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

Flame chemiluminescence and OH LIF imaging in a hydrogen-fuelled spark-ignition engine

Research into novel internal combustion engines requires consideration of the diversity in future fuels in an attempt to reduce drastically CO2 emissions from vehicles and promote energy sustainability. Hydrogen has been proposed as a possible fuel for future internal combustion engines. Hydrogen’s wide flammability range allows higher engine efficiency with much leaner operation than conventional fuels, for both reduced toxic emissions and no CO2 gases. This paper presents results from an optical study of combustion in a spark-ignition research engine running with direct injection and port injection of hydrogen. Crank-angle resolved flame chemiluminescence images were acquired and post-processed for a series of consecutive cycles in order to calculate in-cylinder rates of flame growth. Laser induced fluorescence of OH was also applied on an in-cylinder plane below the spark plug to record detailed features of the flame front for a series of engine cycles. The tests were performed at various air-to-fuel ratios, typically in a range of φ = 0.50–0.83 at 1000 RPM with 0.5 bar intake pressure. The engine was also run with gasoline in direct-injection and port-injection modes to compare with the operation on hydrogen. The observed combustion characteristics were analysed with respect to laminar and turbulent burning velocities, as well as flame stretch. An attempt was also made to review relevant hydrogen work from the limited literature on the subject and make comparisons were appropriate

Controlled autoignition of hydrogen in a direct-injection optical engine

Research into novel internal combustion engines requires consideration of the diversity in future fuels in an attempt to reduce drastically CO2 emissions from vehicles and promote energy sustainability. Hydrogen has been proposed as a possible fuel for future internal combustion engines and can be produced from renewable sources. Hydrogen’s wide flammability range allows higher engine efficiency than conventional fuels with both reduced toxic emissions and no CO2 gases. Most previous work on hydrogen engines has focused on spark-ignition operation. The current paper presents results from an optical study of controlled autoignition (or homogeneous charge compression ignition) of hydrogen in an engine of latest spark-ignition pentroof combustion chamber geometry with direct injection of hydrogen (100 bar). This was achieved by a combination of inlet air preheating in the range 200–400 °C and residual gas recirculated internally by negative valve overlap. Hydrogen fuelling was set to various values of equivalence ratio, typically in the range ϕ = 0.40–0.63. Crank-angle resolved flame chemiluminescence images were acquired for a series of consecutive cycles at 1000 RPM in order to calculate in-cylinder rates of flame expansion and motion. Planar Laser Induced Fluorescence (LIF) of OH was also applied to record more detailed features of the autoignition pattern. Single and double (i.e. ‘split’ per cycle) hydrogen injection strategies were employed in order to identify the effect of mixture preparation on autoignition’s timing and spatial development. An attempt was also made to review relevant in-cylinder phenomena from the limited literature on hydrogen-fuelled spark-ignition optical engines and make comparisons were appropriate

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