且操作简便,满足企业需求。

基于模糊控制的惰化反应器温度控制

为了抑制飞机耗氧型燃油惰化系统整体式反应器内飞温现象的发生,为提升工作效率,延长使用寿命,利用Modelica语言对整体式反应器建立双温度模型,开展动态仿真,并设计了逆流式冷却措施。根据反应器温度特性,制定了相对应的控制策略,建立模糊控制器,研究了不同工况下模糊控制器在逆流式反应器温度控制上的工作特性,以及冷却管道排布对反应通道工作效率的影响。研究结果表明:模糊控制对逆流式反应器温度控制稳定性较好,有效抑制了反应器尾部的飞温现象;相比无温控管道,单侧和双侧存在温控管道时,单根反应通道的平均反应速率分别提升了1倍和10倍,添加温度控制后,反应器整体燃油蒸气转化率从10%提升至99%。隶属函数方差σ^(2)对控制效果有明显的影响,方差σ^(2)越大,控制精度越低,响应速度越快,要根据不同进口状态选取合适的σ^(2),以获得最佳控制性能。

基于阴极再循环的燃料电池系统建模与动态性能分析

阴极再循环通过调节电堆进气中的氧分压和水蒸气分压特别适合解决车用燃料电池系统在低怠速工况下的缺水和高电势问题。建立了一个带阴极再循环功能的燃料电池非线性系统控制模型,使用阴极再循环泵将阴极出口的气体引回到空压机出口实现了进气加湿和氧分压调节。该模型可捕捉由阴极再循环带来的一系列动态响应。结果表明,阴极再循环可以极大地改善小电流密度下的进气湿度,随着阴极再循环比的增加,加湿动态响应加快;氧分压调节的动态响应较慢,但通过合理的阴极再循环策略仍能将小电流密度下的高电势限制在0.8V以下。通过抑制小电流密度下的电压,实现了50%的怠速功率降幅。

油耗测量设备控制系统设计

为实现某款工程机械用发动机油耗测量设备正常工作,对其控制系统进行了设计。该控制系统以STM32单片机为控制单元,分别对电源管理模块、单片机模块、电磁阀驱动模块、传感器选择与信号处理模块、数据存储模块和显示屏模块等硬件电路进行设计,并完成了对应的软件开发。测试实验结果表明:该控制系统能够控制电磁阀启停,使设备平稳供油连续测量,与高精度油耗仪器对比,该设备测量误差在1.5%以内,满足了预期的要求。