Modeling cycle-to-cycle dynamics and mode transition in HCCI engines with variable valve actuation

Due to dilution limits, homogeneous charge compression ignition (HCCI) engines will need to switch to a conventional spark ignition (SI) or diesel mode at very low and high load conditions. This paper outlines a simple and accurate control-oriented model of a single-cylinder multi-mode HCCI engine using exhaust reinduction. An exhaust manifold model ties the exhausted gas from one cycle to that reinducted on the next cycle. The results show that the cyclic dynamics which occur during an SI-to-HCCI mode transition are essentially just an extension of the inherent cycle-to-cycle dynamics of HCCI. Multi-cycle, multi-mode simulations demonstrate the steady state and dynamic behavior seen on an experimental system. Predicted combustion timing, in-cylinder pressure, work output and exhaust temperature values agree very well with experiment. This model provides a basis for developing and validating controllers capable of controlling HCCI during mode transitions.     

Real-time control oriented HCCI engine cycle-to-cycle dynamic modelling

For homogeneous charge compression ignition (HCCI) combustion, the auto-ignition process is very sensitive to in-cylinder conditions, including in-cylinder temperature, in-cylinder components and concentrations. Therefore, accurate control is required for reliable and efficient HCCI combustion. This paper outlines a simplified gasoline-fueled HCCI engine model implemented in Simulink environment. The model is able to run in real-time and with fixed simulation steps with the aim of cycle-to-cycle control and hardware-in-the-loop simulation. With the aim of controlling the desired amount of the trapped exhaust gas recirculation (EGR) from the previous cycle, the phase of the intake and exhaust valves and the respective profiles are designed to vary in this model. The model is able to anticipate the auto-ignition timing and the in-cylinder pressure and temperature. The validation has been conducted using a comparison of the experimental results on Ricardo Hydro engine published in a research by Tianjin University and a JAGUAR V6 HCCI test engine at the University of Birmingham. The comparison shows the typical HCCI combustion and a fair agreement between the simulation and experimental results.     

Simple adaptive air-fuel ratio control of a port injection SI engine with a cylinder pressure sensor

The problem of air-fuel ratio(AFR) control of the port injection spark ignition(SI) engine is still of considerable importance because of stringent demands on emission control. In this paper, the static AFR calculation model based on in-cylinder pressure data and on the adaptive AFR control strategy is presented. The model utilises the intake manifold pressure, engine speed, total heat release, and the rapid burn angle, as input variables for the AFR computation. The combustion parameters, total heat release,and rapid burn angle, are calculated from in-cylinder pressure data. This proposed AFR model can be applied to the virtual lambda sensor for the feedback control system. In practical applications, simple adaptive control(SAC) is applied in conjunction with the AFR model for port-injected fuel control. The experimental results show that the proposed model can estimate the AFR, and the accuracy of the estimated value is applicable to the feedback control system. Additionally, the adaptive controller with the AFR model can be applied to regulate the AFR of the port injection SI engine.     

汽油机非线性卡尔曼滤波暂态进气量估计及空燃比控制

暂态工况下缸进气量的准确估计是提高发动机空燃比控制精度的有效措施之一,为此本文提出一种基于无迹卡尔曼滤波的暂态缸进气量估计算法,并利用估计的缸进气量设计了一种前馈-反馈空燃比控制器.MATLAB环境下的仿真实验给出了所提出的算法与现有进气量估计算法的比较,同时基于暂态气量估计的空燃比控制仿真实验验证了估计的有效性.论文与现有成果的区别在于:一是暂态进气量估计模型不仅包含了歧管压力动态还考虑了曲轴角速度动态,并采用了基于非线性辨识的均值模型;二是考虑了泵气波动的影响,采用了移动平均值法的数字滤波器对泵气波动进行滤波;三是采用无迹卡尔曼滤波算法对歧管压力和曲轴角速度进行估计.     

