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%

Analysis of the Performance of Different Machine Learning Techniques for the Definition of Rule-based Control Strategies in a Parallel HEV

Abstract Two different machine-learning techniques have been assessed and applied to define rule-based control strategies for a parallel hybrid midsize sport utility vehicle equipped with a diesel engine. Both methods include two phases: a clustering algorithm and a rule definition. In the first method, a homemade clustering algorithm is preliminarily run to generate the set of clusters, while the rules are identified by minimizing an objective function. In the second method, a genetic algorithm provides the optimal size of the clusters, while the associated rules are extracted from the results obtained with a benchmark optimizer. The controllers were tested over NEDC, 1015, AMDC and WLTP

An optimal fuzzy logic power sharing strategy for Parallel Hybrid Electric Vehicles

International audienceVehicle emission reduction has been a research objective for many years, by improving fuel economy and energy efficiency. Therefore, this paper presents a fuzzy logic controller for a Parallel Hybrid Electric Vehicle (PHEV). The PHEV required driving torque is generated by a combined contribution from an Internal Combustion Engine (ICE) and an Induction Motor (IM). The proposed Fuzzy Logic Controller (FLC) is designed based on the desired driving torque and the batteries State of Charge (SoC) with the objective to minimize fuel consumption and emissions, while enhancing or maintaining the PHEV driving performance characteristics. The fuzzy controller output controls the ICE throttle angle degree to achieve operation in a high efficiency region. The induction motor is sized to supply peak power to meet the load power requirement of the PHEV. The proposed PHEV fuzzy controller is implemented and simulated via the advanced vehicle simulator ADVISOR using the European urban (ECE-15) and sub-urban (EUDC) driving cycles. Simulation results reveal that the proposed fuzzy torque distribution strategy is effective over the entire operating range of the vehicle in terms of performance, fuel economy, and emissions

A modified particle swarm optimization algorithm for the optimization of a fuzzy classification subsystem in a series hybrid electric vehicle

Algoritmi optimizacije temeljeni na optimizaciji roja čestica (PSO - particle swarm optimization) su jednostavne i lako primjenljive metode male računske složenosti što ih čini podesnim alatom za rješavanje opsežnih nelinearnih problema optimizacije. U radu je prikazana modificirana verzija originalne metode kombiniranjem PSO i lokalne metode pretraživanja na kraju svakog ciklusa ponavljanja. Novi se algoritam primjenjuje u zadatku optimizacije parametara podsustava fuzzy klasifikacije u serijskom hibridnom električnom vozilu u cilju smanjenja ispuštanja štetnih zagađivača. Novom se metodom uz primjenu sličnih parametara osigurava vrijednost veće prikladnosti nego bilo s originalnim PSO algoritmom ili algoritmom umjetnog imunološkog sustava zasnovanog na klonskoj selekciji (CLONALG).Particle swarm optimization (PSO) based optimization algorithms are simple and easily implementable techniques with low computational complexity, which makes them good tools for solving large-scale nonlinear optimization problems. This paper presents a modified version of the original method by combining PSO with a local search technique at the end of each iteration cycle. The new algorithm is applied for the task of parameter optimization of a fuzzy classification subsystem in a series hybrid electric vehicle (SHEV) aiming at the reduction of the harmful pollutant emission. The new method ensured a better fitness value than either the original PSO algorithm or the clonal selection based artificial immune system algorithm (CLONALG) by using similar parameters

Design and evaluation of a predictive powertrain control system for a plug-in hybrid electric vehicle to improve the fuel economy and the emissions

Taghavipour, A., Azad, N. L., & McPhee, J. Design and evaluation of a predictive powertrain control system for a plug-in hybrid electric vehicle to improve the fuel economy and the emissions. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 229(5), 624–640. Copyright © 2014 SAGE. Reprinted by permission of SAGE Publications. https://dx.doi.org/10.1177/0954407014547925In this article, a power management scheme for a plug-in power-split hybrid electric vehicle is designed on the basis of the model predictive control concept of charge depletion plus charge sustenance strategy and the blended-mode strategy. The commands of model predictive control are applied to the powertrain components through appropriate low-level controllers: standard proportional–integral controllers for electric machines, and sliding-mode controllers for engine torque control. Minimization of the engine emissions is a key factor for designing the engine’s low-level controller. Applying this control scheme to a validated high-fidelity model of a plug-in hybrid electric vehicle, developed in the MapleSim environment with a chemistry-based Lithium-ion battery model, results in considerable improvements in the fuel economy and the emissions performance.NSERCToyotaMaplesoft Industrial Research Chair progra

