Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

디젤 엔진의 성능과 배기 최적화를 위한 연소 디자인 연구

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계공학부, 2021.8. 이광호.최근 내연기관 성능향상을 위한 연구는 연비, 배출량, 소음, 진동 등의 측면에 초점이 맞춰지고 있다. 연비는 지구온난화에 영향을 준 이산화탄소 배출 감소와 관련이 있다. 또한, 디젤 연소로 인한 질소산화물과 그을음 배출은 인간의 건강에 해로우며 생명까지도 위협한다. 배기 가스의 유해성은 많은 나라들의 정부들로 하여금 차량 배출 규제를 엄격하게 만들도록 동기를 부여하고 있다. 최근에는 실험실의 인증치와 도로의 실제 배출량 수준 간의 차이를 고려해 Real-driving emissions 규제가 시행됐다. 소음 공해는 또한 인간과 공중 보건 문제의 관점에서 중요한 주제이다. 엔진에서 발생하는 연소 소음은 엔진 변수 및 연소 특성에 의해 영향을 받는 실린더 압력 배출에 따라 달라진다. 적절한 분사 전략 또는 연소 형태는 원하는 연소 소음 수준을 만족하도록 최적화할 수 있다. 엔진 개발 과정 중에 연비, 배기 배출물 및 소음의 각 성능을 최적화하기 위한 많은 노력과 시간이 소모된다. 최적의 성능을 얻으려면 연소 및 엔진 작동 변수를 최적화하기 위해 많은 실험이 필요하다. 실험 없이 엔진 성능을 최적화하기 위해 전산유체역학 시뮬레이션을 수행하려면 높은 계산 비용이 필요하다. 따라서, 계산 비용이 낮은 0-D 연소 최적화 방법론을 개발하는 것이 중요하다. 이전 연구들에 의한 0-D 연소 최적화 방법은 분사 전략 또는 엔진 변수를 최적화했다. 결과로 도출되는 연소는 기존 연소 형상의 범위 내에 있으며 연소 형상의 다양성 측면에서 실험적으로 변수를 최적화하는 방법과 다를 바 없다. 본 연구에서는 원하는 성능을 입력으로, 최적 연소 및 연소 변수를 출력으로 도출되는 연소 디자인 방법론을 개발하였다. 첫 번째 단계로 엔진 실험을 수행하여 베이스 조건, EGR 스윙, 흡기 온도 및 냉각수 온도 스윙 조건에서의 연비 및 배기 배출의 기본 성능을 평가하였다. 이 결과는 연소 디자인에 활용되는 0-D soot 모델 수립과 최적화 과정에서 초기 조건으로 사용되었다. Soot 생성 모델은 lift-off length에서 당량비를 계산하는 단순화된 스프레이 모델을 기반으로 했다. Lift-off length에서의 당량비는 그을음 형성 모델의 주요 요인 중 하나로 사용되었다. 연소 디자인 과정에서 IMEP는 연비를 대변하는 인자로 사용되었다. 연소 소음 평가에는 연소 소음 지수가 사용되었다. NOx 배출량 예측에는 이전 연구로부터 개발된 0-D NOx 모델이 적용되었다. 연소 디자인 방법론에서, 실린더 내 압력 계산에 필요한 변수는 흡기 압력, 람다 및 질량 연소율이었다. 질량 연소율은 기존에 널리 사용되는 위베 함수와 연소상에 대한 새로운 접근법으로 다항식 함수를 사용하여 결정되었다. 실린더 내 공기의 질량과 EGR율은 초기 조건으로 결정된 흡기 압력, 온도, 람다 및 화학 반응 방정식으로 계산되었다. 연소 중의 기체 조성비는 polytropic 지수 및 여러 열역학적 변수의 계산을 위해 계산되었다. 실린더 내 압력은 polytropic 과정과 열 발생률으로 계산되었다. 최적화 과정에서 연소 디자인 방법에 사용된 최적화 알고리즘은 제한된 비선형 다변량 함수(Interior-point 기법)와 입자 군집 최적화의 최소값이었다. 경계 조건과 제약 조건은 최적화 과정의 효율적인 iteration을 위해 결정되었다. 최적화를 위한 목적 함수의 기본 형태는 입력으로 사용된 원하는 성능을 가지는 특정한 연소를 찾을 수 있게 했다. 목표 성능을 최대화하는 다양한 디자인 목적에 따라 목표 함수는 변형되어 사용되었다. 연소 디자인의 결과는 다양한 운전 영역에서 MFB와 목적 함수에 따라 연구되었다. WLTP 운전 영역에서 정상 상태 연소들을 디자인하여 연소 디자인 방법론을 WLTP에 적용하였다. 적용 결과, WLTP 중 연료 소모량은 4.7% 감소되었다. NOx와 soot 배출은 각각 44.7%와 60.7%의 감소율을 보였다. 본 연구에서는 원하는 성능의 연소를 도출하는 0-D 연소 시뮬레이션 및 최적화 방법을 제공하였다. 이 연구는 원하는 성능 혹은 최적화된 성능을 가지는 연소상을 열역학적인 조건들과 함께 제시할 수 있어 이를 바탕으로 기존의 연구 방법과 다른 목표 연소를 구현하기 위한 엔진과 연소 전략 개발 방향을 제시할 수 있다.Recently, the researches for improving the performance of the internal combustion engines have been focused on the respect of thermal efficiency, emissions, noise and vibration. The thermal efficiency is related with decreasing carbon dioxide (CO2) emission that has affected global warming. Also, nitrogen oxides (NOx) and soot emissions from diesel combustion are harmful for human. The harmfulness of exhaust gases has motivated governments of many countries to make vehicle emission regulations stringent. Recently, real-driving emissions (RDE) regulation was enforced, considering the discrepancy between the certified values in laboratory and the actual emission levels on the road. Noise pollution is also important in the perspective of human and public health problem. The combustion noise from the engine depends on the cylinder pressure excitation, which is affected by the engine parameters and combustion characteristics. Proper injection strategies or combustion shape can be optimized to meet the desired combustion noise level. The engine development process takes a lot of effort and time to optimize each performance of thermal efficiency, emissions and noise. To achieve desired optimal performance, many trials and errors and experiments are required to optimize combustion and engine operating parameters. As an optimization tool, computational fluid dynamics (CFD) simulation needs substantial calculation cost. Thus, it is important to develop 0-D combustion optimization methodology that has low calculation cost. Previously studied 0-D combustion optimization methods have been optimized the injection strategy or engine parameters. The resulting combustion comes out of a narrow range and it is similar to the methodology of optimizing variables experimentally in terms of diversity of combustion. In this study, the combustion design methodology was developed that used the desired performance as input and derived combustion and combustion parameters as outputs in a diesel engine. The thermal efficiency, noise and emissions were needed to be calculated by 0-D combustion simulation for the combustion optimization. As a one of emission models, the 0-D soot model was developed through cooperative research with Youngbok Lee. The engine test that evaluated the soot emission by EGR rate, intake and coolant temperature was conducted to develop the 0-D soot model and acquire initial conditions for