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.     

Optimal combustion control with application to engine design and development

The need to reduce development time whilst simultaneously improving engine performance has motivated this application of optimal control to product development processes for engines and powertrains. The optimisation of the fuel consumption is formulated as a constrained Optimal Control Problem (OCP) and solved using pseudospectral methods, giving the optimum heat release and injection profiles in the presence of cylinder pressure rate and cylinder pressure constraints. The technique is applied to an engine design problem and used to reduce fuel consumption by optimising compression ratio within a cylinder pressure limit, also providing new insights into the combustion processes.     

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。     

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.     

Extremum seeking-based optimal EGR set-point design for combustion engines in lean-burn mode

In lean combustion mode, exhaust gas ratio (EGR) is a significant factor that affects fuel economy and combustion stability. A proper EGR level is beneficial for the fuel economy; however, the combustion stability (coefficient of variation (COV) in indicated mean effective pressure (IMEP)) deteriorated monotonously with increasing EGR. The aim of this study is to achieve a trade-off between the fuel economy and combustion stability by optimizing the EGR set-point. A cost function (J) is designed to represent the trade-off and reduce the calibration burden for optimal EGR at different engine operating conditions. An extremum-seeking (ES) algorithm is adopted to search for the extreme value of J and obtain the optimal EGR at an operating point. Finally, a map of optimal EGR set-value is designed and experimentally validated on a real driving cycle.     

Cyclic moving average control approach to cylinder pressure and its experimental validation

Cyclic variability is a factor adversely affecting engine performance. In this paper a cyclic moving average regulation approach to cylinder pressure at top dead center （TDC） is proposed, where the ignition time is adopted as the control input. The dynamics from ignition time to the moving average index is described by ARMA model. With this model, a one-step ahead prediction-based minimum variance controller （MVC） is developed for regulation. The performance of the proposed controller is illustrated by experiments with a commercial car engine and experimental results show that the controller has a reliable effect on index regulation when the engine works under different fuel injection strategies, load changing and throttle opening disturbance.     

Microprocessor control of ignition advance angle

The ignition control requirements of an internal combustion petrol engine are reviewed and the benefits of accurate ignition control are discussed. A design for a microprocessor-based open-loop ignition controller is described and the experimental results obtained with this controller presented. Various means of achieving further improvements are suggested, including a closed-loop controller for optimum ignition timing under all conditions of engine wear. This strategy uses peak cylinder pressure angle as the feedback signal on which the adaptive control is based.     

Cycle-to-cycle modeling and sliding mode control of blended-fuel HCCI engine

Fast and robust control of combustion phasing is an important challenge for real-time model-based control of Homogenous Charge Compression Ignition (HCCI). In this paper a new discrete Control Oriented Model (COM) for predicting HCCI combustion phasing on a cycle-to-cycle basis is outlined and validated against experimental data from a single cylinder Ricardo engine. The COM has sufficient accuracy for real-time HCCI control and can be implemented in real-time.A Discrete Sliding Mode Controller (DSMC) coupled with a Kalman filter is designed to control combustion phasing by adjusting the ratio of two Primary Reference Fuels (PRFs). The results indicate the DSMC maintains the stability of the engine operation in a wide range of loads and speeds. The DSMC is compared with an empirical Proportional Integral (PI) controller. The results show the SMC outperforms a PI controller particularly in rejecting disturbances while maintaining HCCI combustion phasing in its desired range.     

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.     

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.     

A model-based gain scheduling approach for controlling the common-rail system for GDI engines

The progressive reduction in vehicle emission requirements have forced the automotive industry to invest in research for developing alternative and more efficient control strategies. All control features and resources are permanently active in an electronic control unit (ECU), ensuring the best performance with respect to emissions, fuel economy, driveability and diagnostics, independently from engine working point. In this article, a considerable step forward has been achieved by the common-rail technology which has made possible to vary the injection pressure over the entire engine speed range. As a consequence, the injection of a fixed amount of fuel is more precise and multiple injections in a combustion cycle can be made. In this article, a novel gain scheduling pressure controller for gasoline direct injection (GDI) engine is designed to stabilise the mean fuel pressure into the rail and to track demanded pressure trajectories. By exploiting a simple control-oriented model describing the mean pressure dynamics in the rail, the control structure turns to be simple enough to be effectively implemented in commercial ECUs. Experimental results in a wide range of operating points confirm the effectiveness of the proposed control method to tame efficiently the mean value pressure dynamics of the plant showing a good accuracy and robustness with respect to unavoidable parameters uncertainties, unmodelled dynamics, and hidden coupling terms.     

