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

Review of air fuel ratio prediction and control methods

Air pollution is one of main challenging issues nowadays that researchers have been trying to address.The emissions of vehicle engine exhausts are responsible for 50 percent of air pollution. Different types of emissions emit from vehicles including carbon monoxide, hydrocarbons, NOX, and so on. There is a tendency to develop strategies of engine control which work in a fast way. Accomplishing this task will result in a decrease in emissions which coupled with the fuel composition can bring about the best performance of the vehicle engine.Controlling the Air-Fuel Ratio (AFR) is necessary, because the AFR has an enormous impact on the effectiveness of the fuel and reduction of emissions.This paper is aimed at reviewing the recent studies on the prediction and control of the AFR, as a bulk of research works with different approaches, was conducted in this area.These approaches include both classical and modern methods, namely Artificial Neural Networks (ANN), Fuzzy Logic, and Neuro-Fuzzy Systems are described in this paper.The strength and the weakness of individual approaches will be discussed at length

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

Studies on SI engine simulation and air/fuel ratio control systems design

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.More stringent Euro 6 and LEV III emission standards will immediately begin execution on 2014 and 2015 respectively. Accurate air/fuel ratio control can effectively reduce vehicle emission. The simulation of engine dynamic system is a very powerful method for developing and analysing engine and engine controller. Currently, most engine air/fuel ratio control used look-up table combined with proportional and integral (PI) control and this is not robust to system uncertainty and time varying effects. This thesis first develops a simulation package for a port injection spark-ignition engine and this package include engine dynamics, vehicle dynamics as well as driving cycle selection module. The simulations results are very close to the data obtained from laboratory experiments. New controllers have been proposed to control air/fuel ratio in spark ignition engines to maximize the fuel economy while minimizing exhaust emissions. The PID control and fuzzy control methods have been combined into a fuzzy PID control and the effectiveness of this new controller has been demonstrated by simulation tests. A new neural network based predictive control is then designed for further performance improvements. It is based on the combination of inverse control and predictive control methods. The network is trained offline in which the control output is modified to compensate control errors. The simulation evaluations have shown that the new neural controller can greatly improve control air/fuel ratio performance. The test also revealed that the improved AFR control performance can effectively restrict engine harmful emissions into atmosphere, these reduce emissions are important to satisfy more stringent emission standards

A Tabulated-Chemistry Approach applied to a Quasi-Dimensional Combustion Model for a Fast and Accurate Knock Prediction in Spark-Ignition Engines

The description of knock phenomenon is a critical issue in a combustion model for Spark-Ignition (SI) engines. The most known theory to explain this phenomenon is based on the Auto-Ignition (AI) of the end-gas, ahead the flame front. The accurate description of this process requires the handling of various aspects, such as the impact of the fuel composition, the presence of residual gas or water in the burning mixture, the influence of cool flame heat release, etc. This concern can be faced by the solution of proper chemistry schemes for gasoline blends. Whichever is the modeling environment, either 3D or 0D, the on-line solution of a chemical kinetic scheme drastically affects the computational time. In this paper, a procedure for an accurate and fast prediction of the hydrocarbons auto-ignition, applied to phenomenological SI engine combustion models, is proposed. It is based on a tabulated approach, operated on both ignition delay times and reaction rates. This technique, widely used in 3D calculations, is extended to 0D models to overcome the inaccuracies typical of the most common ignition delay approaches, based on the Livengood-Wu integral solution. The aim is to combine the predictability of a detailed chemistry with an acceptable computational effort. First, the tabulated technique is verified through comparisons with a chemical solver for a semi-detailed kinetic scheme in constant-pressure and constant-volume configurations. Then a phenomenological model, based on the end-gas AI computation, is utilized to predict the knock occurrence in different SI engines, including both naturally-aspirated and turbocharged architectures. 0D/1D simulations are performed both with an online solution of the chemistry and employing the tabulated approach. Assessment with reference KLSA values shows that the knock model, based on the tabulated chemistry, is able to well reproduce the essential features of the auto-ignition process in the analyzed engines, with a limited impact on the computational time

Adaptive Backstepping Controller for Uncertain Systems With Unknown Input Time-Delay. Application to SI Engines

International audienceIn this paper, we study the equilibrium regulation of potentially unstable linear systems with an unknown input time-delay and unknown parameters in the plant. We extend recent results from the literature where such systems are treated using a backstepping approach applied to a distributed parameters system representation of the delay. We develop a local result, robust to delay errors and apply it for the control of the Air-Fuel Ratio in Spark Ignition engines. A proof of convergence is established for this particular example. Experimental results stress the relevance of the proposed control algorithm