combustion design. The soot formation model was based on the simplified spray model to calculate the equivalence ratio at lift-off length. The equivalence ratio at the lift-off length was used as a one of the main factor for the soot formation model. In the combustion design process, IMEP represented the thermal efficiency. For the combustion noise evaluation, the combustion noise index was used. The 0-D NOx model from previous research was applied to estimate NOx emission. In the combustion design methodology, the initial parameters for constructing in-cylinder pressure were intake pressure, lambda and the mass faction burned. The MFB was determined by using Wiebe function and polynomial function as new approach to combustion phase. The mass of in-cylinder air and EGR rate were calculated from intake pressure, temperature, lambda, that were determined as initial conditions, and the chemical reaction equation. Compression and expansion strokes were assumed as polytropic process. The gas compositions during the combustion were calculated for calculation of polytropic index and other thermodynamic parameters. The in-cylinder pressure was calculated by the heat release rate and polytropic process with the estimated polytropic index. In the optimization process, the optimization algorithms used in the combustion design method were a minimum of constrained nonlinear multivariable function (interior-point) and particle swarm optimization. MATLAB was used as the optimization tool. The boundary conditions and constraints were determined for efficient iteration in optimization process. The base form of objective function for optimization allowed to find specific combustion of desired performance that was used as input. The objective functions for various design concepts were used in maximizing target performance. The results of combustion design were investigated by objective function and MFB function type at various operation point. The combustion design method was applied to WLTP by designing the steady points in WLTP operation region. The fuel consumption during WLTP decreased by 4.6% compared to experimental result The NOx and soot emissions could be reduced by 44.7% and 60.7%. In this study, the 0-D combustion simulation and optimization method that derived the combustion of desired performance were provided. This research can contribute to provide combustion shape with desired or optimized performance in combination with thermodynamic conditions, suggesting the development process different from existing research methods of engine and combustion strategies for target combustion.Chapter 1. Introduction 1 1.1 Background 1 1.2 Literature Review 8 1.2.1 Combustion noise model 8 1.2.2 NOx emission model 11 1.2.3 Soot emission model 13 1.2.4 Combustion optimization 18 1.3 Research Objectives and Contributions 28 1.4 Structure of the Thesis 29 Chapter 2. Experimental Apparatus 31 2.1 Experimental Setup 31 2.1.1 Test engine 31 2.1.2 Test cell and data acquisition systems 31 2.1.3 Emission measurement systems 32 2.1.4 Engine operating conditions 34 Chapter 3. Semi-physical 0-D Soot Model 46 3.1 Simplified Spray Model 46 3.1.1 Spray model description 46 3.1.2 Liquid length calculation 48 3.1.3 Laminar flame speed model 50 3.1.4 The equivalence ratio at the lift-off length 53 3.2 Semi-physical 0-D Soot Model 64 3.2.1 Soot formation model 64 3.2.2 Soot oxidation model 65 3.2.3 The model validation 68 Chapter 4. Other Models for Thermal Efficiency, Noise and NOx Emission 74 4.1 Thermal Efficiency 74 4.2 Noise – Combustion Noise Index (CNI) 74 4.3 The NOx Estimation Model 78 Chapter 5. Combustion Design Methodology 81 5.1 Concept of Combustion Design Method 81 5.2 Process of Constructing Combustion Pressure 86 5.2.1 Mass fraction burned and heat release rate 86 5.2.2 Calculation of in-cylinder air flow and EGR rate 95 5.2.3 Gas composition during the combustion process 102 5.2.4 Polytropic index and constructing cylinder pressure 103 5.2.5 Calculation of the fuel injection timing 107 5.3 Optimization Methodology 113 5.3.1 Optimization algorithms 113 5.3.2 Boundary conditions and constraints 116 5.3.3 Determination of the objective function 121 Chapter 6. Results of Combustion Design 131 6.1 Results from Base Objective Function 131 6.1.1 Low load: 1500 rpm, BMEP 4 bar 131 6.1.2 High load: 2000 rpm, BMEP 8 bar 136 6.2 Results by various design concept 141 6.3 Results Using Polynomial Function as MFB 148 6.4 Application of Combustion Design to WLTP 153 6.4.1 Combustion design at steady points in WLTP operating area 153 6.4.2 Results of an application to WLTP 164 Chapter 7. Conclusions 169 Bibliography 174 국 문 초 록 193박

