Development of an open loop fuzzy logic urea dosage controller for use with an SCR equipped HDD engine

Selective Catalytic Reduction (SCR) has been shown to be the most promising exhaust aftertreatment system for reducing oxides of nitrogen in near term in-use applications. SCRs use the ammonia containing compound urea, as a reducing agent. In order to control the urea dosage during transient operation of the engine, sophisticated control strategies are needed. The goal of this study was to design a controller to achieve the maximum NO x emission reduction possible in the transient mode of engine operation, without causing ammonia slip. The development of an open loop, non-sensor based fuzzy logic urea dosage controller is discussed in this thesis. Urea injection values were controlled with \u27maps\u27 based upon the engine speed and engine load, and fuzzy logic was employed as a robust artificial intelligence technique to allow for the development of these maps. Fuzzy logic was utilized to model the complex SCR system and predict the efficiency of NOx conversion. In order to aid in the development of the fuzzy logic SCR model, other methods for generating urea maps were investigated, as well. The first method was an optimization technique, which involved manual testing of the engine to find the optimal urea injection amount. The other method involved injection of urea based upon the average NOx produced. A correction factor was developed and applied to this map to account for losses of ammonia.;The open loop urea map control strategy was implemented without the use of NOx or NH3 sensors. The final fuzzy logic urea map created was able to reduce NOx by 57% over the FTP cycle and 60% over the ETC cycle. This reduction was achieved without causing any significant ammonia slip. The optimized and average NOx urea maps reduced NO x by 67% and 66% over the FTP cycle, but also resulted in large peaks of ammonia slip during the LAFY section. The average NH3 slip seen during the FTP was less than 10 ppm, which was deemed acceptable. The optimized map was also used on the ETC cycle and NOx was reduced by 65% with no significant NH3 slip. The urea maps created for this study appeared to be cycle independent and could be used to control NOx emissions for any transient mode of engine operation

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

Combustion analysis of a CI engine performance using waste cooking biodiesel fuel with an artificial neural network aid

[Abstract]: A comprehensive combustion analysis has been conducted to evaluate the performance of a commercial DI engine, water cooled two cylinders, in-line, naturally aspirated, RD270 Ruggerini diesel engine using waste vegetable cooking oil as an alternative fuel. In order to compare the brake power and the torques values of the engine, it has been tested under same operating conditions with diesel fuel and waste cooking biodiesel fuel blends. The results were found to be very comparable. The properties of biodiesel produced from waste vegetable oil was measured based on ASTM standards. The total sulfur content of the produced biodiesel fuel was 18 ppm which is 28 times lesser than the existing diesel fuel sulfur content used in the diesel vehicles operating in Tehran city (500 ppm). The maximum power and torque produced using diesel fuel was 18.2 kW and 64.2 Nm at 3200 and 2400 rpm respectively. By adding 20% of waste vegetable oil methyl ester, it was noticed that the maximum power and torque increased by 2.7 and 2.9% respectively, also the concentration of the CO and HC emissions have significantly decreased when biodiesel was used. An artificial neural network (ANN) was developed based on the collected data of this work. Multi layer perceptron network (MLP) was used for nonlinear mapping between the input and the output parameters. Different activation functions and several rules were used to assess the percentage error between the desired and the predicted values. The results showed that the training algorithm of Back Propagation was sufficient enough in predicting the engine torque, specific fuel consumption and exhaust gas components for different engine speeds and different fuel blends ratios. It was found that the R2 (R: the coefficient of determination) values are 0.99994, 1, 1 and 0.99998 for the engine torque, specific fuel consumption,CO and HC emissions, respectively

Fuzzy Modelling and Control of the Air System of a Diesel Engine

This paper proposes a fuzzy modelling approach oriented to the design of a fuzzy controller for regulating the fresh airflow of a real diesel engine. This strategy has been suggested for enhancing the regulator design that could represent an alternative to the standard embedded BOSCH controller, already implemented in the Engine Control Unit (ECU), without any change to the engine instrumentation. The air system controller project requires the knowledge of a dynamic model of the diesel engine, which is achieved by means of the suggested fuzzy modelling and identification scheme. On the other hand, the proposed fuzzy PI controller structure is straightforward and easy to implement with respect to different strategies proposed in literature. The results obtained with the designed fuzzy controller are compared to those of the traditional embedded BOSCH controller