Control of a hybrid electric vehicle with predictive journey estimation

Battery energy management plays a crucial role in fuel economy improvement of charge-sustaining parallel hybrid electric vehicles. Currently available control strategies consider battery state of charge (SOC) and driver’s request through the pedal input in decision-making. This method does not achieve an optimal performance for saving fuel or maintaining appropriate SOC level, especially during the operation in extreme driving conditions or hilly terrain. The objective of this thesis is to develop a control algorithm using forthcoming traffic condition and road elevation, which could be fed from navigation systems. This would enable the controller to predict potential of regenerative charging to capture cost-free energy and intentionally depleting battery energy to assist an engine at high power demand. The starting point for this research is the modelling of a small sport-utility vehicle by the analysis of the vehicles currently available in the market. The result of the analysis is used in order to establish a generic mild hybrid powertrain model, which is subsequently examined to compare the performance of controllers. A baseline is established with a conventional powertrain equipped with a spark ignition direct injection engine and a continuously variable transmission. Hybridisation of this vehicle with an integrated starter alternator and a traditional rule-based control strategy is presented. Parameter optimisation in four standard driving cycles is explained, followed by a detailed energy flow analysis. An additional potential improvement is presented by dynamic programming (DP), which shows a benefit of a predictive control. Based on these results, a predictive control algorithm using fuzzy logic is introduced. The main tools of the controller design are the DP, adaptive-network-based fuzzy inference system with subtractive clustering and design of experiment. Using a quasi-static backward simulation model, the performance of the controller is compared with the result from the instantaneous control and the DP. The focus is fuel saving and SOC control at the end of journeys, especially in aggressive driving conditions and a hilly road. The controller shows a good potential to improve fuel economy and tight SOC control in long journey and hilly terrain. Fuel economy improvement and SOC correction are close to the optimal solution by the DP, especially in long trips on steep road where there is a large gap between the baseline controller and the DP. However, there is little benefit in short trips and flat road. It is caused by the low improvement margin of the mild hybrid powertrain and the limited future journey information. To provide a further step to implementation, a software-in-the-loop simulation model is developed. A fully dynamic model of the powertrain and the control algorithm are implemented in AMESim-Simulink co-simulation environment. This shows small deterioration of the control performance by driver’s pedal action, powertrain dynamics and limited computational precision on the controller performance

