Yes / The natural gas as an alternative fuel has economical and environmental benefits. Bi-fuel engines powered by gasoline and compressed natural gas (CNG) are an intermediate and alternative step to dedicated CNG engines. The conversion to bi-fuel CNG engine could be a short-term solution to air pollution problem in many developing countries. In this paper a mathematical model of a bi-fuel four-stroke spark ignition (SI) engine is presented for comparative studies and analysis. It is based on the two-zone combustion model, and it has the ability to simulate turbulent combustion. The model is capable of predicting the cylinder temperature and pressure, heat transfer, brake work , brake thermal and volumetric efficiency, brake torque, brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), concentration of CO2, brake specific CO (BSCO) and brake specific NOx (BSNOx). The effect of engine speed, equivalence ratio and performance parameters using gasoline and CNG fuels are analysed. The model has been validated by experimental data using the results obtained from a bi-fuel engine. The results show the capability of the model in terms of engine performance optimization and minimization of the emissions. The engine used in this study is a typical example of a modified bi-fuel engine conversion, which could benefit the researchers in the field.

No / Since the beginning of time, energy has pervaded our earth. We rely on it to advance in any development. As the energy sources become scarcer, it is important to learn how to save and economize energy. A perfect energy should be cheap and efficient. Bi-Fuel system is such a concept, which combines the best of Diesel and Gas driven engines. Diesel driven engines though provide high power density but own the drawback of high cost and high on-site fuel storage. Gas driven engines provide low cost but own the drawback of low power density. A Bi-Fuel system is Compression ignited engine, which runs on the simultaneous combustion of Diesel and Natural gas. It works by introducing gas to the engine via various technologies and then electronically controlling flow dependent on output. This greatly extends the runtimes and limits the amount of diesel fuel that must be stored on site.

In this thesis, exergy based SI engine model optimisation (EBSIEMO) is studied and evaluated. A four-stroke bi-fuel spark ignition (SI) engine is modelled for optimisation of engine performance based upon exergy analysis. An artificial neural network (ANN) is used as an emulator to speed up the optimisation processes. Constrained particle swarm optimisation (CPSO) is employed to identify parameters such as equivalence ratio and ignition time for optimising of the engine performance, based upon maximising 'total availability'. In the optimisation process, the engine exhaust gases standard emission were applied including brake specific CO (BSCO) and brake specific NOx (BSNOx) as the constraints. The engine model is developed in a two-zone model, while considering the chemical synthesis of fuel, including 10 chemical species. A computer code is developed in MATLAB software to solve the equations for the prediction of temperature and pressure of the mixture in each stage (compression stroke, combustion process and expansion stroke). In addition, Intake and exhaust processes are calculated using an approximation method. This model has the ability to simulate turbulent combustion and compared to computational fluid dynamic (CFD) models it is computationally faster and efficient. The selective outputs are cylinder temperature and pressure, heat transfer, brake work, brake thermal and volumetric efficiency, brake torque, brake power (BP), brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), concentration of CO2, brake specific CO (BSCO) and brake specific NOx (BSNOx). In this model, the effect of engine speed, equivalence ratio and ignition time on performance parameters using gasoline and CNG fuels are analysed. In addition, the model is validated by experimental data using the results obtained from bi-fuel engine tests. Therefore, this engine model was capable to predict, analyse and useful for optimisation of the engine performance parameters. The exergy based four-stroke bi-fuel (CNG and gasoline) spark ignition (SI) engine model (EBSIEM) here is used for analysis of bi-fuel SI engines. Since, the first law of thermodynamic (the FLT), alone is not able to afford an appropriate comprehension into engine