Fueling Control of Spark Ignition Engines

Presented in this paper is an adaptive, model based, fueling control system for spark ignition-internal combustion engines. Since the fueling control system is model based, the engine maps currently used in engine fueling control are eliminated. This proposed fueling control system is modular and can therefore accommodate changes in the engine sensor set such as replacing the mass-air flow sensor with a manifold air pressure sensor. The fueling algorithm can operate with either a switching type O 2 sensor or a linear O 2 sensor. The fueling control system is also parceled into steady state fueling compensation and transient fueling compensation. This feature provides the distinction between fueling control adaptation for transient fueling and steady state fueling. The steady state fueling compensation utilizes a feedforward controller which determines the necessary fuel pulsewidth after a throttle transient to achieve stoichiometry. This feedforward controller is comprised of two nonlinear models capturing the steady state characteristics of the fueling process. These models are identified from an input-output testing procedure where the inputs are fuel pulsewidth and mass-air flow signal and the output is a lambda signal. These models are adapted via a recursive least squares method to accommodate product variability, engine aging, and changes in the operating environment. The transient fueling compensation also utilizes a feedforward controller that captures the essential dynamic characteristics of the transient fueling operation. This controller is measured using a frequency domain system identification approach. This proposed fueling control system is demonstrated on a Ford 4.6L V-8 fuel injected engine.     

高压共轨柴油机怠速油量控制试验研究

本研究针对4JB1高压共轨柴油机,研究了稳定怠速、从起动过渡到怠速以及从高速运行过渡到怠速3种工况的特点.不同过渡工况采用了相应的怠速油量控制补偿算法及DT1算法,稳定怠速采用PI控制器并实时对参数进行修正.经过试验验证了策略的合理性,怠速转速对环境及工况变化有很好的适应性,保证了怠速过渡工况的转速平顺性,避免了失速及...     

一种实现组合燃烧模式闭环控制的宽域氧传感器控制器

为了降低重型增压燃气发动机燃料消耗和热负荷,并使之运行在稀薄燃烧区,设计了一种宽域氧(UEGO)传感器控制器和基于此控制器快速实现稀薄燃烧控制的方法。该控制器通过采集UEGO、发动机转速和进气压力等信号,精确计算得到当前工况下的空燃比值,并与可标定的目标空燃比值进行比较,判断当前混合气的浓稀状态,向基于理论空燃比控制的燃气发动机ECU实时输出模拟的开关型氧传感器信号。试验表明:控制器结合基于理论空燃比控制的ECU能实现燃气发动机理论空燃比燃烧和稀薄燃烧组合模式的闭环控制。     

Gaussian Process Regression Feedforward Controller for Diesel Engine Airpath

Gaussian Process Regression (GPR) provides emerging modeling opportunities for diesel engine control. Recent serial production hardwares increase online calculation capabilities of the engine control units. This paper presents a GPR modeling for feedforward part of the diesel engine airpath controller. A variable geotmetry turbine (VGT) and an exhaust gas recirculation (EGR) valve outer loop controllers are developed. The GPR feedforward models are trained with a series of mapping data with physically related inputs instead of speed and torque utilized in conventional control schemes. A physical model-free and calibratable controller structure is proposed for hardware flexibility. Furthermore, a discrete time sliding mode controller (SMC) is utilized as a feedback controller. Feedforward modeling and the subsequent airpath controller (SMC+GPR) are implemented on the physical diesel engine model and the performance of the proposed controller is compared with a conventional PID controller with table based feedforward.     

Reduction of engine emissions via a real-time engine combustion control with an egr rate estimation model

Vehicle emissions regulations are becoming increasingly severe and remain a principal issue for vehicle manufacturers. Since, WLTP (Worldwide harmonized Light vehicles Test Procedures) and RDE (real driving emission) regulations have been recently introduced, the engine operating conditions have been rapidly changed during the emission tests. Significantly more emissions are emitted during transient operation conditions compared to those at steady state operation conditions. For a diesel engine, combustion control is one of the most effective approaches to reduce engine exhaust emissions, particularly during the transient operation. The concern of this paper is about reducing emissions using a closed loop combustion control system which includes a EGR rate estimation model. The combustion control system calculates the angular position where 50 % of the injected fuel mass is burned (MFB50) using in-cylinder pressure for every cycle. In addition, the fuel injection timing is changed to make current MFB50 follow the target values. The EGR rate can be estimated by using trapped air mass and in-cylinder pressure when the intake valves are closed. When the EGR rate is different from the normal steady conditions, the target of MFB50 and the fuel injection timing are changed. The accuracy of the model is verified through engine tests, as well as the effect of combustion control. The peaks in NO level was decreased during transient conditions after adoption of the EGR model-based closed loop combustion control system.     