Monitorização do desempenho energético e ambiental de um veículo híbrido

Mestrado em Engenharia MecânicaOs problemas relacionados com o consumo energético e emissões de poluentes relativos ao sector dos transportes representam seguramente a maior preocupação ao nível europeu no que respeita a cumprimento do Protocolo de Quioto e à poluição atmosférica. Uma das formas de resolver/minimizar este problema é através da aposta em novas tecnologias, como os combustíveis alternativos e modos alternativos de propulsão. Os veículos híbridos (gasolina / eléctrico) constituem uma das novas opções tecnológicas com uma penetração efectiva no mercado. O facto de estes veículos possuírem um motor eléctrico juntamente com um motor de combustão interna, bem como sistemas de aproveitamento de energia (dos quais é exemplo o sistema de travagem regenerativa), faz esperar que os consumos de energia sejam mais baixos. A presente Dissertação consistiu na realização experimental de ensaios em banco de rolos e em estrada, com a medição de consumo de energia e análise do desempenho do veículo através de perfis de velocidades e curvas de rendimento para diversas situações de circulação de um veículo. As situações abordadas foram: paragens e arranques sucessivos (“pára-arranca”), circulação em rotundas, diversos perfis de velocidades para três tipos de condução (suave, intermédia e brusca) e um perfil de desaceleração imediatamente seguida de uma aceleração (de modo a simular uma passagem na faixa “Via Verde” de uma portagem). Foi ainda realizada uma modelação numérica com o modelo de emissões Copert 4, de forma a calcular o consumo de gasolina e emissões de CO2 para diversos veículos da mesma gama (incluindo veículos híbridos), mas de idades diferentes. Posteriormente foi efectuada a comparação entre os resultados experimentais e os provenientes pela modelação numérica. Os resultados obtidos entre as monitorizações experimentais revelam que os valores obtidos em banco de rolos para o consumo médio de gasolina são superiores em 51,4% relativamente aos testes em estrada, e por isso também em termos de emissão de CO2 produzidas pelo motor de combustão interna, uma vez que os consumos de gasolina são proporcionais à quantidade de CO2. Verifica-se uma semelhança entre os resultados obtidos em estrada e os provenientes da modelação numérica (os consumos médios de gasolina na modelação numérica diferem em 11,4% relativamente aos provenientes dos ensaios em estrada). Quando comparado com os consumos de veículos a gasolina da mesma categoria, o veículo híbrido é competitivo especialmente para meio urbano. ABSTRACT: The problems related to energy consumption and emissions of pollutants of the transport sector certainly represent a major concern at European level with regard to compliance with Kyoto Protocol and air pollution. One way to solve/minimize this problem is through the use of new technologies such as alternative fuels and alternatives propulsion modes. Hybrid vehicles (gasoline/electric) are one of the new technological options with an effective market penetration. The fact that they have an electrical engine along with an internal combustion engine as well as systems of energy use (such as the regenerative braking system), it is expected that the consumption of energy is lower. This Master Thesis research involved experimental measurements of energy consumption for a hybrid vehicle on a dynamometer and on-road. The performance of a hybrid vehicle through several speed profiles was performed as well as efficiency curves for several situations. The analyzed situations were: stop and go situations, roundabouts, speed profiles for three driving types (calm, average and aggressive) and a profile of a deceleration followed by an acceleration in order to simulate the circulation in the “Green Lane” of a pay toll. Numerical modeling was also performed with Copert 4 model in order to calculate the fuel consumption and CO2 emissions for several vehicles (including hybrids) with the same engine size, but with different ages. Finally, the comparison between the experimental results and the numerical modeling results was performed. The results obtained in the dynamometer for the average gasoline consumption are higher by 51.4% compared to on-road tests, and therefore also in terms of CO2 emissions produced by internal combustion engine, since the consumption of petrol are proportional to the quantity of CO2. There is a similarity between the results in from the road and numerical modeling (the average consumption of petrol in shaping numerical differ in relation to 11.4% from the trials on the road). When compared with average fuel consumptions for similar gasoline vehicles, Toyota Prius is particulary competitive in urban situations (speeds below 50 km/h)

Numerical Analysis and Modelling of Transmission Systems for Hybrid Electric Vehicles and Electric Vehicles

Interest in hybrid electric vehicles (HEVs) and electric vehicles (EVs) has increased rapidly over recent years from both industrial and academic viewpoints due to increasing concerns about environmental pollution and global oil usage. In the automotive sector, huge efforts have been invested in vehicle technology to improve efficiency and reduce carbon emissions with, for example, hybrid and electric vehicles. This thesis focuses on one design area of these vehicles – the transmission – with the aim of investigating the potential benefits of improved transmissions for HEVs and EVs. For HEVs, a novel transmission developed by Nexxtdrive based on a twin epicyclic design is analysed using a matrix method and its performance is compared with the more common single epicyclic arrangement used successfully in the Toyota Prius. Simulation models are then used to compare the performance of a typical HEV passenger car fitted with these two transmissions over standard driving cycles. The conclusion is that the twin epicyclic offers substantial improvements of up to 20% reduction in energy consumption, though the benefits are sensitive to the driving cycle used. For EVs, most designs to date have used a single fixed ratio transmission, and surprisingly little research has explored whether multi-geared transmissions offer any benefits. The research challenge is whether it is possible to optimise the usage of the electric motor in its region of high efficiency by controlling the transmission. Simulation results of two EV examples confirm that energy consumption benefits are indeed achievable – of between 7 and 14% depending on the driving cycle. Overall, the original aspects of this work – the analysis and modelling the twin epicyclic gearbox; the analysis and modelling the twin epicyclic system in a vehicle and a comparison of the results with single epicyclic system; and the analysis and modelling of EVs with and without a transmission system of varying levels of complexity – have shown that there are worthwhile performance benefits from using improved transmission designs for low carbon vehicles

Avaliação do ciclo de vida do sistema veículo/combustível no Brasil.