operations. Therefore, this thesis concentrates on the SI engine operation investigation using the developed engine model by the second law of thermodynamic (the SLT) or exergy analysis outlook (exergy based SI engine model (EBSIEM)) In this thesis, an efficient approach is presented for the prediction of total availability, brake specific CO (BSCO), brake specific NOx (BSNOx) and brake torque for bi-fuel engine (CNG and gasoline) using an artificial neural network (ANN) model based on exergy based SI engine (EBSIEM) (ANN-EBSIEM) as an emulator to speed up the optimisation processes. In the other words, the use of a well trained an ANN is ordinarily much faster than mathematical models or conventional simulation programs for prediction. The constrained particle swarm optimisation (CPSO)-EBSIEM (EBSIEMO) was capable of optimising the model parameters for the engine performance. The optimisation results based upon availability analysis (the SLT) due to analysing availability terms, specifically availability destruction (that measured engine irreversibilties) are more regarded with higher priority compared to the FLT analysis. In this thesis, exergy based SI engine model optimisation (EBSIEMO) is studied and evaluated. A four-stroke bi-fuel spark ignition (SI) engine is modelled for optimisation of engine performance based upon exergy analysis. An artificial neural network (ANN) is used as an emulator to speed up the optimisation processes. Constrained particle swarm optimisation (CPSO) is employed to identify parameters such as equivalence ratio and ignition time for optimising of the engine performance, based upon maximising 'total availability'. In the optimisation process, the engine exhaust gases standard emission were applied including brake specific CO (BSCO) and brake specific NOx (BSNOx) as the constraints. The engine model is developed in a two-zone model, while considering the chemical synthesis of fuel, including 10 chemical species. A computer code is developed in MATLAB software to solve the equations for the prediction of temperature and pressure of the mixture in each stage (compression stroke, combustion process and expansion stroke). In addition, Intake and exhaust processes are calculated using an approximation method. This model has the ability to simulate turbulent combustion and compared to computational fluid dynamic (CFD) models it is computationally faster and efficient. The selective outputs are cylinder temperature and pressure, heat transfer, brake work, brake thermal and volumetric efficiency, brake torque, brake power (BP), brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), concentration of CO2, brake specific CO (BSCO) and brake specific NOx (BSNOx). In this model, the effect of engine speed, equivalence ratio and ignition time on performance parameters using gasoline and CNG fuels are analysed. In addition, the model is validated by experimental data using the results obtained from bi-fuel engine tests. Therefore, this engine model was capable to predict, analyse and useful for optimisation of the engine performance parameters. The exergy based four-stroke bi-fuel (CNG and gasoline) spark ignition (SI) engine model (EBSIEM) here is used for analysis of bi-fuel SI engines. Since, the first law of thermodynamic (the FLT), alone is not able to afford an appropriate comprehension into engine operations. Therefore, this thesis concentrates on the SI engine operation investigation using the developed engine model by the second law of thermodynamic (the SLT) or exergy analysis outlook (exergy based SI engine model (EBSIEM)) In this thesis, an efficient approach is presented for the prediction of total availability, brake specific CO (BSCO), brake specific NOx (BSNOx) and brake torque for bi-fuel engine (CNG and gasoline) using an artificial neural network (ANN) model based on exergy based SI engine (EBSIEM) (ANN-EBSIEM) as an emulator to speed up the optimisation processes. In the other words, the use of a well trained an ANN is ordinarily much faster than mathematical models or conventional simulation programs for prediction. The constrained particle swarm optimisation (CPSO)-EBSIEM (EBSIEMO) was capable of optimising the model parameters for the engine performance. The optimisation results based upon availability analysis (the SLT) due to analysing availability terms, specifically availability destruction (that measured engine irreversibilties) are more regarded with higher priority compared to the FLT analysis.

