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

A STUDY OF MODEL-BASED CONTROL STRATEGY FOR A GASOLINE TURBOCHARGED DIRECT INJECTION SPARK IGNITED ENGINE

To meet increasingly stringent fuel economy and emissions legislation, more advanced technologies have been added to spark-ignition (SI) engines, thus exponentially increase the complexity and calibration work of traditional map-based engine control. To achieve better engine performance without introducing significant calibration efforts and make the developed control system easily adapt to future engines upgrades and designs, this research proposes a model-based optimal control system for cycle-by-cycle Gasoline Turbocharged Direct Injection (GTDI) SI engine control, which aims to deliver the requested torque output and operate the engine to achieve the best achievable fuel economy and minimum emission under wide range of engine operating conditions. This research develops a model-based ignition timing prediction strategy for combustion phasing (crank angle of fifty percent of the fuel burned, CA50) control. A control-oriented combustion model is developed to predict burn duration from ignition timing to CA50. Using the predicted burn duration, the ignition timing needed for the upcoming cycle to track optimal target CA50 is calculated by a dynamic ignition timing prediction algorithm. A Recursive-Least-Square (RLS) with Variable Forgetting Factor (VFF) based adaptation algorithm is proposed to handle operating-point-dependent model errors caused by inherent errors resulting from modeling assumptions and limited calibration points, which helps to ensure the proper performance of model-based ignition timing prediction strategy throughout the entire engine lifetime. Using the adaptive combustion model, an Adaptive Extended Kalman Filter (AEKF) based CA50 observer is developed to provide filtered CA50 estimation from cyclic variations for the closed-loop combustion phasing control. An economic nonlinear model predictive controller (E-NMPC) based GTDI SI engine control system is developed to simultaneously achieve three objectives: tracking the requested net indicated mean effective pressure (IMEPn), minimizing the SFC, and reducing NOx emissions. The developed E-NMPC engine control system can achieve the above objectives by controlling throttle position, IVC timing, CA50, exhaust valve opening (EVO) timing, and wastegate position at the same time without violating engine operating constraints. A control-oriented engine model is developed and integrated into the E-NMPC to predict future engine behaviors. A high-fidelity 1-D GT-POWER engine model is developed and used as the plant model to tune and validate the developed control system. The performance of the entire model-based engine control system is examined through the software-in-the-loop (SIL) simulation using on-road vehicle test data

MODELING OF TRANSFER PATH FOR DETERMINATION OF COMBUSTION AND NOISE METRICS ON DIESEL ENGINES

Determination of combustion metrics for a diesel engine has the potential of providing feedback for closed-loop combustion phasing control to meet current and upcoming emission and fuel consumption regulations. This thesis focused on the estimation of combustion metrics including start of combustion (SOC), crank angle location of 50% cumulative heat release (CA50), peak pressure crank angle location (PPCL), and peak pressure amplitude (PPA), peak apparent heat release rate crank angle location (PACL), mean absolute pressure error (MAPE), and peak apparent heat release rate amplitude (PAA). In-cylinder pressure has been used in the laboratory as the primary mechanism for characterization of combustion rates and more recently in-cylinder pressure has been used in series production vehicles for feedback control. However, the intrusive measurement with the in-cylinder pressure sensor is expensive and requires special mounting process and engine structure modification. As an alternative method, this work investigated block mounted accelerometers to estimate combustion metrics in a 9L I6 diesel engine. So the transfer path between the accelerometer signal and the in-cylinder pressure signal needs to be modeled. Depending on the transfer path, the in-cylinder pressure signal and the combustion metrics can be accurately estimated - recovered from accelerometer signals. The method and applicability for determining the transfer path is critical in utilizing an accelerometer(s) for feedback. Single-input single-output (SISO) frequency response function (FRF) is the most common transfer path model; however, it is shown here to have low robustness for varying engine operating conditions. This thesis examines mechanisms to improve the robustness of FRF for combustion metrics estimation. First, an adaptation process based on the particle swarm optimization algorithm was developed and added to the single-input single-output model. Second, a multiple-input single-output (MISO) FRF model coupled with principal component analysis and an offset compensation process was investigated and applied. Improvement of the FRF robustness was achieved based on these two approaches. Furthermore a neural network as a nonlinear model of the transfer path between the accelerometer signal and the apparent heat release rate was also investigated. Transfer path between the acoustical emissions and the in-cylinder pressure signal was also investigated in this dissertation on a high pressure common rail (HPCR) 1.9L TDI diesel engine. The acoustical emissions are an important factor in the powertrain development process. In this part of the research a transfer path was developed between the two and then used to predict the engine noise level with the measured in-cylinder pressure as the input. Three methods for transfer path modeling were applied and the method based on the cepstral smoothing technique led to the most accurate results with averaged estimation errors of 2 dBA and a root mean square error of 1.5dBA. Finally, a linear model for engine noise level estimation was proposed with the in-cylinder pressure signal and the engine speed as components

