Engine Calibration Research Articles

Reduction of experimental effort in conventional engine calibration process by using reduced order model

The calibration process of the internal combustion engine becomes more costly and time-consuming than ever due to complexity of the engines and stringent emission regulations. The accurate and fast calibration of the engine requires a lot of performance tests which demands time, cost, and expert manpower as well as test facilities that must be taken into considerations. This process needs a large number of test points which should be examined to get an optimum map of the engine. Although some works have been done on reducing test time for the engine calibration, still the process timing remains one of the challenges for the automotive industry. The present study aims to decrease the calibration cost and time by employing the proper orthogonal decomposition (POD) method. The POD-based reduced order model is applied to reduce the number of experimental engine tests to regenerate the entire calibration table by using only a few operating points accurately. The low, mid-range and high RPM test points of a small single-cylinder, two-stroke engine are chosen as the input to the mathematical model to regenerate the missing calibration data. The comparison between the generated and the corresponding experimental data indicates that they are in good agreement (<10% difference) which implies the POD model is capable to decrease the number of test points and consequently the run time of dynamometer. Furthermore, although this approach is implemented for a conventional calibration process, however, the methodology can be extended to complex calibration procedures.

A process for an efficient heat release prediction at the concepts screening stage of gasoline engine development

In recent years, the exploration of new combustion technologies has accelerated in response to increasingly stringent emissions regulations and fuel economy demands. Virtual engineering tools, that enable the screening of novel hardware and engine calibrations at the early stage of engine development, have become imperative to meet new emission regulations. One-dimensional engine simulations are used at the start of the design of a new engine to define the overall combustion system geometries. Later, more complex three-dimensional computational fluid dynamics calculations are coupled to one-dimensional engine system codes to optimise initial concept geometries and define a system design ready for prototyping. To provide meaningful results, one-dimensional engine system codes often use empirical-based combustion models to calculate the engine burn rate. Moreover, realistic engine burn rates responses, for the entire engine map and for different calibrations, are required to provide three-dimensional computational fluid dynamics codes with correct boundary conditions during the design optimisation phase. Thus, the burn characteristic of new non-traditional combustion solution, for which little experimental data are available, needs to be initially assumed. To improve virtual development and reduce this uncertainty, the industry’s attention shifted towards quasi-dimensional combustion models capable of providing engine burn rate predictions. Within the quasi-dimensional modelling framework, turbulence models, adding extra user-input variables, are required to capture the effect of different combustion chamber geometries on the engine combustion rate. Rigorous validation of zero-dimensional turbulence models for different engine concepts and calibrations is therefore needed to enable quasi-dimensional combustion models to predict the engine burn rate. An alternative methodology, with limited dependency on previous test data, is required to enhance the exploration of novel combustion strategies and geometric architectures. An available process, based on a quasi-dimensional combustion stochastic reactor model, a one-dimensional engine system model and non-combusting three-dimensional computational fluid dynamics calculations, was used for this work. The approach uses limited non-combusting computational fluid dynamics calculations and a previously developed scaling factor response for the stochastic reactor model turbulence input ( τSRM) to quickly predict the engine rate of heat release. In this work, the scaling factor response was assessed against two different engine variants over a variety of engine operating conditions. Moreover, the same response was used to predict the effect of different bore-to-stroke ratios on the engine combustion rate and knock tolerance. Non-combusting computational fluid dynamics and one-dimensional engine system simulations have been carried out to investigate changes in turbulence characteristics due to different engine variants and bore-to-stroke ratios. It was shown that limited number of non-combusting computational fluid dynamics runs is required to characterise the in-cylinder turbulence for each explored engine variant. The scaling factor response was used to manipulate the turbulence input ( τSRM) resulting in good engine burn rates predictions for the explored engine variants and bore-to-stroke ratios. The presented methodology showed augmented predictive capabilities and has potential to move the engine development towards a less hardware dependent virtual approach, offering a practical solution for the exploration of new engine concepts.