Urban and extra-urban hybrid vehicles: a technological review

Pollution derived from transportation systems is a worldwide, timelier issue than ever. The abatement actions of harmful substances in the air are on the agenda and they are necessary today to safeguard our welfare and that of the planet. Environmental pollution in large cities is approximately 20% due to the transportation system. In addition, private traffic contributes greatly to city pollution. Further, “vehicle operating life” is most often exceeded and vehicle emissions do not comply with European antipollution standards. It becomes mandatory to find a solution that respects the environment and, realize an appropriate transportation service to the customers. New technologies related to hybrid –electric engines are making great strides in reducing emissions, and the funds allocated by public authorities should be addressed. In addition, the use (implementation) of new technologies is also convenient from an economic point of view. In fact, by implementing the use of hybrid vehicles, fuel consumption can be reduced. The different hybrid configurations presented refer to such a series architecture, developed by the researchers and Research and Development groups. Regarding energy flows, different strategy logic or vehicle management units have been illustrated. Various configurations and vehicles were studied by simulating different driving cycles, both European approval and homologation and customer ones (typically municipal and university). The simulations have provided guidance on the optimal proposed configuration and information on the component to be used

Transient modelling of a diesel engine and air-path control

Due to the inherent nonlinearity of the diesel engine, real-time control of the variable geometry turbocharger (VGT) and exhaust gas recirculation (EGR) valve still remains a challenging task. A controller has to be capable of coping with the transient operating condition of the engine, the interactions between the VGT and EGR, and also the trade-off effect in this control problem. In this work, novel real-time fuzzy logic controllers (RFLC) were developed and tested. Firstly, the proposed controllers were calibrated and validated in a transient diesel engine model which was developed and validated against the Caterpillar 3126B engine test bed located at the University of Sussex. The controllers were then further tested on the engine test bed. Compared to conventional controllers, the proposed controllers can effectively reduce engine emissions as well as fuel consumption. Experimental results show that compared to the baseline engine running on the Nonroad Transient Cycle (NRTC), mean values of the exhaust gas opacity and the nitrogen oxides (NOx) emission production were reduced by 36.8% and 33%, respectively. Instant specific fuel consumption of the RFLC engine was also reduced by up to 50% compared to the baseline engine during the test. Moreover, the proposed fuzzy logic controllers can also reduce development time and cost by avoiding extensive engine mapping of inlet air pressure and flow. When on-line emission measurements were not available, on-board emission predictors were developed and tested to supply the proposed fuzzy logic controller with predictions of soot and NOx production. Alternatively, adaptive neuro fuzzy inference system (ANFIS) controllers, which can learn from fuzzy logic controllers, were developed and tested. In the end, the proposed fuzzy logic controllers were compared with PI controllers using the transient engine model

Development of an Ammonia Reduction After-Treatment Systems for Stoichiometric Natural Gas Engines

Three-way catalyst (TWC) equipped stoichiometric natural gas vehicles have proven to be an effective alternative fuel strategy that shows significant low NOx emissions characteristics. However, recent studies have shown the TWC activity to contribute to elevated levels of tailpipe ammonia (NH 3) emissions. Although a non-regulated pollutant, ammonia is a potent pre-cursor to ambient secondary PM formation. Ammonia is an inevitable byproduct of fuel rich operation that results in lowest NOx slip through the TWC after-treatment system.;The main objective of the study is to develop a passive Ammonia Reduction Catalyst (passive-ARC) based NH3 reduction strategy that results in an overall reduction of ammonia as well as NOx emissions. The study investigated the characteristics of Fe-based and Cu-based zeolites SCR catalysts in storage and desorption of ammonia at high exhaust temperature conditions, that are typical of stoichiometric natural gas engines. Continuous measurements of NOx and NH3 before and after the SCR systems were conducted using a Fourier Transform Infrared Spectrometry (FTIR) gas analyzer. Results of the investigation showed that both, the Fe- and Cu zeolite SCRs adsorbed above 90% of TWC generated NH3 emissions below 350--375 °C SCR temperatures. Desorption or slipping of NH3 was observed at exhaust gas temperatures exceeding 400 °C. In terms of NOx conversions, Fe-zeolite showed efficiency between 50--80% above temperatures of 300--350 °C while Cu-zeolite performed well at lower SCR temperature from 250 °C and above with a conversion efficiency of greater than 50%.;In order to efficiently reduce both NOx and NH3 simultaneously over longer durations it was found that an engine-based air fuel ratio operation strategy for the passive-ARC system must be developed. To this extent, the study extended its objectives to develop an engine-based control strategy that results in stoichiometric ammonia production operation followed by brief lean operation to regenerate the saturated ammonia reduction catalyst using high NOx slip through TWC. The study presents comprehensive results of ammonia storage characteristics of SCRs pertaining to stoichiometric natural gas engine exhaust as well as an advanced engine control strategy approach to simultaneously reduce both NOx and NH3 using an alternating air -fuel ratio approach