As emissões veiculares e o alto consumo de energia vinculado à cadeia de produção e uso de automóveis causam impactos significativos ao meio ambiente. Algumas medidas podem ser implementadas a fim de minimizar os danos causados por essas atividades. O uso eficiente de energia, assim como escolhas por produtos e serviços que liberem menores quantidades de poluentes, são práticas que visam à diminuição dos impactos ao meio ambiente. Existe uma ferramenta de gestão ambiental capaz de quantificar impactos ambientais, analisar produtos, serviços e tecnologias, através da utilização de dados de insumos, energia e emissões ambientais. Esta ferramenta é a Análise do Ciclo de Vida (ACV). O presente trabalho possui o objetivo de realizar análises comparativas dos seguintes sistemas automotivos: veículo com motor de combustão interna, que utiliza a gasolina como fonte de energia (ICEVg); veículo com motor de combustão interna que utiliza etanol como fonte de energia (ICEVe), Veículo com motor de combustão interna que utiliza gasolina e etanol como fontes de energia (ICEVf); Veículo elétrico movido a eletricidade (BEV); Veículo Híbrido plug-in movido a gasolina e eletricidade (PHEV); através da metodologia da ACV. Logo, será realizada uma revisão abrangente dos diferentes cenários, com o objetivo de quantificar e comparar os impactos ambientais provocados por esses sistemas. Os sistemas que utilizam etanol como parte de seu combustível, possuem maiores impactos ambientais para as categorias: eutrofização, acidificação e oxidação fotoquímica. Os sistemas que utilizam gasolina como parte do combustível, possuem os maiores potenciais de impacto para: depleção abiótica, depleção abiótica de combustíveis fósseis e aquecimento global. Veículos que utilizam a bateria de íon de lítio possuem maiores impactos para a toxicidade humana

Control of a hybrid electric vehicle with predictive journey estimation

Battery energy management plays a crucial role in fuel economy improvement of charge-sustaining parallel hybrid electric vehicles. Currently available control strategies consider battery state of charge (SOC) and driver’s request through the pedal input in decision-making. This method does not achieve an optimal performance for saving fuel or maintaining appropriate SOC level, especially during the operation in extreme driving conditions or hilly terrain. The objective of this thesis is to develop a control algorithm using forthcoming traffic condition and road elevation, which could be fed from navigation systems. This would enable the controller to predict potential of regenerative charging to capture cost-free energy and intentionally depleting battery energy to assist an engine at high power demand. The starting point for this research is the modelling of a small sport-utility vehicle by the analysis of the vehicles currently available in the market. The result of the analysis is used in order to establish a generic mild hybrid powertrain model, which is subsequently examined to compare the performance of controllers. A baseline is established with a conventional powertrain equipped with a spark ignition direct injection engine and a continuously variable transmission. Hybridisation of this vehicle with an integrated starter alternator and a traditional rule-based control strategy is presented. Parameter optimisation in four standard driving cycles is explained, followed by a detailed energy flow analysis. An additional potential improvement is presented by dynamic programming (DP), which shows a benefit of a predictive control. Based on these results, a predictive control algorithm using fuzzy logic is introduced. The main tools of the controller design are the DP, adaptive-network-based fuzzy inference system with subtractive clustering and design of experiment. Using a quasi-static backward simulation model, the performance of the controller is compared with the result from the instantaneous control and the DP. The focus is fuel saving and SOC control at the end of journeys, especially in aggressive driving conditions and a hilly road. The controller shows a good potential to improve fuel economy and tight SOC control in long journey and hilly terrain. Fuel economy improvement and SOC correction are close to the optimal solution by the DP, especially in long trips on steep road where there is a large gap between the baseline controller and the DP. However, there is little benefit in short trips and flat road. It is caused by the low improvement margin of the mild hybrid powertrain and the limited future journey information. To provide a further step to implementation, a software-in-the-loop simulation model is developed. A fully dynamic model of the powertrain and the control algorithm are implemented in AMESim-Simulink co-simulation environment. This shows small deterioration of the control performance by driver’s pedal action, powertrain dynamics and limited computational precision on the controller performance.EThOS - Electronic Theses Online ServiceGBUnited Kingdo