A predictive combustion model for one-dimensional gasoline engine simulation

A new predictive combustion model for a one-dimensional computational fluid dynamics tool in the multibody dynamics processes of gasoline engines was developed and validated. The model consists of (1) a turbulent burning velocity model featuring a flame radius–based transitional function, steady burning velocity that considers local quenching using the Karlovitz number and laminarization by turbulent Reynolds number, as well as turbulent flame thickness and its quenching model near the liner wall, and (2) a knock model featuring auto-ignition by the Livengood–Wu integration and ignition delay time obtained using a full-kinetic model. The proposed model and previous models were verified under a wide range of operating conditions using engines with widely different specifications. Good agreement was only obtained for combustion characteristics by the proposed model without requiring individual calibration of model constants. The model was also evaluated for utilization after prototyping. Improved accuracy, especially of ignition timing, was obtained after further calibration using a small amount of engine data. It was confirmed that the proposed model is highly accurate at the early stage of the engine development process, and is also applicable for engine calibration models that require higher accuracy.

A Novel Approach to Test Cycle-Based Engine Calibration Technique Using Genetic Algorithms to Meet Future Emissions Standards

A Novel Approach to Test Cycle-Based Engine Calibration Technique Using Genetic Algorithms to Meet Future Emissions Standards

Deep-learning based in-cylinder pressure modeling and resolution of ion current signals

Engine calibration becomes much more costly due to strict exhaust emission regulations and individual consumer needs. Measuring the in-cylinder pressure could be a promising alternative for various sensors, thus reducing the calibration cost because the measurement provides diverse, low-delay, and precise information inside the cylinders. However, a piezoelectric pressure sensor for this measurement is too expensive, which prevents its use in production vehicles. The less-expensive, more reliable, and responsive ion current measurement provides signals that highly correlate with the in-cylinder combustion process and pressure. Many proposed methods correlate the ion current and pressure through chemical-kinetic models or manually tuned machine-learning models. A few methods can automatically estimate the pressure change or predict the peak pressure with the ion current, which provides valuable information for advance control and engine monitoring. In this paper, an autoencoder deep-learning model is developed that is unprecedentedly well fit for these two tasks. It automatically encodes ion current signals into a semantic representation with a convolutional neural network. With this, the model can either predict the peak pressure or estimate the pressure change through the gated-recurrent-unit decoder of the model. The evaluation of the model predictions and estimations is performed on an actual engine-based dataset, where the model demonstrates state-of-the-art performance on both tasks, and the mean relative error is 7.84% and 19.68%, respectively. Additionally, an orthogonal analysis method is applied to study the resolution of the ion current signals, making it possible to categories these signals by converting them into semantic representations.

Optimal Calibration Strategy of a Hybrid Electric Vehicle Equipped with an Ultra-Lean Pre-Chamber SI Engine for the Minimization of CO2 and Pollutant Emissions

The complexity of modern hybrid powertrains poses new challenges for the optimal control concerning, on one hand, the thermal engine to maximize its efficiency, and, on the other hand, the vehicle to minimize the noxious emissions and CO2. In this context, the engine calibration has to be conducted by considering simultaneously the powertrain management, the vehicle characteristics, and the driving mission. In this work, a calibration methodology for a two-stage boosted ultra-lean pre-chamber spark ignition (SI) engine is proposed, aiming at minimizing its CO2 and pollutant emissions. The engine features a flexible variable valve timing (VVT) control of the valves and an E-compressor, coupled in series to a turbocharger, to guarantee an adequate boost level needed for ultra-lean operation. The engine is simulated in a refined 1D model. A simplified methodology, based on a network of proportional integral derivative (PID) controllers, is presented for the calibration over the whole operating domain. Two calibration variants are proposed and compared, characterized by different fuel and electric consumptions: the first one aims to exclusively maximize the brake thermal efficiency, and the second one additionally considers the electric energy absorbed by the E-compressor and drained from the battery. After a verification against the outcomes of an automatic optimizer, the calibration strategies are assessed based on pollutant and CO2 emissions along representative driving cycles by vehicle simulations. The results highlight slightly lower CO2 emissions with the calibration approach that minimizes the E-compressor consumption, thus revealing the importance of considering the engine calibration phase, the powertrain management, the vehicle characteristics, and its mission.