柴油发动机气缸压力和燃烧始点的辨识

在柴油发动机气缸压力的识别过程中，针对缸盖振动信噪比较低的问题，提出了对RBF网络训练样本进行时域统计平均降噪的方法，同时在对燃烧始点的辨识中应用了Kaiser差分器。时域统计平均有效提高了信号的信噪比，对柴油发动机气缸压力的恢复获得了很好的结果；Kaiser差分器有效提高了对燃烧始点的辨识。通过实践证明，该计算方法可以有效地从柴油发动机缸盖振动信号恢复气缸压力，并进一步辩识燃烧始点。     

Nonlinear identification of a gasoline HCCI engine using neural networks coupled with principal component analysis

Homogeneous charge compression ignition (HCCI) is a futuristic combustion technology that operates with high efficiency and reduced emissions. HCCI combustion is characterized by complex nonlinear dynamics which necessitates the use of a predictive model in controller design. Developing a physics based model for HCCI involves significant development times and associated costs arising from developing simulation models and calibration. In this paper, a neural networks (NN) based methodology is reported where black box type models are developed to predict HCCI combustion behavior during transient operation. The NN based approach can be considered a low cost and quick alternative to the traditional physics based modeling. A multi-input single-output model was developed each for indicated net mean effective pressure, combustion phasing, maximum in-cylinder pressure rise rate and equivalent air–fuel ratio. The two popular architectures namely multi-layer perceptron (MLP) and radial basis network (RBN) models were compared with respect to design, prediction performance and overall applicability to the transient HCCI modeling problem. A principal component analysis (PCA) is done as a pre-processing step to reduce input dimension thereby reducing memory requirements of the models. Also, PCA reduces the cross-validation time required to identify optimal model hyper-parameters. On comparing the model predictions with the experimental data, it was shown that neural networks can be a powerful approach for non-linear identification of a complex combustion system like the HCCI engine.     

A methodology for increasing the signal to noise ratio for the misfire detection at high speed in a high performance engine

The paper presents a methodology for pre-processing the combustion time intervals, that is the basic signal used in misfire detection strategies, with the aim of increasing the signal-to-noise ratio to enable a more efficient misfire diagnosis, especially when the engine is running at high speeds and low loads. The performance of the basic misfire detection algorithm shows that in those engine operating conditions the background noise amplitude has approximately the same value of the information related to the misfire presence, thus hiding the misfire event that may not be detected. The proposed methodology is based on the correction of the combustion time signal cycle-by-cycle, using a vector of data that take into account the specific behavior of every cylinder. The vector of data for the combustion time correction is stored in a map inside the control unit and could be continuously updated with an auto-adaptive learning technique.     

MODELING AND CONTROL OF INTERNAL COMBUSTION ENGINES USING INTELLIGENT TECHNIQUES

This article will compare two different fuzzy-derived techniques for controlling small internal combustion engine and modeling fuel spray penetration in the cylinder of a diesel internal combustion engine. The first case study is implemented using conventional fuzzy-based paradigm, where human expertise and operator knowledge were used to select the parameters for the system. The second case study used an adaptive neuro-fuzzy inference system (ANFIS), where automatic adjustment of the system parameters is affected by a neural networks based on prior knowledge. The ANFIS model was shown to achieve an improved accuracy compared to a pure fuzzy model, based on conveniently selected parameters. Future work is concentrating on the establishment of an improved neuro-fuzzy paradigm for adaptive, fast and accurate control of small internal combustion engines.     

