车用汽油机进气歧管空气流量智能测量模型

为了确保车用汽油机在运转工况下能获得最佳浓度的混合气要求,利用节气门元器件,基于压差式节流流量测量原理,通过对节气门旋转角度函数链神经网络拟合和节气门的流出系数拟合建立了车用汽油机进气歧管空气流量测量模型。实验验证结果表明:随着进气歧管压力升高,车用汽油机空气质量流量智能测量误差呈降低趋势,且其误差均小于5.0%。     

某涡轮增压器壳体热应力分析

一、前言 涡轮增压器主要由涡轮机和压气机等构成,其将发动机排出的废气引入涡轮机,利用废气的能量推动涡轮机旋转,由此驱动与涡轮同轴的压气机实现增压.涡轮机进气口与发动机排气歧管相连,排气口则接在排气管上;压气机进气口与空气滤清器相连,排气口则接在进气歧管上.     

基于观测器的发动机转矩跟踪模型预测控制

针对如何实现发动机转矩快速精准地跟踪期望转矩的问题,提出一种基于观测器的模型预测控制策略.首先,利用均值模型对汽油发动机的进气歧管压力动态、转矩和转速动态进行建模,考虑到发动机真实转矩不可测的情况,采用Lyapunov稳定性理论和可测转速信号设计观测器对进气歧管压力进行在线估计,进而获得发动机的实时估计转矩;然后,利用基于观测器的模型预测控制算法设计转矩跟踪控制器,通过C/GMRES数值优化算法在线求解滚动时域优化问题,实现转矩的实时跟踪控制;最后,利用汽油发动机实验台进行实验验证以表明所提出算法的有效性.     

歧管加热器自动测试系统

0引言汽车安装歧管加热器能够解决冬天不易启动的问题。为了确保加热器的产品质量，我们研制了进气歧管加热器自动测试系统对加热器的主要性能指标进行测试。歧管加热器由铝合金压铸而成。中间为直径80mrn，高40mm的圆柱体。一个端面凸出，均匀排列着45很棒状加热销；另一...     

增程器用天然气发动机转速双闭环自适应控制

针对增程器用天然气发动机参数不确定,输出扭矩难以精确计算,存在未知干扰,且需要大范围调速等问题,设计发动机转速的双闭环自适应控制策略,并分析系统的稳定性.所提策略的外环为发动机转速环,控制器输出为目标进气压力,内环为进气歧管压力环,控制器输出为节气门开度.该策略结构简单,不需要知道发动机各个参数的具体值,抗干扰性能强,能够满足增程器发动机大范围调速的特点.分别在Matlab/Simulink平台和增程器台架上验证了所提策略的有效性和实用性.     

硅压阻MAP传感器的温度补偿校准

介绍如何利用Maxim的信号调理器MAX1452/MAX1455对硅压阻传感器进行温度补偿校准,使其满足低排放汽车对进气歧管绝对压力(MAP)传感器的要求。     

组态王在传感器检测台中的应用

文章阐述了利用组态王对汽车进气歧管中所使用的T-MAP型压力温度传感器进行自动化检测的设计与实现.考虑到组态王对采集时间的限制,于是利用VB对采集卡进行控制,通过DDE的方式实现VB与组态王数据通信.系统利用组态王提供的标准控件,实现了动态加载并更新数据库.该系统在现场运行状态良好,可以推广到类似的项目开发中.     

排气歧管流固耦合仿真与试验

针对发动机排气歧管的断裂问题,对某发动机排气歧管的热负荷进行流固耦合分析,并在台架试验中测量其温度场和应变.考虑热辐射和材料的非线性,建立排气歧管的内流场CFD模型、传热模型和有限元模型,分析得到排气歧管的温度场、应力场和塑性应变云图等.仿真结果与试验结果吻合较好.仿真结果表明排气歧管汇合处的温度最高,塑性应变最大位置与试验测得的断裂危险区域吻合.     

发动机排气歧管流固耦合仿真

应用AVL-fire软件和Abaqus软件对某发动机排气歧管进行流固耦合仿真.对排气歧管材料进行各温度下的材料试验,得到准确的材料在不同温度下的属性曲线.由AVL-fire软件和Abaqus软件进行流固耦合仿真得到排气歧管的温度场分布,并进行排气歧管应力和应变分析.仿真结果与试验结果的对比表明,塑性应变高的区域正是发生开裂的区域.     

基于Abaqus的某柴油机排气歧管热固耦合分析

排气歧管在工作状态下会承受很高的环境温度,而在较高的环境温度下金属材料的力学性能和热学性能通常会发生较大变化.通过添加实测的各种部件材料随温度变化的性能参数,介绍某柴油机排气歧管热固耦合分析过程,展现使用AVL系列软件与Abaqus软件进行的排气歧管热固耦合分析流程.     

Combustion timing control of HCCI engine based on NNPC and Elman-NN model under complex conditions

Homogeneous Charge Compression Ignition (HCCI) combines the characteristics of gasoline engine and diesel engine with high thermal efficiency and low emissions. However, since there is no direct initiator of combustion, it is difficult to control the combustion timing in HCCI engines under complex working conditions. In this paper, Neural Network Predictive Control (NNPC) for combustion timing of the HCCI engine is designed and implemented. First, the black box model based on Elman neural network is designed and developed to estimate the combustion timing. The fuel equivalence ratio, intake valve closing timing, intake manifold temperature, intake manifold gas pressure, and engine speed are chosen as the system inputs. Then, a NNPC controller is designed to control combustion timing by controlling the intake valve closing timing. Simulation results show that the Elman neural network black box model is capable of estimating the HCCI engine combustion timing. In addition, regardless of whether the HCCI engine is in constant or complex condition, the designed NNPC controller is capable of keeping the combustion timing within the ideal range. In particular, under New European Driving Cycle (NEDC) working conditions, the maximum overshoot of the controller is 28.95% and the average error is 1.03 crank angle degree. It is concluded that the controller has good adaptability and robustness.     

