The Optimisation of Common Rail FIE Equipped Engines Through the Use of Statistical Experimental Design, Mathematical Modelling and Genetic Algorithms

Edwards, Simon; Pilley, A.D.; Michoń, S; Fournier, G.

doi:10.4271/970346

Cited by 36 publications

(10 citation statements)

References 10 publications

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“…In response to an increased number of parameters that can be calibrated, model-based calibration has been adopted by the diesel engine industry in the last decade or so, but only for steady-state calibration [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Transient calibration is currently a manual process depending on the expertise of the engineer and is largely performed to meet regulation rather than optimize the system.…”

Section: Introductionmentioning

confidence: 99%

Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 1: data requirements, processing, and analysis

Brahma

Chi

2011

International Journal of Engine Research

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This is the first part of a study investigating a model-based transient calibration process for diesel engines. The motivation is to populate hundreds of parameters (which can be calibrated) in a methodical and optimum manner by using model-based optimization in conjunction with the manual process so that, relative to the manual process used by itself, a significant improvement in transient emissions and fuel consumption and a sizable reduction in calibration time and test cell requirements is achieved. Empirical transient modelling and optimization has been addressed in the second part of this work, while the required data for model training and generalization are the focus of the current work. Transient and steady-state data from a turbocharged multicylinder diesel engine have been examined from a model training perspective. A single-cylinder engine with external air-handling has been used to expand the steady-state data to encompass transient parameter space. Based on comparative model performance and differences in the non-parametric space, primarily driven by a high engine difference between exhaust and intake manifold pressures (DP) during transients, it has been recommended that transient emission models should be trained with transient training data. It has been shown that electronic control module (ECM) estimates of transient charge flow and the exhaust gas recirculation (EGR) fraction cannot be accurate at the high engine DP frequently encountered during transient operation, and that such estimates do not account for cylinder-to-cylinder variation. The effects of high engine DP must therefore be incorporated empirically by using transient data generated from a spectrum of transient calibrations. Specific recommendations on how to choose such calibrations, how many data to acquire, and how to specify transient segments for data acquisition have been made. Methods to process transient data to account for transport delays and sensor lags have been developed. The processed data have then been visualized using statistical means to understand transient emission formation. Two modes of transient opacity formation have been observed and described. The first mode is driven by high engine DP and low fresh air flowrates, while the second mode is driven by high engine DP and high EGR flowrates. The EGR fraction is inaccurately estimated at both modes, while EGR distribution has been shown to be present but unaccounted for by the ECM. The two modes and associated phenomena are essential to understanding why transient emission models are calibration dependent and furthermore how to choose training data that will result in good model generalization.

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Section: Introductionmentioning

confidence: 99%

Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 1: data requirements, processing, and analysis

Brahma

Chi

2011

International Journal of Engine Research

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“…So although the engine load can be estimated with the above mentioned high accuracy, only discrete values around the required load can be set. [16], [25] and [26]. We will omit here any introduction to DoE.…”

Section: Methodsmentioning

confidence: 99%

Combustion Characteristics of Hydrogen-Natural Gas Mixtures in Passenger Car Engines

Dimopoulos¹,

Boulouchos²,

Rechsteiner³

et al. 2007

SAE Technical Paper Series

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The presented concept in this study consists of a state of the art passenger car natural-gas engine fired by different hydrogen (H 2) and compressed-natural-gas (CNG) fuel blends. The hydrogen content in the fuel was varied among 5 and 15vol% corresponding to 0.6-2.1 mass%, while comparisons include also engine operation on pure CNG. Increasing hydrogen content of the fuel accelerated combustion leading to modest efficiency improvements. Combustion analysis showed that the increasing burning rates mainly affected the initial combustion phase (duration for 5% mass fraction burned). With optimal combinations of spark timing and EGR rate the achievements are additional efficiency increase with substantially lower engine-out NO x while total unburned hydrocarbons or CO-engine-out emissions are not affected. Investigations using Design of Experiments (DoE) algorithms provided a comprehensive picture of the entire parameter space. The investigations showed two fuel consumption optimal combustion domains: Low EGR and late spark timing with associated high engine-out NO x emissions as well as high EGR and early spark timing with very low engine-out NO x .

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“…Recently genetic algorithms have been introduced to optimize engine performances. Edwards et al [4] used engine models resulting from statistical experimental design as evaluation method for a standard single-objective algorithm. Senecal et al [6][7][8] developed a KIVA-GA computer code which performs CFD engine simulations in the framework of a multiobjective genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Combined Optimization Method for Common Rail Diesel Engines

Risi

Carlucci

Donateo

et al. 2002

Design, Operation, and Application of Modern Internal Combustion Engines and Associated Systems

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The optimization method proposed in the present study consists of a multi-objective genetic algorithm combined with an experimental investigation carried out on a test bench, by using a DI Diesel engine. The genetic algorithm selects the injection parameters for each operating condition whereas the output measured by the experimental apparatus determines the fitness in the optimization process. The genetic algorithm creates a random population, which evolves combining the genetic code of the most capable individuals of the previous generation. Each individual of the population is represented by a set of parameters codified with a binary string. The evolution is performed using the operators of crossover, mutation and elitist reproduction. This genetic algorithm allows competitive fitness functions to be optimized with a single optimization process. For the determination of the overall fitness function the concept of Pareto optimality has been implemented. In this work, the input variables used for the optimization method are injection parameters like start of pilot and main injection, injection pressure and duration. The engine used is a FIAT 1929 cc DI diesel engine, in which the traditional injection system has been replaced by a common rail high pressure injection system. The competitive fitness functions were determined based on the measured values of fuel consumption, emissions levels (i.e. NOx, soot, CO, CO2, HC); combustion noise and overall engine noise, for each operating conditions. The optimization was performed for different engine speed and torque conditions typical of the EC driving cycles.

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The Optimisation of Common Rail FIE Equipped Engines Through the Use of Statistical Experimental Design, Mathematical Modelling and Genetic Algorithms

Cited by 36 publications

References 10 publications

Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 1: data requirements, processing, and analysis

Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 1: data requirements, processing, and analysis

Combustion Characteristics of Hydrogen-Natural Gas Mixtures in Passenger Car Engines

A Combined Optimization Method for Common Rail Diesel Engines

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