Review of air fuel ratio prediction and control methods

Air pollution is one of main challenging issues nowadays that researchers have been trying to address.The emissions of vehicle engine exhausts are responsible for 50 percent of air pollution. Different types of emissions emit from vehicles including carbon monoxide, hydrocarbons, NOX, and so on. There is a tendency to develop strategies of engine control which work in a fast way. Accomplishing this task will result in a decrease in emissions which coupled with the fuel composition can bring about the best performance of the vehicle engine.Controlling the Air-Fuel Ratio (AFR) is necessary, because the AFR has an enormous impact on the effectiveness of the fuel and reduction of emissions.This paper is aimed at reviewing the recent studies on the prediction and control of the AFR, as a bulk of research works with different approaches, was conducted in this area.These approaches include both classical and modern methods, namely Artificial Neural Networks (ANN), Fuzzy Logic, and Neuro-Fuzzy Systems are described in this paper.The strength and the weakness of individual approaches will be discussed at length

Parameter Identification of Nonlinear System on Combustion Engine Based MVEM using PEM

In four-stroke engine injection system, often called spark ignition (SI) engine, the air-fuel ratio (AFR) is taken from the measurement of lambda sensor in the exhaust. This sensor does not directly describe how much AFR in the combustion chamber due to the large transport delay. Therefore, the lambda sensor is used only as a feedback in AFR control "correction", not as the "main" control. The purpose of this research is to identify the parameters of the non-linear system in SI engines to produce AFR estimator. The AFR estimator is expected to be used as a feedback of the main "AFR" control system. The process of identifying the parameters using the Gauss-Newton method, due to its rapid computation to Achieve convergence, is based on prediction error minimization (PEM). The models of AFR estimator is an open-loop system without a universal exhaust gas oxygen (UEGO) sensors as feedback, called a virtual AFR sensor. The high price of UEGO sensors makes the virtual AFR sensor can be a practical solution to be applied in AFR control. The model in this research is based on the mean value engine models (MVEM) with some modifications. The research dataset was taken from a Hyundai Verna 2002 with the additional UEGO type of lambda sensors. The throttle opening angle (input) is played by stepping on the gas pedal and the signal to the injector (input) is set to a certain quantity to produce the AFR (output) value read by the UEGO sensor. This research produces an open loop estimator model or AFR virtual sensors with normalized root mean square error (NRMSE) = 0.06831 = 6.831%

On the use of neural networks and statistical tools for nonlinear modeling and on-field diagnosis of solid oxide fuel cell stacks

Abstract The paper reports on the activities performed within the European funded project GENIUS to develop black-box models for modeling and diagnosis of solid oxide fuel cell (SOFC) stacks. Two modeling techniques were investigated, i.e. Neural Networks (NNs) and Statistical Tools (STs). The deployment of NNs was twofold: Recurrent Neural Networks (RNNs) and an NN classifier were developed to simulate transient operation of SOFCs and identify some specific faults that may occur in such devices, respectively. On the other hand, STs are based on a stepwise multiple regression. Data for model development were obtained from experiments specifically designed to reach maximal information content. The final aim was to obtain highly general models of SOFC stacks' operation in both transient and steady state. All the developed black-box models exhibited high accuracy and reliability on both training and test data-sets. Moreover, the black-box models were also proven effective in performing real-time monitoring and degradation analysis for different SOFC stack technologies