Iterative Learning Control Algorithm for Feedforward Controller of EGR and VGT Systems in a CRDI Diesel Engine

The modern diesel engines equip the exhaust gas recirculation (EGR) system to suppress the NOx emissions. In addition, the variable geometric turbocharger (VGT) system is installed to improve the drivability and fuel efficiency. These EGR and VGT systems have cross-coupled behavior because they interact with the intake and the exhaust manifolds. Furthermore, the turbocharger time delay, gas flow dynamics through EGR pipe cause the nonlinearity characteristics. These nonlinear multi-input-multi-output characteristics cause the degradation of control accuracy, especially during the transient condition. In order to improve the control accuracy, this study proposes an iterative learning control (ILC) algorithm for feedforward controller of EGR and VGT systems. The feedforward controller obtains the values about EGR and VGT actuators using the previous control results in predefined transient states. The ILC algorithm consists of a PD-type learning function and a low-pass filter. The control gains of learning function are determined to guarantee the convergence of learning results. In addition, the low-pass filter is designed for robustness against plant disturbance. The proposed control algorithm was validated by engine experiment which repeated predefined transient states. The error was reduced by 15 % according to the gain. As a result, the proposed algorithm is affordable for improving the transient control performance.     

信息融合技术在汽油机过渡工况空燃比控制中的应用

对多传感器信息融合技术作了简要介绍,并结合汽油机过渡工况空燃比控制系统的要求与特点,探讨了信息融合技术用于空燃比控制系统的基本层次结构。将信息融合的层次与空燃比控制的功能相对应,提出了汽油机过渡工况空燃比控制的信息融合模型,进而对汽油机空燃比系统中信息融合在不同层次上的实现方法进行了讨论。     

Wall-wetting model based method for air-fuel ratio transient control in gasoline engines with dual injection system

Modern gasoline engines with dual injection system improve combustion efficiency by using the two injection commands simultaneously. Due to the different dynamics in the two injection paths, the transient air-fuel ratio (A/F) performance when the injection authority is changed remarkably affects the emission performance of the engine system. For this kind of engines, this paper proposes a model-based design approach that gives a dynamic feedforward compensation for achieving good transient A/F performance. Actually, this work centers on discussing the mean-value model parameters for characterizing the wall-wetting phenomenon when the port injection authority is changed under different engine operation conditions. Moreover, from the view of practice, taking into account the event-based physics of the engine system, significant calibration for the model parameters that are employed for achieving the feedforward compensation is investigated experimentally. Finally, experimental validation results demonstrate the improvement of the transient A/F performance by using the control scheme.     

基于Elman神经网络的车用汽油机过渡工况空燃比辨识

以HL495Q型电喷汽油机为研究对象,分析了汽油机空燃比的数学模型,提出了一种基于Elman神经网络的过渡工况空燃比辨识方法。试验结果表明,Elman神经网络空燃比模型具有简单的网络结构,能高精度地逼近车用汽油机空燃比的实际动态过程,模型的平均相对误差小于1%,优于前馈BP神经网络模型的辨识结果。建立的Elman神经网络空燃比模型能改善过渡工况空燃比控制精度,提高排放性能。     

汽油机点火正时微处理机自适应控制系统的研究

本文介绍了一种利用微处理机自适应控制汽油机最佳点火正时的系统。该系统由一单板微处理机和一套全晶体管电子点火装置组成,反馈信号由汽油机转速传感器检取。汽油机稳定工况的台架试验和整车道路试验表明:用这种自适应控制点火系统与用机械分电器装置相比,汽油机的输出功率增大,油耗率下降,整车的动力性和燃油经济性得到提高。     

汽油机过渡工况进气流量的神经网络预测研究

进气流量的精确测量是车用汽油机空燃比精确控制的基础,发动机工作在过渡工况时,因进气状态变化,空气流量传感器的滞后响应影响了过渡工况空燃比的控制精度。提出了一种基于汽油机过渡工况各种参数信息融合的过渡工况进气流量预测方法,分析了影响汽油机过渡工况进气流量的各种工况参数,提取了特征参数并建立了BP神经网络信息融合预测模型。对车用汽油机加减速工况试验数据进行仿真,研究结果表明,该方法能够准确实时地预测汽油机过渡工况的进气流量,同时能够消除空气流量传感器的滞后特性。     

基于SIMULINK的汽油机速度模型与仿真

根据汽油机的动力学原理,在MATLAB/SIMULINK下建立了各子系统的模型,并比较了不同的燃油动态子模型对转速的影响,仿真结果表明燃油动态对暂态转速影响显著,并验证了燃油参数使系统成为非线性的作用比节气门开度、发动机负载等的作用更大。     

直接氢气燃料电池发动机特性的仿真

在综合各种建模技术的基础上对直接氢气燃料电池发动机进行了建模，并在Matlab/Simulink平台上实现了单级和二级增压两种不同结构系统的仿真模型。通过仿真分析，在各子系统间优化匹配的基础上，对发动机的稳态和动态性能进行了详细探讨，对发动机工作参数的优化进行了研究。     

Nonlinear feedforward–feedback control of clutch-to-clutch shift technique

To improve the shift quality of the vehicle with clutch-to-clutch gear shifts, a nonlinear feedforward–feedback control scheme is proposed for clutch slip control during the shift inertia phase. The feedforward control is designed based on flatness in consideration of the system nonlinearities, and the linear feedback control is given to accommodate the model errors and the disturbances. Lookup tables, which are widely used to represent complex nonlinear characteristics of powertrain systems, appear in their original form in the designed feedforward controller, while the linear feedback controller is calculated through linear matrix inequalities such that the control system is robust against the parameter uncertainties. Finally, the designed controller is tested on an AMESim powertrain simulation model, which contains a time-variant model of clutch actuators.     