Stability analysis of residual-affected HCCI using convex optimization

Residual-affected homogeneous charge compression ignition (HCCI) is a promising methodology for simultaneously reducing emissions and fuel consumption. However, the process relies on cycle-to-cycle coupling between subsequent engine cycles through the exhaust gas temperature, resulting in sections of the state space which are unstable. This paper exploits a previously validated control model of HCCI to analytically determine the area of the state space which is stable to perturbation of either combustion timing or in-cylinder pressure. As efforts to control and expand the operating range of HCCI continue, analytical stability tools like that developed here will likely play an increasingly important role.     

Robust oxygen fraction estimation for conventional and premixed charge compression ignition engines with variable valve actuation

In-cylinder oxygen fraction serves as a critical control input to advanced combustion strategies, but is extremely difficult to measure on production engines. Fortunately, the in-cylinder oxygen levels can be estimated based on accurate estimates or measurements of the oxygen fraction in the intake and exhaust manifolds, the in-cylinder charge mass, and the residual mass. This paper outlines such a physically based, generalizable strategy to estimate the in-cylinder oxygen fraction from only production viable measurements or estimates of exhaust oxygen fraction, fresh air flow, charge flow, fuel flow, turbine flow and EGR flow. While several of these flows are accurately measured or estimated, significant errors in the turbine and EGR flows are commonly observed and can highly degrade the accuracy of any calculations which utilize these flows. An EGR flow estimator was developed to improve the accuracy of this flow measurement over the stock engine control module (ECM) method and is detailed in this paper. Furthermore, the in-cylinder oxygen estimation algorithm is developed, and proven, to be robust to turbine flow errors. Regulation of in-cylinder oxygen levels is of interest for not only in conventional combustion modes but also in advanced combustion strategies such as premixed charge compression ignition. The proposed oxygen fraction estimator is designed such that its performance and stability is ensured in both conventional and advanced combustion modes. The model-based observer estimates the oxygen fractions to be within 0.5% O₂ and is shown to have exponential estimator error convergence with a time constant less than 0.05 s, even with turbine flow errors of up to 25%.     

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.     

Two air path observers for turbocharged SI engines with VCT

Nowadays, downsizing is a major way to reduce fuel consumption and pollutant emissions of spark ignition (SI) engines. In downsized engines, new air path management systems such as turbocharging or variable camshaft timing (VCT) are included, and an efficient control of the air actuators is required for engine torque control. Two non-linear estimators are proposed to estimate non-measured variables of the air path. The first one is an in-cylinder air mass observer that combines feedforward neural static models and a linear parameter varying (LPV) polytopic observer. The second one is a neural estimator of the burned gas and scavenged air masses. Test bench results on a turbocharged SI engine with VCT show the real time applicability and good performance of the proposed estimators. Finally, a strategy for developing the engine supervisor is presented.     

Mutual information of cylinder pressure and combustion phase estimation in spark ignition engines

For the study of internal combustion engines, combustion control is an important method to achieve high efficiency and low emissions. Currently, in-cylinder pressure sensor-based closed-loop control strategies have become the preferred solution. However, their productional application in automotive industries is limited due to the cost of intensive pressure acquisition for a whole cycle and the calculation load of combustion phase indicators. This paper proposes a method of combustion phase estimation for spark ignition (SI) engines. In this method, the combustion phase is estimated only based on pressure measurements at several crank angles. Information entropy and mutual information are introduced to analyze the feasibility and accuracy of the combustion phase estimation, which shows that the pressure measurements at selected points contain most of the information for the estimation. As a result, only pressure measurements at 3 points and ELM estimation models are required to obtain the combustion phase, instead of intensive data acquisition and calculation.     

