Study on a Closed-Loop Air-Fuel Control System of Gasoline Engines by Simulation

In order to study the factors that influence the air fuel ratio(A/F), the amplitude and frequency of A/F fluctuation, to reform the control strategy, and to improve the efficiency of three way catalyst(TWC), a model of closed loop control system including the engine, air fuel mixing and transportation, oxygen sensor and controller, etc., is developed. Various factors that influence the A/F control are studied by simulation. The simulation results show that the reference voltage of oxygen sensor will influence the mean value of A/F ratio, the controller parameters will influence the amplitude of A/F fluctuation, and the operating conditions of the engine determine the frequency of A/F fluctuations, the amplitude of A/F fluctuation can be reduced to within demanded values by logical selection of the signal acquisition method and controller parameters. Higher A/F fluctuation frequency under high speed and load can be reduced through software delay in the controller. The A/F closed loop control system based on the simulation results, accompanied with a rare earth element TWC, gives a better efficiency of conversion against harmful emissions.

The Possibility of Active Attitude Control for Fuel Spray

The internal combustion engine (ICE) is an attractive power source for automobiles, with its superior storability, transportability, and suppliability of liquid fuel with high energy density. Compact ICEs with high performance and a low environmental load are greatly needed. In the future, smart active control of combustion by means of fuel spray injection must be considered as a breakthrough technology to address serious issues related to conventional ICEs, such as emissions. A designed fuel injection rate and spray pattern during the injection period have been technically developed, and combustion can be partially controlled in the conventional ICE. However, spatial fuel distribution is not progressing as desired in the field of combustion;thus, new and effective active control technologies for fuel spray are very necessary for the smart control of combustion. Cavitation, flash boiling, spray-to-spray interaction, spray-to-wall interaction, and air flow have potential as a basis for active attitude control of fuel spray. This article uses evidence from the literature to discuss the possibility of active spray attitude control for future fuel spray combustion technology in a smart compact ICE.

Adaptive Air-Fuel Ratio Control with MLP Network

This paper presents an application of adaptive neural network model-based predictive control (MPC) to the air-fuel ratio of an engine simulation. A multi-layer perceptron (MLP) neural network is trained using two on-line training algorithms: a back propagation algorithm and a recursive least squares (RLS) algorithm. It is used to model parameter uncertainties in the nonlinear dynamics of internal combustion (IC) engines. Based on the adaptive model, an MPC strategy for controlling air-fuel ratio is realized, and its control performance compared with that of a traditional PI controller. A reduced Hessian method, a newly developed sequential quadratic programming (SQP) method for solving nonlinear programming (NLP) problems, is implemented to speed up nonlinear optimization in the MPC. Keywords Air-fuel ratio control - IC engine - adaptive neural networks - nonlinear programming - model predictive control Shi-Wei Wang PhD student, Liverpool John Moores University; MSc in Control Systems, University of Sheffield, 2003; BEng in Automatic Technology, Jilin University, 2000; Current research interests automotive engine control, model predictive control, sliding mode control, neural networks.Ding-Li Yu obtained B.Eng from Harbin Civil Engineering College, Harbin, China in 1981, M.Sc from Jilin University of Technology, Changchun, China in 1986 and PhD from Coventry University, U.K. in 1995, all in control engineering. He is currently a Reader in Process Control at Liverpool John Moores University, U.K. His current research interests are in process control, engine control, fault detection and adaptive neural nets. He is a member of SAFEPROCESS TC in IFAC and an associate editor of the IJMIC and the IJISS.

Development of fuzzy control of a fuel cell generation system using FPGA

Afuzzy controller based oni mproved Generalized-Membership-Function(GMF) algorithmfor afuel cell generationsys-tem wasintroduced.Under the demands on control in application of the converter,a Field Programmable Gate Array(FPGA) re-alization method to manage the power flow was given.This control systembased onthe proposed modified GMF was proved to bea universal approxi mation systemin theory.The fuzzy control technique was combined with Eletronic Design Automatic(EDA)technique and a paralleling fuzzy controller was i mplemented in FPGA.Paralleling fuzzy controller based oni mproved GMF algo-rithm wasi mplemented on a Cyclone FPGA.The result of si mulation based on QuartusII confirmed the validity of the proposed method.