Development of Automated Calibration Methodology for Last Generation of Diesel Automotive Powertrains

L'abstract è presente nell'allegato / the abstract is in the attachmen

Non-weighted aggregate evaluation function of multi-objective optimization for knock engine modeling

In decision theory, the weighted sum model (WSM) is the best known Multi-Criteria Decision Analysis (MCDA) approach for evaluating a number of alternatives in terms of a number of decision criteria. Assigning weights is a difficult task, especially if the number of criteria is large and the criteria are very different in character. There are some problems in the real world which utilize conflicting criteria and mutual effect. In the field of automotive, the knocking phenomenon in internal combustion or spark ignition engines limits the efficiency of the engine. Power and fuel economy can be maximized by optimizing some factors that affect the knocking phenomenon, such as temperature, throttle position sensor, spark ignition timing, and revolution per minute. Detecting knocks and controlling the above factors or criteria may allow the engine to run at the best power and fuel economy. The best decision must arise from selecting the optimum trade-off within the above criteria. The main objective of this study was to proposed a new Non-Weighted Aggregate Evaluation Function (NWAEF) model for non-linear multi-objectives function which will simulate the engine knock behavior (non-linear dependent variable) in order to optimize non-linear decision factors (non-linear independent variables). This study has focused on the construction of a NWAEF model by using a curve fitting technique and partial derivatives. It also aims to optimize the nonlinear nature of the factors by using Genetic Algorithm (GA) as well as investigate the behavior of such function. This study assumes that a partial and mutual influence between factors is required before such factors can be optimized. The Akaike Information Criterion (AIC) is used to balance the complexity of the model and the data loss, which can help assess the range of the tested models and choose the best ones. Some statistical tools are also used in this thesis to assess and identify the most powerful explanation in the model. The first derivative is used to simplify the form of evaluation function. The NWAEF model was compared to Random Weights Genetic Algorithm (RWGA) model by using five data sets taken from different internal combustion engines. There was a relatively large variation in elapsed time to get to the best solution between the two model. Experimental results in application aspect (Internal combustion engines) show that the new model participates in decreasing the elapsed time. This research provides a form of knock control within the subspace that can enhance the efficiency and performance of the engine, improve fuel economy, and reduce regulated emissions and pollution. Combined with new concepts in the engine design, this model can be used for improving the control strategies and providing accurate information to the Engine Control Unit (ECU), which will control the knock faster and ensure the perfect condition of the engine