Air-fuel ratio control with stochastic L_2 disturbance attenuation in gasoline engines

In this paper, the problem of stochastic L2 disturbance attenuation of the air-fuel ratio is investigated with consideration of cyclic variation of the residual gas fraction （RGF）. A stochastic robust controller is designed based on a discrete-time dynamic model in which the RGF is modeled as a stochastic process with Markovian property. Finally, the sampling process-based statistical analysis for the RGF and the validation of the proposed control law are presented through the experiments conducted on a gasoline engine test bench.     

Neuro-controller for reducing cyclic variation in lean combustion spark ignition engines

Literature shows that by controlling engines at extreme lean operating conditions (equivalence ratio <0.75) can reduce emissions by as much as 30% (Inoue, Matsushita, Nakanishi, & Okano (1993). Toyota lean combustion system—the third generation SAE, 930,873) and also it improves fuel efficiency by as much as 5-10%. However, the engine exhibits strong cyclic variation in heat release which may lead to instability and poor performance. A novel neural network (NN) controller is developed to control spark ignition (SI) engines at extreme lean conditions. The purpose of neuro-controller is to reduce the cyclic variation in heat release at lean engine operation even when the engine dynamics are unknown. The stability analysis of the closed-loop control system is given and the boundedness of all the signals is ensured. The adaptive NN does not require an offline learning phase and the weights can be initialized at zero or random. Results demonstrate that the cyclic variation is reduced significantly using the proposed controller developed using an experimentally validated engine model. The proposed approach can also be applied to a class of nonlinear systems that have a similar structure as that of the engine dynamics.     

Closed-loop diesel engine combustion phasing control based on crankshaft torque measurements

Methods for closed-loop combustion phasing control in a diesel engine, based on measurements of crankshaft torque, are developed and evaluated. A model-based method for estimation of cylinder individual torque contributions from the crankshaft torque measurements is explained and a novel approach for identification of crankshaft dynamics is proposed. The use of the combustion net torque concept for combustion phasing estimation in the torque domain is also described. Two different control schemes, one for individual cylinder control and one for average cylinder control, are studied. The proposed methods are experimentally evaluated using a light-duty diesel engine equipped with a crankshaft integrated torque sensor. The results indicate that it is possible to estimate and control on a cylinder individual basis using the measurements from the crankshaft torque sensor. Combustion phasing is estimated with bias levels of less than 0.5 crank angle degrees (CAD) and cycle-to-cycle standard deviations of less than 0.7 CAD for all cylinders and the implemented combustion phasing controllers manage to accurately counteract disturbances in both fuel injection timing and EGR fraction.     

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.     

Using combustion net torque for estimation of combustion properties from measurements of crankshaft torque

Two methods, both based on the concept of combustion net torque, for estimation of combustion properties using measurements of crankshaft torque data are investigated in this work. The first of the proposed methods estimates entire burned mass fraction traces from corresponding combustion net torque traces. This is done by solving a convex optimization problem that is based on a derived analytical relation between the two quantities. The other proposed estimation method estimates the well established combustion phasing measure referred to as 50% burned mass fraction directly from combustion net torque using a nonlinear black-box mapping. The methods are assessed using both simulations and experimental data gathered from a 5-cylinder light-duty diesel engine equipped with a crankshaft torque sensor and cylinder pressure sensors that are used for reference measurements. The results indicate that both methods work well but the method that estimates entire burned mass fraction traces is more sensitive to torque data quality. Based on the experimental crankshaft torque data, the direct combustion phasing estimation method delivers estimates with a bias of less than 1 CAD and a cycle-to-cycle standard deviation of less than 2.7 CAD for all cylinders.     

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.     

Hybrid intelligent control of combustion process for ore-roasting furnace

Because of its synthetic and complex characteristics, the combustion process of the shaft ore-roasting furnace is very difficult to control stably. A hybrid intelligent control approach is developed which consists of two systems： one is a cascade fuzzy control system with a temperature soft-sensor, and the other is a ratio control system for air flow with a compensation model for heating gas flow and air-fuel ratio. This approach combined intelligent control, soft-sensing and fault diagnosis with conventional control. It can adjust both the heating gas flow and the air-fuel ratio in real time. By this way, the difficulty of online measurement of the furnace temperature is solved, the fault ratios during combustion process is decreased, the steady control of the furnace temperature is achieved, and the gas consumption is reduced. The successful application in shaft furnaces of a mineral processing plant in China indicates its effectiveness.     