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.

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.

Structure and Control Strategies of Fuel Cell Vehicle

The structure and kinds of the fuel cell vehicle (FCV) and the mathematical model of the fuel cell processor are discussed in detail. FCV includes many parts: the fuel cell thermal and water management, fuel supply, air supply and distribution, AC motor drive, main and auxiliary power management, and overall vehicle control system. So it requires different kinds of control strategies, such as the PID method, zero-pole method, optimal control method, fuzzy control and neural network control. Along with the progress of control method, the fuel cell vehicle's stability and reliability is up-and-up. Experiment results show FCV has high energy efficiency.

Safe Controlled Storage of SVBR-100 Spent Nuclear Fuel in the Extended-Range Future

Experience of operating reactor facilities (RF) with lead-bismuth coolant (LBC) has revealed that it is possible to perform safe refueling in short terms if the whole core is replaced and a kit of the special refueling equipment is used. However, comparing with RFs of nuclear submarines (NS), in which at the moment of performance of refueling the residual heat release is small, at RF SVBR-100 in a month after the reactor has been shut down, at the moment of performance of refueling the residual heat release is about 500 kW. Therefore, it is required to place the spent removable unit (SRU) with spent fuel subassemblies (SFSA) into the temporal storage tank (TST) filled with liquid LBC, in which the conditions for coolant natural circulation (NC) and heat removal via the tank vessel to the water cooling system are provided. After the residual heat release has been lowered to the level allowing transportation of the TST with SRU in the transporting-package container (TPC), it is proposed to consider a variant of TPCs transportation to the special site. On that site after the SRU has been reloaded into the long storage tank (LST) filled with quickly solidifying liquid lead, the TPCs can be stored during the necessary period. Thus, the controlled storage of LSTs is realized during several decades untill the time when SNF reprocessing and NFC closing are becoming economically expedient. On that storage, the four safety barriers are formed on the way of the release of radioactive products into the environment, namely: fuel matrix, fuel element cladding, solid lead and steel casing of the LST.

Transient Air-Fuel Ratio Control in a CNG Engine Using Fuzzy Neural Networks

The fuzzy neural networks has been used as means of precisely controlling the air-fuel ratio of a lean-burn compressed natural gas (CNG) engine. A control algorithm, without based on engine model, has been (utilized) to construct a feedforward/feedback control scheme to regulate the air-fuel ratio. Using fuzzy neural networks, a fuzzy neural hybrid controller is obtained based on PI controller. The new controller, which can adjust parameters online, has been tested in transient air-fuel ratio control of a CNG engine.

Output Tracking Control of a Hydrogen-air PEM Fuel Cell

Hydrogen-air proton exchange membrane U+0028 PEM U+0029 fuel cell is a promising clean energy. However, the stack output tracking control is still a challenging problem due to the soft characteristic of the stack. Both over- and less-control will cause the stack flooding or oxygen lacking which dramatically decreases the life of stacks. Traditional control methods rely on the accurate model of the fuel cell system, which is a high-order nonlinear system, and involve a complex controller design process. This paper combines the data-based fuzzy cluster modeling technology with the sliding mode control and the integral actions. The sliding mode controller tracks the dynamic changes of the fuel cell system and the integral controller eliminates the steady-state errors. Simulation results demonstrate good performance of the proposed control method. © 2017 Chinese Association of Automation.

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。

航空发动机燃油控制装置可靠性研究综述

航空发动机燃油控制装置是航空发动机的核心控制单元,长期服役在高温、高压、强振的恶劣环境,在航空发动机外场使用中故障率占比高,产品的寿命及可靠性是当前制约我国装备能力提升的短板。本文总结了当前燃油控制装置的典型故障类型,对比分析了国内外在燃油控制装置可靠性设计与分析技术、可靠性优化技术、寿命试验技术及可靠性评估技术方面的研究进展;剖析了我国燃油控制装置可靠性技术研究及应用方面的不足及原因,同时结合航空发动机正向研发体系建设需要,指出了航空发动机自主创新设计中燃油控制装置性能及可靠性一体化设计所面临的技术难点和挑战,并立足于现有国内设计水平及工业基础,给出了夯实我国航空燃油控制装置设计能力需开展的基础性研究建议。

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.