Pressure-based transient intake manifold temperature reconstruction in Diesel engines

Temperature measurements by the typical thermocouples contain some first-order dynamics with varying time-constants and need to be reconstructed in transient conditions for improving the accuracy of the temperature information. Particularly, for Diesel engine advanced combustion mode control, the accurate acquisitions of the rapidly varying transient temperatures, such as the intake manifold gas temperature, are of importance. In this paper, a temperature reconstruction method, without using additional sensors, is proposed by utilizing the counterpart pressure signal. Through investigating the thermocouple dynamics in terms of the intake manifold pressure and temperature, an intake manifold temperature model was derived. According to this proposed temperature model, the transient temperature reconstruction can be formulated as a thermocouple time-constant estimation problem. Within this framework, an extended Kalman filter (EKF) based method was devised for the parameter estimations. The proposed method was validated through high-fidelity GT-Power engine model simulations as well as experimental results obtained on a multi-cylinder medium-duty Diesel engine.     

Nonlinear Observer-based Control Design and Experimental Validation for Gasoline Engines with Exhaust Gas Recirculation

This paper presents a nonlinear observer-based control design approach for gasoline engines equipped with exhaust gas recirculation (EGR) system. A mean value engine model is designed for control which includes both the intake manifold and exhaust manifold dynamic focused on gas mass flows. Then, the nonlinear feedback controller based on the developed model is designed for the state tracking control, and the stability of the close loop system is guaranteed by a constructed Lyapunov function. Since the exhaust manifold pressure is usually unmeasurable in the production engines, a nonlinear observer-based feedback controller is proposed by using standard sensors equipped on the engine, and the asymptotic stability of the both observer dynamic system and control dynamic system are guaranteed with Lyapunov design assisted by the detail analysis of the model. The experimental validations show that the observer-based nonlinear feedback controller is able to regulate the intake pressure and exhaust pressure state to the desired values during both the steady-state and transient conditions quickly by only using the standard sensors.     

热膜式空气质量流量传感器动态特性研究

空气流量传感器可以精确测量发动机进气量,发动机在进气管内的流动是流速变化较大的非稳态流动,需要流量传感器具有较快的动态响应特性。通过建立取样流道中的热固耦合模型,利用流体力学的方法,模拟计算动态响应特性,获得了流量传感器探头的温度场,利用阶跃脉冲模拟计算了传感器的动态响应特性,利用气道试验台进行动态响应特性的测试。热膜的热平衡需要一个自我调整的过程,所以,实测的动态响应速度低于计算模拟值。     

Modeling and online parameter estimation of intake manifold in gasoline engines using sliding mode observer

Model based control of automotive engines for fuel economy and pollution minimization depends on accuracy of models used. A number of mathematical models of automotive engine processes are available for this purpose but critical model parameters are difficult to obtain and generalize. This paper presents a novel method of online estimation of discharge coefficient of throttle body at the intake manifold of gasoline engines. The discharge coefficient is taken to be a varying parameter. Air mass flow across the throttle body is a critical variable in maintaining a closer to stoichiometric air fuel ratio; which is necessary to minimize the pollution contents in exhaust gases. The estimation method is based on sliding mode technique. A classical first Sliding mode observer is designed to estimate intake manifold pressure and the model uncertainty arising from the uncertain and time varying discharge coefficient is compensated by the discontinuity/switching signal of sliding mode observer. This discontinuity is used to compute coefficient of discharge as a time varying signal. The discharge coefficient is used to tune/correct the intake manifold model to engine measurements. The resulting model shows a very good agreement with engine measurements in steady as well s transient state. The stability of the observer is shown by Lyapunov direct method and the validity of the online estimation is successfully demonstrated by experimental results. OBD-II (On Board Diagnostic revision II) based sensor data acquisition from the ECU (Electronic Control Unit) of a production model vehicle is used. The devised algorithm is simple enough to be designed and implemented in a production environment. The online estimation of parameter can also be used for engine fault diagnosis work.     

汽车发动机模型硬件在环仿真研究

由于对汽车发动机模型研究侧重面不同,其方法、复杂度和深度也各异。为保证模型的相对简单和有效性并适应控制和诊断需要,简化了气缸内的燃烧过程和机械系统动力学过程,并在MATALB／SIMULNK环境下建立了面向控制和诊断的发动机模型,主要包括：进气岐管子模型,气缸内气体压力子模型和机械系统动力学模型,并基于RT-LAB机群对所建模型进行了硬件在环仿真。     

An engine fault diagnosis system using intake manifold pressure signal and Wigner–Ville distribution technique

This paper proposed an engine fault diagnosis system based on intake manifold pressure signal and artificial neural network with the Wigner–Ville distribution technique. Traditionally, the engine diagnostic method depends on the experience of the technician, but some faults might be inaccurately judged by the technician’s experience when the engine is operating. In the present study, an engine platform diagnosis system using intake manifold pressure was developed. The algorithm of the proposed system consisted of Wigner–Ville distribution (WVD) for feature extraction and the neural network technique for fault classification. In previous work, the Wigner–Ville distribution was often used to analyze the non-stationary signal, because it provides a simple and clear energy spectrum diagram both in the time and frequency domains. This instantaneous energy diagram presented the magnitude of each engine fault under various operating conditions. The Wigner–Ville distribution extracts these features as database input to a neural network and the neural network is used to develop the training and testing modules. To prove the efficiency of the neural network, both the radial basis function neural network and generalized regression neural network are used and compared. The experimental results demonstrated the proposed system is effective and the performance is satisfactory.