가솔린 엔진에서의 0-D 노킹 발생예측 모델링에 관한 연구

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2019. 2. 민경덕.가솔린 엔진의 효율 향상은 지속적으로 연구되어 왔으나, 최근의 급격히 강화되는 연비, 배기 규제로 인해 효율 향상의 필요성이 그 어느 때보다 더욱 대두되고 있다. 따라서 가솔린 엔진의 효율 향상을 위한 다양한 방법이 제시되고 있으며, 그 중에서도 압축비의 상승은 효율 개선에 매우 효과적이다. 그러나 압축비의 상승은 혼합기의 초기 온도를 상승시키며, 이는 연소기간 중 말단가스의 온도 상승으로 직결되어 미연 가스의 자발화 현상이 발생하기 쉽게 한다. 자발화 현상이 폭발의 형태로 발생하는 경우, 순간적인 열 방출이 발생하고 실린더 내부에 압력파를 형성하여 소음과 엔진 손상을 유발한다. 이는 노킹 현상으로 불리며, 엔진 손상을 방지하기 위하여 반드시 회피되어야 한다. 따라서 이에 대해 노킹을 저감하는 연구뿐만 아니라, 노킹을 회피하기 위한 다양한 제어 알고리즘이 제시되고 있다. 기존 양산 차량에서의 노킹 제어 알고리즘은 노킹 센서에 의존하고 있으며, 이는 센서에서 노킹 발생을 감지하는 것이 선행되어야 하므로 노킹을 완전히 회피할 수 없다. 또한, 노킹 발생 시 회피를 위해 제어인자가 급격히 변경되며, 이는 운전편의성이 나빠질 뿐만 아니라, 엔진 효율을 감소시키게 된다. 이러한 점을 개선하기 위해 제어인자를 점진적으로 변경하는 등 진보적인 제어 방법이 개발되고 있다. 노킹현상은 엔진 내의 유동, 열 전달 및 연소 특성과 같은 인자에 영향을 받으므로, 무작위적인 특성을 보인다. 따라서, 노킹을 보수적으로 회피하기 위해 기존의 제어는 노킹 회피 후에 운전 인자를 천천히 최적 운전 조건으로 복귀시킨다. 이는 전반적으로 효율이 낮은 지점에 운전 조건을 유지시키므로, 이를 개선한다면 고부하 조건에서 추가적인 효율 상승을 기대할 수 있다. 이에 대한 개선 방법의 하나로 노킹을 예측하여 선재적으로 제어를 수행하는 모델 기반의 제어를 제시할 수 있다. 이 경우, 노킹을 회피하기 용이해 질 뿐만 아니라, 노킹 발생 사전에 제어 인자를 지정함으로써 기존에 발생하던 과도한 제어 인자 변경으로 인한 효율 저하를 방지할 수 있다. 본 연구에서는 선재적 제어를 위한 기저연구로, 엔진에서의 노킹 발생 예측에 대한 모델링이 이루어졌다. 모델에는 실제 운전 상황에의 적용을 고려하여, 기존 양산 엔진에서 얻을 수 있는 변수 및 측정값만이 이용되었다. 첫째로, 실린더 내 압력 예측 모델링이 이루어 졌으며, 이는 실린더 내부 초기 조건의 판정, 압축기간 동안의 압력 예측, 그리고 연소율을 이용한 연소 압력 예측의 세가지 과정으로 진행되었다. 둘째로, 예측된 압력을 이용하여 미연가스의 온도를 계산하였으며, 이는 점화 지연을 계산하는 데에 이용되었다. 점화 지연은 실제 자발화 발생 여부를 판정하지 못하므로, 예측된 자발화 시점에서의 연소율을 이용하여 노킹 여부를 판정하였다. 구성된 모델은 사이클 별 편차가 고려된 압력 예측모델과 함께 적용되었을 경우 노킹 발생 빈도를 잘 예측하여, 충분한 예측 정확성을 가지고 있다고 판단되었다.Though the efficiency improvements of gasoline engines have been continually studied, the recent surge in fuel efficiency and emissions regulations has made the need for greater effort on efficiency improvement than ever before. Therefore, various methods have been proposed to improve the efficiency of gasoline engines, and among them, the increase in compression ratio has been known to be effective in improving fuel efficiency. However, the higher compression ratio increases the initial temperature of the mixture, which is led to in-cylinder condition which auto-ignition is likely to occur. If auto-ignition occurs in the form of an explosion, a stiff heat release occurs and forms a pressure wave inside the cylinder, causing noise and engine damage. This is called a knock and must be avoided to prevent engine damage. Therefore, various control algorithms have been proposed to avoid knock as well as researches to mitigate knock. Knocking control algorithms in conventional mass-produced vehicles rely on knock sensors. Because it is preceded by detection of knock from sensors, knock cannot be completely avoided. In addition, the control parameters change rapidly to avoid convolutions in case of knock, which not only deteriorates the driving convenience but also decreases fuel efficiency In order to improve this point, progressive control methods are being developed. The knock phenomenon is influenced by factors such as gas flow, heat transfer and combustion characteristics