Injection engine as a control object. II. Problems of automatic control of the engine

Specific features of injection engine as a control object are discussed, strict formulations of problems of engine automatic control and principles of their solution are presented. Examples of solution of the problem of stabilization of air-fuel ratio and engine torque control problems are presented as illustrations for demonstration of application of modern methods of automatic control theory for solution of control problems of injection engines.     

Semi-physical mean-value NOx model for diesel engine control

A semi-physical model has been developed to predict nitrogen oxide (NO_x) emissions produced by diesel engines. This model is suitable for online NO_x estimation and for model-based engine control. It is derived from a zero-dimensional thermodynamic model which was simplified by only retaining main phenomena contributing to NO_x formation. The crank angle evolution of the burned gas temperature, which has a strong impact on NO_x formation rate, is described by a semi-empirical model whose key variable is the maximum burned gas temperature. This variable presents a good correlation with the molar fraction of NO_x at the end of combustion and can be expressed as a function of the intake burned gas ratio and the start of combustion. The maximum burned gas temperature sub-model is then coupled to an averaged NO_x formation kinetic model (based on the Zeldovich mechanism) to form a mean-value model for NO_x computation. This latter model was validated using data sets recorded in two diesel engines for steady-state operating conditions as well as for several driving cycles including parametric variations of the engine calibration.     

Revisiting the benchmark problem of starting control of combustion engines

Starting of combustion engines is a typical transient operating mode that has significant influence to the engine performance. Due to the distinct variations in the pathes of air intake and fuel injection, the model of the engine system contains considerable uncertain parameters. To search effective control schemes that guarantee desired performance, engine starting control is proposed as a benchmark challenge problem. As a challenging result, a model-based control scheme is developed perviously. In this work, the benchmark problem is revisited and a modification for the fuel injection path control of the previous work is proposed by integrating a time sequence regressive based parameter tuning strategy. Validation by the benchmark problem simulator shows that although the new strategy has simple structure, similar control performance is obtained. Especially, the new strategy has potential extensibility with learning based methods to further improve the performance of the benchmark problem on engine starting control.     

Hybrid modelling of homogeneous charge compression ignition (HCCI) engine dynamics—a survey

The Homogeneous charge compression ignition (HCCI) principle holds promise to increase efficiency and to reduce emissions from internal combustion engines. As HCCI combustion lacks direct ignition timing control and auto-ignition depends on the operating condition, control of auto-ignition is necessary. Since auto-ignition of a homogeneous mixture is very sensitive to operating conditions, a fast combustion phasing control is necessary for reliable operation. To this purpose, HCCI modelling and model-based control with experimental validation were studied. A six-cylinder heavy-duty HCCI engine was controlled on a cycle-to-cycle basis in real time using a variety of sensors, actuators and control structures for control of the HCCI combustion. Combustion phasing control based on ion current was compared to feedback control based on cylinder pressure. With several actuators for controlling HCCI engines suggested, two actuators were compared, dual fuel and variable valve actuation (VVA). Model-based control synthesis requiring dynamic models of low complexity and HCCI combustion models were estimated by system identification and by physical modelling, the physical models aiming at describing the major thermodynamic and chemical interactions in the course of an engine stroke and their influence on combustion phasing. The models identified by system identification were used to design model-predictive control (MPC) with several desirable features and today applicable to relatively fast systems, the MPC control results being compared to PID control results. Both control of the combustion phasing and control of load-torque with simultaneous minimization of the fuel consumption and emissions, while satisfying the constraints on cylinder pressure, were included.