基于优化功率跟随控制的E-REV能量管理策略研究

基于功率跟随控制的增程式电动汽车能量管理策略具有减缓电池寿命衰减与提高车辆NVH性能等优势,但存在阈值参数依赖性强、增程器启停频繁等问题,为此提出了一种基于优化功率跟随控制的E-REV能量管理策略。依据车速、SOC状态与驾驶员的加速踏板力度等信息特征,制定基于功率跟随控制的能量管理策略。在此基础上,针对固定规则参数的局限性,以车辆行驶总成本与SOC变化梯度为目标函数,结合灰狼优化算法对增程器启停功率阈值参数进行优化,减少发动机频繁启停现象。运用Matlab/Simulink搭建控制策略模型,并联合基于Simcenter/AMESIM搭建的整车物理模型进行仿真试验,结果表明:CHTC-LT循环工况下,优化功率跟随控制策略与功率跟随控制策略相比,SOC最大波动值降低了28%,增程器启停次数减少了28.5%,整车燃油经济性提升了6.89%。

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。

基于LSTM-MPC的PEMFC运行状态建模与容错控制

质子交换膜燃料电池(proton exchange membrane fuel cell,PEMFC)具有多物理场耦合特性易产生不同故障且难以控制。为了能在故障状态下快速有效控制,提出基于模型预测控制(modelpredictivecontrol,MPC)的容错控制方案。首先,以长短时记忆神经网络(long short-term memory,LSTM)的预测误差为遗传算法的适应度函数,寻优获取LSTM的最优超参数组合,基于数据驱动构建PEMFC系统在4种不同运行状态下的LSTM预测模型作为预测模型模块。然后,建立基于神经网络的控制器作为优化控制器模块,根据上述模块制定以PEMFC系统阴阳极输入气体压强为控制量、电堆电压为输出量的容错控制方案。最后,仿真验证LSTM预测模型与容错控制方案得到,LSTM预测模型在训练集和测试集的评估指标均方根误差(root mean square error,RMSE)指标值分别为0.0489和0.0558,具有较好的拟合效果。在不同故障状态下,MPC相较于传统PID容错控制方案电压恢复时间缩短50%及以上,并在氢气泄露故障状态下,最大压降降低22.2%,证明了所提控制策略的有效性和正确性。

旁通阀结构对燃料电池系统阴极压力控制效果的分析

为减小压力波动对燃料电池寿命的不利影响,通过仿真及对比试验,探究增加旁通阀对阴极供给系统压力波动的改善效果。根据对燃料电池输出特性与各组件工作原理的分析,建立机理与控制模型;采用基于自抗扰的反向解耦方法实现流量与压力的解耦控制;运用模糊PI方法实现压力波动控制。通过Matlab/Simulink仿真验证了解耦方法在该系统结构下的控制效果,在对比试验中,压力波动峰值分别为无旁通阀1.82kPa与有旁通阀1.09kPa,旁通阀的增加减小了压力波动,使阴极流道压力的稳定性得到了更有效的控制,对提升燃料电池寿命有重要意义。

主动预燃室燃料控制参数对汽油机稀燃特性的影响

预燃室点火系统可促进稀薄混合气燃烧,从而显著提高热效率。本文基于自行开发的主动预燃室及其燃料供给系统,在压缩比为16的发动机上分别测试了预混气体喷射压力和燃料占比对发动机稀燃边界下燃烧特性的影响。试验结果表明:0.19、0.14和0.09 MPa 3种预混气体喷射压力中,在0.09 MPa的喷射压力下可获得最高的平均指示压力和最短的着火延迟期和燃烧持续期,燃烧最稳定;当预混气体燃料占比从0.54%逐步增加到2.69%时,发动机的平均指示压力先增加后减少,并在占比1.61%处取得最大值,此时的燃烧最为稳定,着火延迟期和燃烧持续期均最短。主动预燃室可实现λ=1.8的稳定燃烧,指示热效率相比普通火花塞点火的32.9%提高到39.4%,相对提升了19.8%。

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.

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。