in the engine, so that it shows random characteristics. Therefore, in order to avoid the knocking conservatively, the conventional control slowly returns the operating parameters to the optimum condition after knocking avoidance. This maintains the operating conditions at lower efficiency points, so additional efficiency improvement is expected in high load condition if the control algorithm is improved. As one of the solutions, an advanced control with knock prediction can be suggested. In this case, not only is it able to avoid knock, but also by determining control factors prior to knock occurrence, it is also possible to avoid efficiency deterioration due to excessive change in control parameters. In this study, modeling of knock prediction was done as a base study for model based control. In the model, only operating parameters and measured values obtainable from the mass production engine were used, considering the application to actual driving conditions. Firstly, the in-cylinder pressure prediction modeling was done with three stepsdetermination of the initial conditions inside the cylinder, pressure prediction during the compression, and combustion pressure prediction using the burn rate. Additionally, with consideration on variation of Wiebe function, the cyclic variation model was constructed. Secondly, the temperature of the unburned gas was calculated using the predicted pressure. With those value, the ignition delay was calculated. Since the ignition delay only determines the onset of auto-ignition, not the occurrence, the burn rate at predicted onset was used to determine the knock occurrence. The pressure prediction and ignition delay model were combined to a single model and tested with cyclic variation model. As a result, the model was judged to have sufficient accuracy, predicting knock incidence accurately under various operating conditions.Abstract i Contents iv List of Tables vi List of Figures vii Nomenclature ix Chapter 1. Introduction 1 1.1 Backgrounds and Motivations 1 1.2 Literature Review 6 1.2.1 Auto-ignition and knock phenomenon 6 1.2.2 Knock methodologies 10 1.2.3 Knock mitigation strategies 16 1.3 Research objective 20 Chapter 2. Methodology 21 2.1 Test cell configuration 21 2.1.1 Test engine specification 21 2.1.2 Cell facility and equipment 21 2.2 Experimental conditions 26 2.2.1 Knock detection and incidence 26 2.2.2 Operating condition 26 2.3 Combustion analysis 29 2.3.1 Heat release rate and heat loss 29 2.3.2 Knock onset determination 33 2.3.3 Unburned gas temperature 36 2.3.4 Residual gas fraction estimation 37 Chapter 3. Model Description 44 3.1 Prediction of in-cylinder pressure 44 3.1.1 Initial gas state 44 3.1.2 Compression process with polytropic index model 46 3.1.3 Combustion process estimation 50 3.1.4 Simulation of cyclic variation 57 3.2 Criteria for knock determination 60 3.2.1 Ignition delay estimation 60 3.2.2 Mass burned fraction and knock onset 63 Chapter 4. Result and discussion 65 Chapter 5. Conclusion 70 Bibliography 73 국 문 초 록 82Maste