Online Learning of a Neural Fuel Control System for Gaseous Fueled SI Engines

This dissertation presents a new type of fuel control algorithm for gaseous fuelled vehicles. Gaseous fuels such as hydrogen and natural gas have been shown to be less polluting than liquid fuels such as gasoline, both at the tailpipe and on a total cycle basis. Unfortunately, it can be expensive to convert vehicles to gaseous fuels, partially due to small production runs for these vehicles. One of major development costs for a new vehicle is the development and calibration of the fuel controller. The research presented here includes a fuel controller which does not require an expensive calibration phase.The controller is based upon a two-part model, separating steady state and dynamic effects. This model is then used to estimate the optimum fuelling for the measured operating condition. The steady state model is calculated using an artificial neural network with an online learning scheme, allowing the model to continually update to improve the controller's performance. This is important during both the initial learning of the characteristics of a new engine, as well as tracking changes due to wear or damage.The dynamic model of the system is concerned with the significant transport delay between the time the fuel is injected and when the exhaust gas oxygen sensor makes the reading. One significant result of this research is the realization that a previous commonly used model for this delay has become significantly less accurate due to the shift from carburettors or central point injection to port injection.In addition to a description of the control scheme used, this dissertation includes a new method of algebraically inverting a neural network, avoiding computationally expensive iterative methods of optimizing the model. This can greatly speed up the control loop (or allow for less expensive, slower hardware).An important feature of a fuel control scheme is that it produces a small, stable limit cycle between rich and lean fuel-air mixtures. This dissertation expands the currently available models for the limit cycle characteristics of a system with a linear controller as well as developing a similar model for the neural network controller by linearizing the learning scheme.One of the most important aspects of this research is an experimental test, in which the controller was installed on a truck fuelled by natural gas. The tailpipe emissions of the truck with the new controller showed better results than the OEM controller on both carbon monoxide and nitrogen oxides, and the controller required no calibration and very little information about the properties of the engine.The significant original contributions resulting from this research include: -collection and summarization of previous work, -development of a method of automatically determining the pure time delay between the fuel injection event and the feedback measurement, -development of a more accurate model for the variability of the transport delay in modern port injection engines, -developing a fuel-air controller requiring minimal knowledge of the engine's parameters, -development of a method of algebraically inverting a neural network which is much faster than previous iterative methods, -demonstrating how to initialize the neural model by taking advantage of some important characteristics of the system, -expansion of the models available for the limit cycle produced by a system with a binary sensor and delay to include integral controllers with asymmetrical gains, -development of a limit cycle model for the new neural controller, and -experimental verification of the controller's tailpipe emissions performance, which compares favourably to the OEM controller

Modeling and Model-Based Control Of Multi-Mode Combustion Engines for Closed-Loop SI/HCCI Mode Transitions with Cam Switching Strategies.

Homogeneous charge compression ignition (HCCI) combustion has been investigated by many researchers as a way to improve gasoline engine fuel economy through highly dilute unthrottled operation while maintaining acceptable tailpipe emissions. A major concern for successful implementation of HCCI is that it's feasible operating region is limited to a subset of the full engine regime, which necessitates mode transitions between HCCI and traditional spark ignition (SI) combustion when the HCCI region is entered/exited. The goal of this dissertation is to develop a methodology for control-oriented modeling and model-based feedback control during such SI/HCCI mode transitions. The model-based feedback control approach is sought as an alternative to those in the SI/HCCI transition literature, which predominantly employ open-loop experimentally derived actuator sequences for generation of control input trajectories. A model-based feedback approach has advantages both for calibration simplicity and controller generality, in that open-loop sequences do not have to be tuned, and that use of nonlinear model-based calculations and online measurements allows the controller to inherently generalize across multiple operating points and compensate for case-by-case disturbances. In the dissertation, a low-order mean value modeling approach for multi-mode SI/HCCI combustion that is tractable for control design is described, and controllers for both the SI to HCCI (SI-HCCI) and HCCI to SI (HCCI-SI) transition are developed based on the modeling approach. The model is shown to fit a wide range of steady-state actuator sweep data containing conditions pertinent to SI/HCCI mode transitions, and is extended to capture transient SI-HCCI transition data through using an augmented residual gas temperature parameter. The mode transition controllers are experimentally shown to carry out SI-HCCI and HCCI-SI transitions in several operating conditions with minimal tuning, though the validation in the SI-HCCI direction is more extensive. The model-based control architecture is also equipped with an online parameter updating routine, to attenuate error in model-based calculations and improve robustness to engine aging and cylinder to cylinder variability. Experimental examples at multiple operating conditions illustrate the ability of the parameter update routine to improve controller performance by using transient data to tune the model parameters for enhanced accuracy during SI-HCCI mode transitions.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113351/1/pgoz_1.pd