Adaptation Strategy for Exhaust Gas Recirculation and Common Rail Pressure to Improve Transient Torque Response in Diesel Engines

Fuel injection limitation algorithms are widely used to reduce particulate matter (PM) emissions under transient states in diesel engines. However, the limited injection quantity leads to a decrease in the engine torque response under transient states. To overcome this issue, this study proposes an adaptation strategy for exhaust gas recirculation (EGR) and common rail pressure combined with a fuel injection limitation algorithm. The proposed control algorithm consists of three parts: fuel injection limitation, EGR adaptation, and rail pressure adaptation. The fuel injection quantity is limited by adjusting the exhaust burned gas rate, which is predicted based on various intake air states like air mass flow and EGR mass flow. The control algorithm for EGR and rail pressure was designed to manipulate the set-points of the EGR and rail pressure when the fuel injection limitation is activated. The EGR controller decreases the EGR gas flow rate to rapidly supply fresh air under transient states. The rail pressure controller increases the rail pressure set-point to generate a well-mixed air-fuel mixture, resulting in an enhancement in engine torque under transient states. The proposed adaptation strategy was validated through engine experiments. These experiments showed that PM emissions were reduced by up to 11.2 %, and the engine torque was enhanced by 5.4 % under transient states compared to the injection limitation strategy without adaptation.     

Common rail injection system iterative learning control based parameter calibration for accurate fuel injection quantity control

This paper presents an accurate engine fuel injection quantity control technique for high pressure common rail (HPCR) injection systems by an iterative learning control (ILC)-based, on-line calibration method. Accurate fuel injection quantity control is of importance in improving engine combustion efficiency and reducing engine-out emissions. Current Diesel engine fuel injection quantity control algorithms are either based on pre-calibrated tables or injector models, which may not adequately handle the effects of disturbances from fuel pressure oscillation in HPCR, rail pressure sensor reading inaccuracy, and the injector aging on injection quantity control. In this paper, by using an exhaust oxygen fraction dynamic model, an on-line parameter calibration method for accurate fuel injection quantity control was developed based on an enhanced iterative learning control (EILC) technique in conjunction with HPCR injection system. A high-fidelity, GT-Power engine model, with parametric uncertainties and measurement disturbances, was utilized to validate such a methodology. Through simulations at different engine operating conditions, the effectiveness of the proposed method in rejecting the effects of uncertainties and disturbance on fuel injection quantity control was demonstrated.     

汽油发动机水温传感器检测与故障分析

水温传感器的功能是检测发动机冷却液温度,并将温度信号转换为电信号传送给发动机电控单元,电控单元根据该信号修正喷油时间和点火时间,使发动机工况处于最佳运行状态。当水温传感器发生故障时,发动机会易出现冷、热车启动困难、油耗增加、怠速稳定性降低、废气排放量升高等等故障现象。     

Comparing rear wheel steering and rear active differential approaches to vehicle yaw control

A comparison between two different approaches to vehicle stability control is carried out, employing a robust non-parametric technique in the controller design. In particular, an enhanced internal model control strategy, together with a feedforward action and a suitably generated reference map, is employed for the control of a vehicle equipped either with a rear wheel steering (RWS) system or with a rear active differential (RAD) device. The uncertainty arising from the wide range of operating conditions is described by an additive model set employed in the controller design. Extensive steady state and transient tests simulated with an accurate 14 degrees of freedom nonlinear model of the considered vehicle show that both systems are able to improve handling and safety in normal driving conditions. RAD devices can make the vehicle reach higher lateral acceleration values but they achieve only slight stability improvements against oversteer. On the other hand, 4WS systems can greatly improve both vehicle safety and manoeuvrability in all driving situations, making this device an interesting and powerful stability system.     

柴油机冷起动过程喷油系统的控制模式

冷起动性能是柴油机重要的使用特性之一。与机械式喷油泵相比，电控喷油系统可使柴油机冷起动过程自动化和冷起动混合气状态最佳化。从柴油机冷起动过程及其影响因素、冷起动燃烧过程和喷油系统的最佳控制模式等方面，论述了最佳冷起动过程的电控喷油模式。设计了冷起动控制器，使柴油机能自动地与变化的起动条件相适应。     

Dynamic sensor zeroing algorithm of 6D IMU mounted on ground vehicles

The main focus of this paper is to compensate the steady state offset error of the 6D IMU which provides the measurements that include the vehicle linear accelerations and angular rates of all three axes. Additionally, the sensor compensation algorithm exploits the wheel speed data and the steering angle information, since they are already available in most of the modern mass production vehicles. These inputs are combined with the inverse vehicle kinematics to estimate the steady state offset error of each sensor inputs as it is done in a disturbance observer, and the raw sensor measurements are compensated by the estimated offset errors. The stability of the error dynamics regarding the integrated signal processing system is verified, and finally, the performance of the system is tested via experiments based on a real production SUV.