Optimization of adaptive test design methods for the determination of steady-state data-driven models in terms of combustion engine calibration

This thesis deals with the development of a model-based adaptive test design strategy with a focus on steady-state combustion engine calibration. The first research topic investigates the question how to handle limits in the input domain during an adaptive test design procedure. The second area of scope aims at identifying the test design method providing the best model quality improvement in terms of overall model prediction error. To consider restricted areas in the input domain, a convex hull-based solution involving a convex cone algorithm is developed, the outcome of which serves as a boundary model for a test point search. A solution is derived to enable the application of the boundary model to high-dimensional problems without calculating the exact convex hull and cones. Furthermore, different data-driven engine modeling methods are compared, resulting in the Gaussian process model as the most suitable one for a model-based calibration. To determine an appropriate test design method for a Gaussian process model application, two new strategies are developed and compared to state-of-the-art methods. A simulation-based study shows the most benefit applying a modified mutual information test design, followed by a newly developed relevance-based test design with less computational effort. The boundary model and the relevance-based test design are integrated into a multicriterial test design strategy that is tailored to match the requirements of combustion engine test bench measurements. A simulation-based study with seven and nine input parameters and four outputs each offered an average model quality improvement of 36 % and an average measured input area volume increase of 65 % compared to a non-adaptive space-filling test design. The multicriterial test design was applied to a test bench measurement with seven inputs for verification. Compared to a space-filling test design measurement, the improvement could be confirmed with an average model quality increase of 17 % over eight outputs and a 34 % larger measured input area