Prediction of performance and exhaust emissions of a diesel engine fueled with biodiesel produced from waste frying palm oil

Biodiesel is receiving increasing attention each passing day because of its fuel properties and compatibility with the petroleum-based diesel fuel (PBDF). Therefore, in this study, the prediction of the engine performance and exhaust emissions is carried out for five different neural networks to define how the inputs affect the outputs using the biodiesel blends produced from waste frying palm oil. PBDF, B100, and biodiesel blends with PBDF, which are 50% (B50), 20% (B20) and 5% (B5), were used to measure the engine performance and exhaust emissions for different engine speeds at full load conditions. Using the artificial neural network (ANN) model, the performance and exhaust emissions of a diesel engine have been predicted for biodiesel blends. According to the results, the fifth network is sufficient for all the outputs. In the fifth network, fuel properties, engine speed, and environmental conditions are taken as the input parameters, while the values of flow rates, maximum injection pressure, emissions, engine load, maximum cylinder gas pressure, and thermal efficiency are used as the output parameters. For all the networks, the learning algorithm called back-propagation was applied for a single hidden layer. Scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) have been used for the variants of the algorithm, and the formulations for outputs obtained from the weights are given in this study. The fifth network has produced R² values of 0.99, and the mean % errors are smaller than five except for some emissions. Higher mean errors are obtained for the emissions such as CO, NO_x and UHC. The complexity of the burning process and the measurement errors in the experimental study can cause higher mean errors.     

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.     

Robust control design of an air‐breathing engine for a supersonic vehicle using backstepping and UKF

This paper presents an efficient robust control design approach for an air‐breathing engine for a supersonic vehicle using the Lyapunov stability theory based nonlinear backstepping control, augmented with unscented Kalman filter (UKF). The primary objective of the control design is to ensure that the thrust produced by the engine tracks the commanded thrust by regulating the fuel flow to the combustion chamber. Moreover, as the engine operates in a supersonic range, an important secondary objective is to manage the shock wave location in the intake for maximum pressure recovery with adequate safety margin by varying the throat area of the nozzle simultaneously. To estimate the states and parameters as well as to filter out the process and sensor noises, a UKF has been incorporated for robust output feedback control computation. Furthermore, independent control designs for the actuators have been carried out to assure satisfactory performance of the engine. Additionally, a guidance loop is designed to generate a typical flight trajectory of the representative vehicle using a nonlinear suboptimal input constrained model predictive static programming formulation for testing the performance of the engine. Simulation results clearly indicate quite successful robust performance of the engine during both climb and cruise phases.     

The torque ratio concept for combustion monitoring of internal combustion engines

This work presents a method to analyze combustion events in an internal combustion engine, called the torque ratio concept. The method is based on crankshaft torque measurements, but an extension to angular speed measurements is possible. The torque ratio concept provides a parametrized model for the combustion progress from which, e.g. combustion phasing can be extracted. The torque ratio concept is derived mathematically and related theoretically to other combustion analysis methods, such as pressure ratio and net heat release. Finally, analysis on recorded data from a five cylinder spark ignited engine verifies the relationships between the three methods. For combustion phasing, the 50% torque ratio is an equivalent measure to 50% pressure ratio and can be transformed into the 50% net heat release position by using a derived volume ratio function.     

Neural networks and fuzzy logic-based spark advance control of SI engines

In SI engines, spark advance (SA) needs to be controlled to get Maximum Brake Torque (MBT) timing. Spark advance can be controlled either by open loop or by closed loop controller. The open loop controller requires extensive testing and calibration of engine, to develop look up tables. In closed loop controller, empirical rules relating variables deduced from cylinder pressure are used. One of such empirical rules is to fix location of peak pressure (LPP) at a desired value of the crank angle. In the present work, a combined neural network and fuzzy logic-based control scheme is designed for SA control to get MBT timing. The fuzzy logic controller is designed to maintain LPP of SI engine close to 16° ATDC. The controller works in conjunction with Recurrent Neural Network model for cylinder pressure identification. LPP is estimated from cylinder pressure curve reconstructed using neural network model and is used as feedback signal to fuzzy logic controller. The simulations have been carried out to test the performance of the combined neural network and fuzzy logic-based control strategy. The simulation results show that the proposed strategy can quite satisfactorily control LPP to its desired value.