A modelagem de motores a combustão interna deve grande parte de sua importância ao uso de unidades de controle eletrônicas que buscam gerenciar as funções do motor. De forma a fornecer melhor suporte para o projetista de controle, a modelagem oferece informações que servem de planta, sobre a qual estratégias de controle serão desenvolvidas. Nesta dissertação, procurou-se estudar e modelar cinco fenômenos: a admissão de ar e de combustível, a produção de energia efetiva através da combustão, a evolução térmica do motor e o comportamento dos gases no sistema de exaustão. Investigou-se também, em todos estes fenômenos, a influência do uso de composição variada gasolina/etanol. Na admissão de ar, buscou-se estudar como a abertura da válvula borboleta e a velocidade do motor influenciam no fluxo de ar admitido, ponderando esta grandeza através de um fator de correção denominado eficiência volumétrica. Na admissão de combustível, no caso modelada para motores com injeção indireta na porta, procurou-se explanar quantitativamente sobre os diversos aspectos que influenciam a evaporação do combustível. Na geração de energia útil, priorizou-se a análise de como as características do motor e da combustão afetam a produção de torque. Na evolução térmica do motor, examinaram-se os principais fluxos energéticos do motor e os aspectos que os influenciam. Ademais, foram executadas as validações dos modelos levantados para o motor EA 111 VHT 1.6l. Os resultados, com seus respectivos erros, podem ser encontrados neste trabalho. / The internal combustion engine modeling owes big part of its importance to the use of electronic control units that aim to manage the engine functions. To provide better support to the control designer, the modeling offers information that can compose the plant, on which control strategies will be developed. In this master thesis, it was sought to study and to model five phenomena: the air intake and the fuel admission, the effective energy production from the combustion, the engine thermic evolution and the gas behavior in the exhaust system. It was also considered how the influence of the gasoline/ethanol varied composition affects all these phenomena. In the air intake, it was studied how the butterfly valve opening and the engine speed influence the intake air flow, pondering this variable through a correction factor named volumetric efficiency. In the fuel admission, in the case of this study modelled for port-fuel injection engines, it was attempted to explain quantitatively the many aspects that influence the fuel evaporation. In the mechanical energy generation, it was prioritized the analysis about how the engine and combustion characteristics affect the torque production. In the engine thermic evolution, it was examined the major energy flows and the aspects that influence them. Also, the validations of the models raised for the EA 111 VHT 1.6l engine were executed. The results, with its respective errors, can be found in this work.

A indústria automobilística é um dos mais importantes setores da economia no Brasil e no mundo. Nos últimos anos viu-se praticamente obrigada a melhorar o desempenho de seus veículos produzidos e reduzir seus custos. Um dos marcos desta transformação foi o desenvolvimento do sensor de oxigênio, sendo este um dos principais elementos dos sistemas gerenciadores de motor. Esta dissertação propõe o estudo de arquiteturas de sistemas inteligentes para sensoriamento virtual de oxigênio em veículos bicombustíveis, utilizando-se redes neurais artificiais supervisionadas, com arquitetura Perceptron multicamadas. As topologias implementadas atingiram resultados com erros relativos médios menores que 1% em centenas de topologias. Verificou-se também que para o sensoriamento virtual de oxigênio em veículos bicombustíveis, a abordagem de se realizar treinamentos com todos os tipos de combustíveis, segmentando conjuntos de todo o universo de dados, mostra-se a mais adequada. / The automotive industry is one of the most important sectors in Brazilians economy and in the world. In recent years, this industry has been forced to improve the performance of their produced vehicles and to reduce their costs. One of the landmarks of this transformation was the development of the oxygen sensor, which is one of the main elements of the engine management systems. This dissertation proposes the use of intelligent systems architectures for virtual oxygen sensing of bi-fuel vehicles, using multilayer Perceptron artificial neural networks. The implemented topologies reach results with mean relative errors less than 1% in hundreds of topologies. It was also noted that the approach to train the neural network with all types of fuels, using subsets of data universe, it is the most appropriate to have a virtual sensing of oxygen in bi-fuel vehicles.