ADAPTIVE MODEL BASED COMBUSTION PHASING CONTROL FOR MULTI FUEL SPARK IGNITION ENGINES

This research describes a physics-based control-oriented feed-forward model, combined with cylinder pressure feedback, to regulate combustion phasing in a spark-ignition engine operating on an unknown mix of fuels. This research may help enable internal combustion engines that are capable of on-the-fly adaptation to a wide range of fuels. These engines could; (1) facilitate a reduction in bio-fuel processing, (2) encourage locally-appropriate bio-fuels to reduce transportation, (3) allow new fuel formulations to enter the market with minimal infrastructure, and (4) enable engine adaptation to pump-to-pump fuel variations. These outcomes will help make bio-fuels cost-competitive with other transportation fuels, lessen dependence on traditional sources of energy, and reduce greenhouse gas emissions from automobiles; all of which are pivotal societal issues. Spark-ignition engines are equipped with a large number of control actuators to satisfy fuel economy targets and maintain regulated emissions compliance. The increased control flexibility also allows for adaptability to a wide range of fuel compositions, while maintaining efficient operation when input fuel is altered. Ignition timing control is of particular interest because it is the last control parameter prior to the combustion event, and significantly influences engine efficiency and emissions. Although Map-based ignition timing control and calibration routines are state of art, they become cumbersome when the number of control degrees of freedom increases are used in the engine. The increased system complexity motivates the use of model-based methods to minimize product development time and ensure calibration flexibility when the engine is altered during the design process. A closed loop model based ignition timing control algorithm is formulated with: 1) a feed forward fuel type sensitive combustion model to predict combustion duration from spark to 50% mass burned; 2) two virtual fuel property observers for octane number and laminar flame speed feedback; 3) an adaptive combustion phasing target model that is able to self-calibrate for wide range of fuel sources input. The proposed closed loop algorithm is experimentally validated in real time on the dynamometer. Satisfactory results are observed and conclusions are made that the closed loop approach is able to regulate combustion phasing for multi fuel adaptive SI engines

Low-Pressure EGR in Spark-Ignition Engines: Combustion Effects, System Optimization, Transients & Estimation Algorithms

Low-displacement turbocharged spark-ignition engines have become the dominant choice of auto makers in the effort to meet the increasingly stringent emission regulations and fuel efficiency targets. Low-Pressure cooled Exhaust Gas Recirculation introduces important efficiency benefits and complements the shortcomings of highly boosted engines. The main drawback of these configurations is the long air-path which may cause over-dilution limitations during transient operation. The pulsating exhaust environment and the low available pressure differential to drive the recirculation impose additional challenges with respect to feed-forward EGR estimation accuracy. For these reasons, these systems are currently implemented through calibration with less-than-optimum EGR dilution in order to ensure stable operation under all conditions. However, this technique introduces efficiency penalties. Aiming to exploit the full potential of this technology, the goal is to address these challenges and allow operation with near-optimum EGR dilution. This study is focused on three major areas regarding the implementation of Low-Pressure EGR systems: Combustion effects, benefits and constraints System optimization and transient operation Estimation and adaptation Results from system optimization show that fuel efficiency benefits range from 2% – 3% over drive cycles through pumping and heat loss reduction, and up to 16% or more at higher loads through knock mitigation and fuel enrichment elimination. Soot emissions are also significantly reduced with cooled EGR. Regarding the transient challenges, a methodology that correlates experimental data with simulation results is developed to identify over-dilution limitations related to the engine’s dilution tolerance. Different strategies are proposed to mitigate these issues, including a Neural Network-actuated VVT that controls the internal residual and increases the over-dilution tolerance by 3% of absolute EGR. Physics-based estimation algorithms are also developed, including an exhaust pressure/temperature model which is validated through real-time transient experiments and eliminates the need for exhaust sensors. Furthermore, the installation of an intake oxygen sensor is investigated and an adaptation algorithm based on an Extended Kalman Filter is created. This algorithm delivers short-term and long-term corrections to feed-forward EGR models achieving a final estimation error of less than 1%. The combination of the proposed methodologies, strategies and algorithms allows the implementation of near-optimum EGR dilution and translates to fuel efficiency benefits ranging from 1% at low-load up to 10% at high-load operation over the current state-of-the-art