Advanced Neural Network Based Control for Automotive Engines

This thesis investigates the application of artificial neural networks (NN) in air/fuel ratio (AFR) control of spark ignition(SI) engines. Three advanced neural network based control schemes are proposed: radial basis function(RBF) neural network based feedforward-feedback control scheme, RBF based model predictive control scheme, and diagonal recurrent neural network (DRNN) - based model predictive control scheme. The major objective of these control schemes is to maintain the air/fuel ratio at the stoichiometric value of 14.7 , under varying disturbance and system uncertainty. All the developed methods have been assessed using an engine simulation model built based on a widely used engine model benchmark, mean value engine model (MVEM). Satisfactory control performance in terms of effective regulation and robustness to disturbance and system component change have been achieved. In the feedforward-feedback control scheme, a neural network model is used to predict air mass flow from system measurements. Then, the injected fuel is estimated by an inverse NN controller. The simulation results have shown that much improved control performance has been achieved compared with conventional PID control in both transient and steady-state response. A nonlinear model predictive control is developed for AFR control in this re- . search using RBF model. A one-dimensional optimization method, the secant method is employed to obtain optimal control variable in the MPC scheme, so that the computation load and consequently the computation time is greatly reduced. This feature significantly enhances the applicability of the MPC to industrial systems with fast dynamics. Moreover, the RBF model is on-line adapted to model engine time-varying dynamics and parameter uncertainty. As such, the developed control scheme is more robust and this is approved in the evaluation. The MPC strategy is further developed with the RBF model replaced by a DRNN model. The DRNN has structure including a information-storing neurons and is therefore more appropriate for dynamics system modelling than the RBF, a static network. In this research, the dynamic back-propagation algorithm (DBP) is adopted to train the DRNN and is realized by automatic differentiation (AD) technique. This greatly reduces the computation load and time in the model training. The MPC using the DRNN model is found in the simulation evaluation having better control performance than the RBF -based model predictive control. The main contribution of this research lies in the following aspects. A neural network based feedforward-feedback control scheme is developed for AFR of SI engines, which is performed better than traditional look-up table with PI control method. This new method needs moderate computation and therefore has strong potential to be applied in production engines in automotive industry. Furthermore, two adaptive neural network models, a RBF model and a DRNN model, are developed for engine and incorporated into the MPC scheme. Such developed two MPC schemes are proved by simulations having advanced features of low computation load, better regulation performance in both transient and steady state, and stronger robustness to engine time-varying dynamics and parameter uncertainty. Finally, the developed schemes are considered to suit the limited hardware capacity of engine control and have feasibility and strong potential to be practically implemented in the production engines

Smart controller design of air to fuel ratio (AFR) and brake control system on gasoline engine

Development of internal combustion engine control system is currently oriented on exhaust emissions, performance and fuel efficiency. This is caused by fuel prices rising which led to a crisis on the transport sector; therefore it is crucial to develop a fuel-efficient vehicles technology. Gasoline engine fuel efficiency can be improved by several methods such as by controlling the air-to-fuel ratio (AFR). AFR technology currently still has many problems due to its difficulty setting characteristic since AFR control is usually as internally engine control. Fuel efficiency can be improved by influence of external engine system. Brake control system is an external engine system that used in this research. The purpose of this research is to design and implement the AFR and brake control system in a vehicle to improve fuel efficiency of gasoline engines along braking period. The basic idea is the controller has to reduce the consumption of fuel injection along braking period. The applied control system on vehicle works using smart controller, such as Fuzzy Logic Controller (FLC). When the vehicle brakes, fuel injection is controlled by the ECU brake control system. This control system works in parallel with the vehicle control system default. The results show, when the engine speed exceeds 2500 rpm, AFR value increased infinitely, so that maximum efficiency is achieved. At engine speed less than 2500 rpm, AFR value reaches a value of 22. The fuel measurement has been able to show a decrease in fuel consumption of 6 liters to 4 liters within the distance of 50.7 km. Improvement of fuel efficiency can be achieved by approximately of 33.3%

Data driven nonlinear dynamic models for predicting heavy-duty diesel engine torque and combustion emissions

Diesel engines' reliable and durable structures, high torque generation capabilities at low speeds, and fuel consumption efficiencies make them irreplaceable for heavy-duty vehicles in the market. However, ine ciencies in the combustion process result in the release of emissions to the environment. In addition to the restrictive international regulations for emissions, the competitive demands for more powerful engines and increasing fuel prices obligate heavy-duty engine and vehicle manufacturers to seek for solutions to reduce the emissions while meeting the performance requirements. In line with these objectives, remarkable progress has been made in modern diesel engine systems such as air handling, fuel injection, combustion, and after-treatment. However, such systems utilize quite sophisticated equipment with a large number of calibratable parameters that increases the experimentation time and effort to find the optimal operating points. Therefore, a dynamic model-based transient calibration is required for an e cient combustion optimization which obeys the emission limits, and meets the desired power and efficiency requirements. This thesis is about developing optimizationoriented high delity nonlinear dynamic models for predicting heavy-duty diesel engine torque and combustion emissions. Contributions of the thesis are: (i) A new design of experiments is proposed where air-path and fuel-path input channels are excited by chirp signals with varying frequency pro les in terms of the number and directions of the sweeps. The proposed approach is a strong alternative to the steady-state experiment based approaches to reduce the testing time considerably and improve the modeling accuracy in both steady-state and transient conditions. (ii) A nonlinear nite impulse response (NFIR) model is developed to predict indicated torque by including the estimations of friction, pumping and inertia torques in addition to the torque measured from the engine dynamometer. (iii) Two different nonlinear autoregressive with exogenous input (NARX) models are proposed to predict NOx emissions. In the first structure, input regressor set for the nonlinear part of the model is reduced by an orthogonal least square (OLS) algorithm to increase the robustness and decrease the sensitivity to parameter changes, and linear output feedback is employed. In the second structure, only the previous output is used as the output regressor in the model due to the stability considerations. (iv) An analysis of model sensitivities to parameter changes is conducted and an easy-tointerpret map is introduced to select the best modeling parameters with limited testing time in powertrain development. (v) Soot (particulated matter) emission is predicted using LSTM type networks which provide more accurate and smoother predictions than NARX models. Experimental results obtained from the engine dynamometer tests show the e ectiveness of the proposed models in terms of prediction accuracies in both NEDC (New European Driving Cycle) and WHTC (World Harmonized Transient Cycle) cycle

