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

Best multiple non-linear model factors for knock engine (SI) by using ANFIS

Knock Prediction in vehicles is an ideal problem for non-linear regression to deal with, which use many of the factors of information to predict another factor. Training data were collected through a test engine for the Malaysian Proton company and in various states of speed.Selected six influential factors on the knocking(Throttle Position Sensor(TPS),Temperature(TEMP),Revolution Per Minute(RPM),(TORQUE),Ignition Timing( IGN),Acceleration Position(AC_POS)), has been taking data for this study and then applied to a single cylinder,output factor (output variable) to be prediction factor is a knock.We compare the performance of resultant ANFIS and Linear regression to obtain results shows effectiveness ANFIS, as well as three factors were selected from six non-linear factors to get the best model by using Adaptive Neuro-Fuzzy Inference System (ANFIS).Experiments demonstrate that although soft computing methods are somewhat of tolerant of inaccurate inputs, cleaned data results in more robust models for practical problems

New Model for Knock Factors Optimization in Internal Combustion Engine (SI)

The main goal of this paper is to construct a new mathematical model and study the behavior of the factors affecting the problem of knocking in internal combustion engines.Curve fitting technique was used in construction of the model, and also Akaike Information Criterion (AIC) was used as a test in choosing the best model.Factors affecting the problem of knocking have been identified through the use of test engine had promised to do so. The mathematical model was built through real data under certain conditions. Three influential factors (Temp., TPS and RPM) have been taken into consideration. Curve fitting models were used in achieving the goal and then studied the effect of one of the factors in the problem of knocking was investigated.Results obtained through the application of the new model is a low level knocking with increasing temperature (Temp) at the same points in Throttle (TPS), the Revolution Per Minute (RPM), which shows the effectiveness of the new model with non-linear behavior of the factors affecting the knock

Statistical analysis for multiple non-linear knock factors in internal combustion engine

In this study, we will address the problem of knocking in internal combustion engines, and some of the factors affecting the knocking, through the study of the power of the effect of each factor after finding a model representing the relationship between the factors. We found Curve fitting model from data that has been obtained through the engine test (1.3L Campro, modified to turbocharger, 4-cylinder, MPI). This model has been evaluated statistically after finding the parameters that intervened in the construction of that model