Identifying static and dynamic prediction models for NOx emissions with evolving fuzzy systems

Antipollution legislation in automotive internal combustion engines requires active control and prediction of pollutant formation and emissions. Predictive emission models are of great use in the system calibration phase, and also can be integrated for the engine control and on-board diagnosis tasks. In this paper, fuzzy modelling of the NOx emissions of a diesel engine is investigated, which overcomes some drawbacks of pure engine mapping or analytical physical-oriented models. For building up the fuzzy NOx prediction models, the FLEXFIS approach (short for FLEXible Fuzzy Inference Systems) is applied, which automatically extracts an appropriate number of rules and fuzzy sets by an evolving version of vector quantization (eVQ) and estimates the consequent parameters of Takagi-Sugeno fuzzy systems with the local learning approach in order to optimize the least squares functional. The predictive power of the fuzzy NOx prediction models is compared with that one achieved by physical-oriented models based on high-dimensional engine data recorded during steady-state and dynamic engine states.This work was supported by the Upper Austrian Technology and Research Promotion. This publication reflects only the author's view. Furthermore, we acknowledge PSA for providing the engine and partially supporting our investigation. Special thanks are given to PO Calendini, P Gaillard and C. Bares at the Diesel Engine Control Department.Lughofer, E.; Macian Martinez, V.; Guardiola García, C.; Klement, EP. (2011). Identifying static and dynamic prediction models for NOx emissions with evolving fuzzy systems. Applied Soft Computing. 11(2):2487-2500. doi:10.1016/j.asoc.2010.10.004S2487250011

Ionization In Diesel Combustion For On-Board Diagnostics And Engine Control

Diesel engines have been known for their high thermal efficiency and specific power output, but there is concern about engine-out NOx and particulate matter emissions. To meet the current emission standards, advanced diesel engines are fitted with electronically controlled fuel injection systems and sophisticated and expensive after-treatment devices. Further improvements are still needed to meet future goals in better fuel economy and the more stringent emission standards. In order to meet these goals, there is a need for the control of the combustion process to reduce engine-out emissions in real-time and reduce the demand on the after-treatment devices. This requires a signal indicative of the in-cylinder conditions to be fed in the ECU (Engine Control Unit). The most promising sensors in internal combustion engines are the cylinder gas pressure transducer and the combustion produced ion current sensor. Ion current probes have many advantages over pressure transducers because they are less expensive, more rugged, and are sensitive to the in cylinder gas temperature, and the composition of the combustion products. The ion current technique has been used in some SI engines, based on an understanding of the ionization produced from the combustion of a homogeneous charge. This is not the case in diesel engines where different types of flames are produced from the combustion of the heterogeneous mixture. This study investigates in details the characteristics of the ion current signal in diesel engines and its use for combustion diagnostics and feedback control of the engine. Experimental investigations and CFD simulation models are used to understand the characteristics of the ion current signal under different operating conditions. The investigations proved that the ion current signal carry basic information about combustion. 3-D mathematical models developed gave more insight into the distribution of the ionized species in the combustion chamber and enhanced the development of feedback control of the combustion process and enable the engine to autonomously operate properly on fuels of a wide range of physical and chemical properties. In addition, algorithms have been developed to use the signal for on-board diagnostics of different combustion, performance and engine-out emissions parameters