Mathematical Problems in Engineering

Volume 2016 (2016), Article ID 6180758, 8 pages

http://dx.doi.org/10.1155/2016/6180758

## An ANN-GA Framework for Optimal Engine Modeling

^{1}Industrial Engineering Department, The University of Jordan, Amman 11942, Jordan^{2}Mechanical and Industrial Engineering Department, Applied Science University, Amman, Jordan

Received 2 November 2015; Accepted 11 February 2016

Academic Editor: Hung-Yuan Chung

Copyright © 2016 Khaldoun K. Tahboub et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Internal combustion engines are a main power source for vehicles. Improving the engine power is important which involved optimizing combustion timing and quantity of fuel. Variable valve timing (VVT) can be used in this respect to increase peak torque and power. In this work Artificial Neural Network (ANN) is used to model the effect of the VVT on the power and genetic algorithm (GA) as an optimization technique to find the optimal power setting. The same proposed technique can be used to improve fuel economy or a balanced combination of both fuel and power. Based on the findings of this work, it was noticed that the VVT setting is more important at high speed. It was also noticed that optimal power can be obtained by changing the VVT settings as a function of speed. Also to reduce computational time in obtaining the optimal VVT setting, an ANN was successfully used to model the optimal setting as a function of speed.

#### 1. Introduction

Internal combustion engines have been a major power source throughout the history of ground vehicles. The two dominating combustion concepts, the Otto engine and the Diesel engine, were developed in the late 1800s. The introduction of electronic ignition and fuel injection systems in the 1980s have given the engineers far more capability of engine control than before. Since then, both fuel economy and emissions have improved, but still more progress can be expected in the future. One of the limitations in engine control development lies in the scarce online information about the controlled process, the combustion [1].

Variable valve timing (VVT) is used in spark ignition automotive engines to improve fuel economy, reduce gases, and increase peak torque and power [2]. Valve control is one of the most important parameters for optimizing efficiency and emissions, permitting combustion engines to conform to future emission targets and standards. Thermodynamic conditions during the closed cycle (compression, combustion, and expansion) can be directly controlled by adjusting the intake valve opening (IVO) and intake valve closing (IVC) angle, which defines the total intake mass flow rate and the effective compression ratio of the engine [3]. Control of the intake valve provides optimal filling of the cylinder at all engine speeds. This natural supercharging, and the improved engine torque and power that accompany it, makes it possible to downsize engine capacity and thus reduce fuel consumption at all operating conditions [4]. Variable valve timing (VVT) relates to both the opening time and the duration of the valve’s open interval. Controlling valve timing can improve the torque curve, the brake power curve, or the indicator-power curve of a given engine. Variable valve timing can also be used to reduce the fuel consumption and, to a small extent, the engine emissions [5]. The adoption of a continuous variable valve timing (VVT) system is able to optimize engine torque and efficiency [6].

Genetic algorithm (GA) as an optimization technique is widely used for optimization of engineering problems. Many engineering design problems are very complex and therefore difficult to solve with conventional optimization techniques [7]. There are some studies in the literature about using GA for optimization of engine characteristics [8–11]. There is no guarantee that a GA will give an optimal solution or arrangement; there is only a guarantee that the solution will be near optimal in the light of the specific fitness function used in the evaluation of the many possible solutions generated. By near optimal, it is implied that a more optimal solution may exist; however, the stochastic approach is by nature nondeterministic and therefore global optima cannot be guaranteed, and some hybrid techniques exist to combine “hill-climbing” deterministic approaches to stochastic GA approaches to determine the best solution to the accuracy required after the GA method has determined the best region of space to investigate. However, this refinement is not necessary in this study [12].

Artificial Neural Network (ANN) models may be used as an alternative way in engineering analysis and predictions. They are recently used also in engine optimization regarding engine operating parameters and emissions [13–16]. ANN models mimic somewhat the learning process of a human brain. They operate like a “black box” model, requiring no detailed information about the system. Instead, they learn the relationship between the input parameters and the controlled and uncontrolled variables by studying previously recorded data, similar to the way a nonlinear regression might perform. Another advantage of using ANNs is their ability to handle large and complex systems with many interrelated parameters. They seem simply to ignore excess data that are of minimal significance and concentrate instead on the more important inputs [12, 17]. Also the neural networks can be used in the form of an ensemble which highly adds to the accuracy of these ANNs [18].

Several methodologies for analysis and optimization of diesel engines including DoE [19], Support Vector Machine (SVM) [20], fuzzy modeling [21], particle swarm optimization [22], and specialized modeling software such as MATLAB [23] have been reported or can be used. In this work, different techniques are applied in the form of a multistage methodology. ANNs are used for engine modeling as a tool for optimization using GA. This GA is run for the purpose of determining the optimal power conditions at each speed. The ANN is used in reverse to dictate the optimal conditions according to speed.

#### 2. Mathematical Background

##### 2.1. Artificial Neural Networks

An Artificial Neural Network is based on the biological neural networks (nervous system) and is composed of “neurons” or “neurodes,” which are artificial nodes, processing elements, or “units.” A neural network is a mathematical model that is based on interconnection of the neurons and the strength of the connections (weights and biases) to model the majority of not all possible known functions. Neural networks in that respect are a generalized function that can be used to model complex relationships between inputs and outputs or to classify patterns in data.

A schematic diagram of a typical multilayer feed-forward neural network architecture is shown in Figure 1(a). The network usually consists of an input layer, some hidden layers, and an output layer. In its simple form, each single neuron is connected to other neurons of a previous layer through adaptable synaptic weights. Knowledge is usually stored as a set of connection weights (presumably corresponding to synapse efficacy in biological neural systems). Figure 1(b) shows how information is processed through a single node. The node receives weighted activation of other nodes through its incoming connections. First, these are added up (summation). The result is then passed through an activation function; the outcome is the activation of the node. For each of the outgoing connections, this activation value is multiplied with the specific weight and transferred to the next node [24].