A modelagem de motores a combustão interna deve grande parte de sua importância ao uso de unidades de controle eletrônicas que buscam gerenciar as funções do motor. De forma a fornecer melhor suporte para o projetista de controle, a modelagem oferece informações que servem de planta, sobre a qual estratégias de controle serão desenvolvidas. Nesta dissertação, procurou-se estudar e modelar cinco fenômenos: a admissão de ar e de combustível, a produção de energia efetiva através da combustão, a evolução térmica do motor e o comportamento dos gases no sistema de exaustão. Investigou-se também, em todos estes fenômenos, a influência do uso de composição variada gasolina/etanol. Na admissão de ar, buscou-se estudar como a abertura da válvula borboleta e a velocidade do motor influenciam no fluxo de ar admitido, ponderando esta grandeza através de um fator de correção denominado eficiência volumétrica. Na admissão de combustível, no caso modelada para motores com injeção indireta na porta, procurou-se explanar quantitativamente sobre os diversos aspectos que influenciam a evaporação do combustível. Na geração de energia útil, priorizou-se a análise de como as características do motor e da combustão afetam a produção de torque. Na evolução térmica do motor, examinaram-se os principais fluxos energéticos do motor e os aspectos que os influenciam. Ademais, foram executadas as validações dos modelos levantados para o motor EA 111 VHT 1.6l. Os resultados, com seus respectivos erros, podem ser encontrados neste trabalho. / The internal combustion engine modeling owes big part of its importance to the use of electronic control units that aim to manage the engine functions. To provide better support to the control designer, the modeling offers information that can compose the plant, on which control strategies will be developed. In this master thesis, it was sought to study and to model five phenomena: the air intake and the fuel admission, the effective energy production from the combustion, the engine thermic evolution and the gas behavior in the exhaust system. It was also considered how the influence of the gasoline/ethanol varied composition affects all these phenomena. In the air intake, it was studied how the butterfly valve opening and the engine speed influence the intake air flow, pondering this variable through a correction factor named volumetric efficiency. In the fuel admission, in the case of this study modelled for port-fuel injection engines, it was attempted to explain quantitatively the many aspects that influence the fuel evaporation. In the mechanical energy generation, it was prioritized the analysis about how the engine and combustion characteristics affect the torque production. In the engine thermic evolution, it was examined the major energy flows and the aspects that influence them. Also, the validations of the models raised for the EA 111 VHT 1.6l engine were executed. The results, with its respective errors, can be found in this work.

A indústria automobilística é um dos mais importantes setores da economia no Brasil e no mundo. Nos últimos anos viu-se praticamente obrigada a melhorar o desempenho de seus veículos produzidos e reduzir seus custos. Um dos marcos desta transformação foi o desenvolvimento do sensor de oxigênio, sendo este um dos principais elementos dos sistemas gerenciadores de motor. Esta dissertação propõe o estudo de arquiteturas de sistemas inteligentes para sensoriamento virtual de oxigênio em veículos bicombustíveis, utilizando-se redes neurais artificiais supervisionadas, com arquitetura Perceptron multicamadas. As topologias implementadas atingiram resultados com erros relativos médios menores que 1% em centenas de topologias. Verificou-se também que para o sensoriamento virtual de oxigênio em veículos bicombustíveis, a abordagem de se realizar treinamentos com todos os tipos de combustíveis, segmentando conjuntos de todo o universo de dados, mostra-se a mais adequada. / The automotive industry is one of the most important sectors in Brazilians economy and in the world. In recent years, this industry has been forced to improve the performance of their produced vehicles and to reduce their costs. One of the landmarks of this transformation was the development of the oxygen sensor, which is one of the main elements of the engine management systems. This dissertation proposes the use of intelligent systems architectures for virtual oxygen sensing of bi-fuel vehicles, using multilayer Perceptron artificial neural networks. The implemented topologies reach results with mean relative errors less than 1% in hundreds of topologies. It was also noted that the approach to train the neural network with all types of fuels, using subsets of data universe, it is the most appropriate to have a virtual sensing of oxygen in bi-fuel vehicles.