Aeronautical engineering: A continuing bibliography with indexes (supplement 278)

This bibliography lists 414 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1992

Development of solid oxide fuel cell stack models for monitoring, diagnosis and control applications

2011 - 2012In the present thesis different SOFC stack models have been presented. The results shown were obtained in the general framework of the GENIUS project (GEneric diagNosis Instrument for SOFC systems), funded by the European Union (grant agreement n° 245128). The objective of the project is to develop “generic” diagnostic tools and methodologies for SOFC systems. The “generic” term refers to the flexibility of diagnosis tools to be adapted to different SOFC systems. In order to achieve the target of the project and to develop stack models suitable for monitoring, control and diagnosis applications for SOFC systems, different modeling approaches have been proposed. Particular attention was given to their implementability into computational tools for on-board use. In this thesis one-dimensional (1-D), grey-box and blackbox stack models, both stationary and dynamic were developed. The models were validated with experimental data provided by European partners in the frame of the GENIUS project. A 1-D stationary model of a planar SOFC in co-flow and counter-flow configurations was presented. The model was developed starting from a 1- D model proposed by the University of Salerno for co-flow configuration (Sorrentino, 2006). The model was cross-validated with similar models developed by the University of Genoa and by the institute VTT. The crossvalidation results underlined the suitability of the 1-D model developed. A possible application of the 1-D model for the estimation of stack degradation was presented. The results confirmed the possibility to implement such a model for fault detection. A lumped gray-box model for the simulation of TOPSOE stack thermal dynamics was developed for the SOFC stack of TOPSOE, whose experimental data were made available in the frame of the GENIUS project. Particular attention was given to the problem of heat flows between stack and surrounding and a dedicated model was proposed. The black-box approach followed for the implementation of the heat flows and its reliability and accuracy was shown to be satisfactory for the purpose of its applications. The procedure adopted turned out to be fast and applicable to other SOFC stacks with different geometries and materials. The good results obtained and the limited calculation time make this model suitable for implementation in diagnostic tools. Another field of application is that of virtual sensors for stack temperature control. Black-box models for SOFC stack were also developed. In particular, a stationary Neural Network for the simulation of the HEXIS stack voltage was developed. The analyzed system was a 5-cells stack operated up to 10 thousand hours at constant load. The neural network exhibited very good prediction accuracy, even for systems with different technology from the one used for training the model. Beyond showing excellent prediction capabilities, the NN ensured high accuracy in well reproducing evolution of degradation in SOFC stacks, especially thanks to the inclusion of time among model inputs. Moreover, a Recurrent Neural Network for dynamic simulation of TOPSOE stack voltage and a similar one for a short stack built by HTc and tested by VTT were developed. The stacks analyzed were: a planar co-flow SOFC stack (TOPSOE) and a planar counter-flow SOFC stack (VTT-HTc). All models developed in this thesis have shown high accuracy and computation times that allow them to be implemented into diagnostic and control tool both for off-line (1-D model and grey-box) and for on-line (NN and RNNs) applications. It is important noting that the models were developed with reference to stacks produced by different companies. This allowed the evaluation of different SOFC technologies, thus obtaining useful information in the models development. The information underlined the critical aspects of these systems with regard to the measurements and control of some system variables, giving indications for the stack models development. The proposed modeling approaches are good candidates to address emerging needs in fuel cell development and on-field deployment, such as the opportunity of developing versatile model-based tools capable to be generic enough for real-time control and diagnosis of different fuel cell systems typologies, technologies and power scales. [edited by author]XI n.s