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

Real-time implementation of a sensor validation scheme for a heavy-duty diesel engine

With ultra-low exhaust emissions standards, heavy-duty diesel engines (HDDEs) are dependent upon a myriad of sensors to optimize power output and exhaust emissions. Apart from acquiring and processing sensor signals, engine control modules should also have capabilities to report and compensate for sensors that have failed. The global objective of this research was to develop strategies to enable HDDEs to maintain nominal in-use performance during periods of sensor failures. Specifically, the work explored the creation of a sensor validation scheme to detect, isolate, and accommodate sensor failures in HDDEs. The scheme not only offers onboard diagnostic (OBD) capabilities, but also control of engine performance in the event of sensor failures. The scheme, known as Sensor Failure Detection Isolation and Accommodation (SFDIA), depends on mathematical models for its functionality. Neural approximators served as the modeling tool featuring online adaptive capabilities. The significance of the SFDIA is that it can enhance an engine management system (EMS) capability to control performance under any operating conditions when sensors fail. The SFDIA scheme updates models during the lifetime of an engine under real world, in-use conditions. The central hypothesis for the work was that the SFDIA scheme would allow continuous normal operation of HDDEs under conditions of sensor failures. The SFDIA was tested using the boost pressure, coolant temperature, and fuel pressure sensors to evaluate its performance. The test engine was a 2004 MackRTM MP7-355E (11 L, 355 hp). Experimental work was conducted at the Engine and Emissions Research Laboratory (EERL) at West Virginia University (WVU). Failure modes modeled were abrupt, long-term drift and intermittent failures. During the accommodation phase, the SFDIA restored engine power up to 0.64% to nominal. In addition, oxides of nitrogen (NOx) emissions were maintained at up to 1.41% to nominal

Output Feedback Controller for Operation of Spark Ignition Engines at Lean Conditions Using Neural Networks

Spark ignition (SI) engines operating at very lean conditions demonstrate significant nonlinear behavior by exhibiting cycle-to-cycle bifurcation of heat release. Past literature suggests that operating an engine under such lean conditions can significantly reduce NO emissions by as much as 30% and improve fuel efficiency by as much as 5%-10%. At lean conditions, the heat release per engine cycle is not close to constant, as it is when these engines operate under stoichiometric conditions where the equivalence ratio is 1.0. A neural network controller employing output feedback has shown ability in simulation to reduce the nonlinear cyclic dispersion observed under lean operating conditions. This neural network (NN) output controller consists of three NNs: a) an NN observer to estimate the states of the engine such as total fuel and air; b) a second NN for generating virtual input; and c) a third NN for generating actual control input. The uniform ultimate boundedness of all closed-loop signals is demonstrated by using the Lyapunov analysis without using the separation principle. Persistency of the excitation condition, the certainty equivalence principle, and the linearity in the unknown parameter assumptions are also relaxed. The controller is implemented for a research engine as a program running on an embeddable PC that communicates with the engine through a custom hardware interface, and the results are similar to those observed in simulation. Experimental results at an equivalence ratio of 0.77 show a drop in NO emissions by around 98% from stoichiometric levels with an improvement of fuel efficiency by 5%. A 30% drop in unburned hydrocarbons from uncontrolled case is observed at this equivalence ratio of 0.77. Similar performance was observed with the controller on a different engine