A foreseeable shortage of petroleum, associated to a growing ecological conscience, demand for alternative sources of energy and more efficient and less pollutant combustion processes. Among the few pollutant fuels this work approaches the combination of natural gas, whose consumption has been increasing year to year, and diesel. It is known that the internal combustion engines convert energy with low efficiency. Based on that, this work evaluates a bi-fuel Diesel engine, power by Diesel and natural gas as means of improving its efficiency. In the engine used as a prototype, the main energy comes from the combustion of natural gas. Being the gas the main fuel, the Diesel is used only to generate the pilot explosion for the combustion process. In this way, the diesel oil is partially substituted by natural gas, increasing the combustion efficiency. Initially it was made a study on the use of the natural gas in Diesel engine through a bibliographical revision. Therefore after, they were certain the parameters that should be monitored to develop an appropriate controller. It was verified that should be appraised the engine rotation and the injection angle. The performance aimed for the action of the loop control should be the rotation of the engine. The more appropriate control techniques were investigated for the management of the natural gas injection. When analyzing the traditional techniques it observed that they present some disadvantages as the mathematical complexity, difficulties in adapt the motor to the everchanging conditions of the motor with time/temperature, limitations in the grade of controller performance and complications for practical implementation on the part of non-specialized operators. To optimize the volume of natural gas supplied to the engine an electronic manager was developed for injection of this fuel. This electronic controller is based on an adaptive fuzzy algorithm to regulate the rate of injection of fuel, which was implemented through a microcontroller. The electronic injection system controls the timing of fuel injection, so managing the volume of gas supplied to each injection cycle. The injection angle is also accurately monitored by the control system. This topology, with few alterations, can be used in any Diesel engine operating in the bifuel mode. Results of this dissertation should contribute to increase the efficiency of Diesel engine as well as reduce the consumption of fuel and emission of pollutants. / Uma previsível escassez de petróleo, aliada a uma crescente consciência ecológica, tem levado pesquisadores a procurar fontes alternativas de energia e processos de combustão mais eficientes e menos poluentes. Entre os combustíveis pouco poluentes este trabalho aborda o uso do gás natural, cujo consumo tem aumentado ano a ano. É sabido que os motores de combustão interna convertem energia com baixa eficiência. Com base nisto, este trabalho avalia um motor Diesel, bi-combustível, movido a Diesel e gás natural como forma de encontrar meios de melhorar sua eficiência. No motor usado como protótipo, nessa dissertação a energia origina-se da combustão do gás natural. Sendo o gás o combustível principal, o Diesel presta-se apenas à geração da chama piloto para o processo de combustão. Assim, substitui-se parcialmente o óleo Diesel por gás natural, aumentando o rendimento da combustão. Inicialmente procurou-se estudar o uso do gás natural em motores Diesel através de uma revisão bibliográfica. Em seguida, determinaram-se quais os parâmetros que seriam monitorados a fim de se desenvolver um controlador adequado. Verificou-se que deveriam ser avaliados a rotação do motor e o ângulo de injeção. A performance almejada para a ação da malha de controle deve ser a rotação do motor. Investigaram-se as técnicas de controle mais apropriadas para o gerenciamento da injeção de gás natural. Ao se analisarem as técnicas tradicionais observou-se que estas apresentam algumas desvantagens como a complexidade matemática, limitações na faixa de atuação do controlador, dificuldades de adaptação às condições do motor sempre variáveis com o tempo/temperatura e complicações para implementação prática por parte de operadores não-especializados. Para otimizar o volume de gás natural fornecido ao motor foi desenvolvido um gerenciador eletrônico para injeção deste combustível. Este controlador eletrônico baseia-se em um algoritmo nebuloso para regular a taxa de injeção de combustível implementado através de um microcontrolador. O sistema de injeção eletrônica controla o tempo de injeção do combustível, gerenciando assim o volume de gás fornecido a cada ciclo de injeção. O ângulo de injeção, também monitorado com precisão pelo sistema, é sincronizado com o eixo de comando de válvulas e, tomando-se como referência de posição angular o ponto morto superior do primeiro cilindro. Com poucas alterações, esta topologia, pode ser usada em qualquer motor Diesel que opere no regime bi-combustível. Os resultados desta dissertação devem contribuir para o aumento da eficiência do motor bem como redução do consumo de combustível e emissão de poluentes.

In this thesis, exergy based SI engine model optimisation (EBSIEMO) is studied and evaluated. A four-stroke bi-fuel spark ignition (SI) engine is modelled for optimisation of engine performance based upon exergy analysis. An artificial neural network (ANN) is used as an emulator to speed up the optimisation processes. Constrained particle swarm optimisation (CPSO) is employed to identify parameters such as equivalence ratio and ignition time for optimising of the engine performance, based upon maximising ¿total availability¿. In the optimisation process, the engine exhaust gases standard emission were applied including brake specific CO (BSCO) and brake specific NOx (BSNOx) as the constraints. The engine model is developed in a two-zone model, while considering the chemical synthesis of fuel, including 10 chemical species. A computer code is developed in MATLAB software to solve the equations for the prediction of temperature and pressure of the mixture in each stage (compression stroke, combustion process and expansion stroke). In addition, Intake and exhaust processes are calculated using an approximation method. This model has the ability to simulate turbulent combustion and compared to computational fluid dynamic (CFD) models it is computationally faster and efficient. The selective outputs are cylinder temperature and pressure, heat transfer, brake work, brake thermal and volumetric efficiency, brake torque, brake power (BP), brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), concentration of CO2, brake specific CO (BSCO) and brake specific NOx (BSNOx). In this model, the effect of engine speed, equivalence ratio and ignition time on performance parameters using gasoline and CNG fuels are analysed. In addition, the model is validated by experimental data using the results obtained from bi-fuel engine tests. Therefore, this engine model was capable to predict, analyse and useful for optimisation of the engine performance parameters. The exergy based four-stroke bi-fuel (CNG and gasoline) spark ignition (SI) engine model (EBSIEM) here is used for analysis of bi-fuel SI engines. Since, the first law of thermodynamic (the FLT), alone is not able to afford an appropriate comprehension into engine operations. Therefore, this thesis concentrates on the SI engine operation investigation using the developed engine model by the second law of thermodynamic (the SLT) or exergy analysis outlook (exergy based SI engine model (EBSIEM)) In this thesis, an efficient approach is presented for the prediction of total availability, brake specific CO (BSCO), brake specific NOx (BSNOx) and brake torque for bi-fuel engine (CNG and gasoline) using an artificial neural network (ANN) model based on exergy based SI engine (EBSIEM) (ANN-EBSIEM) as an emulator to speed up the optimisation processes. In the other words, the use of a well trained an ANN is ordinarily much faster than mathematical models or conventional simulation programs for prediction. The constrained particle swarm optimisation (CPSO)-EBSIEM (EBSIEMO) was capable of optimising the model parameters for the engine performance. The optimisation results based upon availability analysis (the SLT) due to analysing availability terms, specifically availability destruction (that measured engine irreversibilties) are more regarded with higher priority compared to the FLT analysis. In this thesis, exergy based SI engine model optimisation (EBSIEMO) is studied and evaluated. A four-stroke bi-fuel spark ignition (SI) engine is modelled for optimisation of engine performance based upon exergy analysis. An artificial neural network (ANN) is used as an emulator to speed up the optimisation processes. Constrained particle swarm optimisation (CPSO) is employed to identify parameters such as equivalence ratio and ignition time for optimising of the engine performance, based upon maximising ¿total availability¿. In the optimisation process, the engine exhaust gases standard emission were applied including brake specific CO (BSCO) and brake specific NOx (BSNOx) as the constraints. The engine model is developed in a two-zone model, while considering the chemical synthesis of fuel, including 10 chemical species. A computer code is developed in MATLAB software to solve the equations for the prediction of temperature and pressure of the mixture in each stage (compression stroke, combustion process and expansion stroke). In addition, Intake and exhaust processes are calculated using an approximation method. This model has the ability to simulate turbulent combustion and compared to computational fluid dynamic (CFD) models it is computationally faster and efficient. The selective outputs are cylinder temperature and pressure, heat transfer, brake work, brake thermal and volumetric efficiency, brake torque, brake power (BP), brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), concentration of CO2, brake specific CO (BSCO) and brake specific NOx (BSNOx). In this model, the effect of engine speed, equivalence ratio and ignition time on performance parameters using gasoline and CNG fuels are analysed. In addition, the model is validated by experimental data using the results obtained from bi-fuel engine tests. Therefore, this engine model was capable to predict, analyse and useful for optimisation of the engine performance parameters. The exergy based four-stroke bi-fuel (CNG and gasoline) spark ignition (SI) engine model (EBSIEM) here is used for analysis of bi-fuel SI engines. Since, the first law of thermodynamic (the FLT), alone is not able to afford an appropriate comprehension into engine operations. Therefore, this thesis concentrates on the SI engine operation investigation using the developed engine model by the second law of thermodynamic (the SLT) or exergy analysis outlook (exergy based SI engine model (EBSIEM)) In this thesis, an efficient approach is presented for the prediction of total availability, brake specific CO (BSCO), brake specific NOx (BSNOx) and brake torque for bi-fuel engine (CNG and gasoline) using an artificial neural network (ANN) model based on exergy based SI engine (EBSIEM) (ANN-EBSIEM) as an emulator to speed up the optimisation processes. In the other words, the use of a well trained an ANN is ordinarily much faster than mathematical models or conventional simulation programs for prediction. The constrained particle swarm optimisation (CPSO)-EBSIEM (EBSIEMO) was capable of optimising the model parameters for the engine performance. The optimisation results based upon availability analysis (the SLT) due to analysing availability terms, specifically availability destruction (that measured engine irreversibilties) are more regarded with higher priority compared to the FLT analysis.

No presente trabalho, emprega-se a medida da concentração de etanol do combustível para efetuar correções estequiométricas de maneira direta e instantânea, a fim de eliminar o período necessário para adaptação a partir da medida do sensor de oxigênio (sonda lambda) em eventos de reabastecimento no veículo. Com o objetivo de assegurar a operação flex-fuel, foram empregados métodos para a regulação ar/combustível em malha fechada, realimentados por sensores de oxigênio (banda larga amplificada e banda estreita). O projeto foi implementado em uma ECU (Eletronic Control Unit) idealizada para desenvolvimento de rotinas de programa voltadas ao gerenciamento eletrônico para motores de combustão interna ciclo Otto, denominada Flex-ECU. A ETAS/Bosch Flex-ECU possui programação aplicada à ferramenta ASCET (Advanced Simulation and Control Engineering Tool), o qual trata-se de um código open source para sistemas embarcados de tempo real. Por fim, são exibidos resultados de controle, desempenho e eficiência do motor para diferentes composições de combustível comercializados para a frota de veículos leves em território nacional. Os experimentos revelam a dinâmica de funcionamento do controle A/C bicombustível e discute as suas principais características, com o objetivo de exemplificar métodos de otimização de sua eficiência. / In the present work, the ethanol fuel concentration is used to establish stoichiometric corrections in a direct and instantaneous manner, to eliminate the period necessary for adaptation, from the measurement of the oxygen sensor (lambda probe) in events of refueling. To ensure Flex-fuel operation, closed-loop air/fuel regulation methods were used, fed by oxygen sensors (amplified wide band and narrow band). The project was implemented in an ECU (Electronic Control Unit) designed for the development of code routines for electronic management of an Otto cycle internal combustion engine, labeled Flex-ECU. The ETAS / Bosch Flex-ECU has programming applied to the ASCET (Advanced Simulation and Control Engineering Tool) tool, which is an open source code for real time embedded systems. Finally, results of engine control, performance, and efficiency are presented for different fuel compositions available in Brazil for the fleet of light vehicles. The experiments show the dynamics of the operation of the bi-fuel A/C control and discusses its main characteristics, aiming to exemplify optimization methods of its efficiency.