Assessment of Direct Injected Liquefied Petroleum Gas-Diesel Blends for Ultra-Low Soot Combustion Engine Application

Technological and economic concerns correlated to fulfilling future emissions and CO2 standards require great research efforts to define an alternative solution for low emissions and highly efficient propulsion systems. Alternative fuel formulation could contribute to this aim. Liquefied petroleum gas (LPG) with lower carbon content than other fossil fuels and which is easily vaporized at ambient conditions has the advantage of lowering CO2 emissions and optimizing the combustion process. Liquefied petroleum gas characteristics and availability makes the fuel a promising alternative for internal combustion engines. The possible combination of using it in high-efficiency compression ignition engines makes it worth analyzing the innovative method of using LPG as a blend component in diesel. Few relevant studies are detectable in literature in this regard. In this study, two blends containing diesel and LPG, in volume ratios 20/80 and 35/65, respectively, were formulated and utilized. Their effects on combustion and emissions performance were assessed by performing proper experimental tests on a modern light-duty single-cylinder engine test rig. Reference operating points at conventional engine calibration settings were examined. A specific exhaust gas recirculation (EGR) parametrization was performed evaluating the LPG blends’ potential in reducing the smoke emissions at standard engine-out NOx levels. The results confirm excellent NOx-smoke trade-off improvements with smoke reductions up to 95% at similar NOx and efficiency. Unburnt emissions slightly increase, and to acceptable levels. Improvements, in terms of indicated specific fuel consumption (ISFC), are detected in the range of 1–3%, as well as the CO2 decrease proportionally to the mixing ratio.

Influence of Ash-Soot Interactions on the Reactivity of Soot from a Gasoline Direct Injection Engine.

Gasoline particulate filters (GPF) are being utilized in certain markets on gasoline direct injection (GDI) vehicles to reduce tailpipe particulate emissions as required by particle number regulations. GPF filtration efficiency is dependent on soot build-up within the filter. Since soot oxidizes within the GPF during normal vehicle operation, an understanding of soot reactivity is important for optimizing aftertreatment architecture and engine calibration. Past work has indicated that gasoline soot reactivity may depend on levels of metallic ash species. In this work, carbonaceous particulate matter from a GDI engine are evaluated from engine operation at a consistent speed and load but with different levels of fuel injection pressures and timings to vary the relative ash to soot ratio. Soot reactivity is found to vary significantly with the ratio of ash to soot present. Interestingly, the more reactive soots possess a unique oxidation profile by which a conventional Arrhenius type expression cannot be used to quantify reactivity. To understand the mechanisms driving such distinct oxidation differences, soot samples are analyzed after being partially oxidized. Particulate characteristics are evaluated by x-ray photoelectron spectroscopy (XPS), Raman spectroscopy, high-resolution transmission electron microscopy (HR-TEM), and scanning transmission electron microscopy with energy dispersive spectroscopy (STEM + EDS). A mechanism is proposed that may explain further why ash affects gasoline soot reactivity to the extent seen in this and other work.

Reduction of RANS/LES combustion sub-models for quasi-dimensional spark ignition engine simulations and evaluation of the modelling assumptions with DNS

Despite the significant improvement of computational resources, large eddy simulations (LES) are too expensive to be applied to wide ranges of operating conditions and multiple engine architectures. Efficient models employed in quasi/zero-dimensional approaches may offer an attractive alternative for engine calibration or optimisation. Despite their fair predictive capability, validity is typically limited to around their calibration region and is subject to uncertainties that stem from heuristic simplifications. The validity of the individual sub-models is rarely assessed using detailed three-dimensional (3-D) simulations over a wide range of combustion regimes. The objectives of this work are the following: (i) to present a formal reduction of widely applied 3-D LES combustion sub-models found in the literature with particular emphasis on the early flame development and flame-wall interaction, (ii) to assess the assumptions/approximations introduced in quasi-dimensional (Q-D) modelling using Direct Numerical Simulation (DNS), and (iii) to refine the Q-D models used currently, using formally-derived sub-models.It is found that many of the refinements introduced noticeably improve the accuracy of the Q-D model. The functional form of the models obtained through formal reduction of various LES combustion sub-models presents similarities and agrees well with the heuristic functions that can be found in existing Q-D models. Ultimately, this study enhances the transferability of insights from fundamental investigations to real engine applications, which can be useful for future Q-D model development and validation.

Application of Active Subspace Method in Gas Exchange Strategy Calibration on a Variable Valve Timing Gasoline Engine

Abstract Modern internal combustion (IC) engines have complex configurations with many parameters to be tuned, and this system complication usually leads to a large amount of engine calibration experiments. In this study, the active subspace method was used to build predictive models for the gas exchange-related parameters, including volumetric efficiency, intake mass flow and pumping loss, and the power-related parameters, including engine torque and engine power. The results show that the predicted outputs fit well with the experimental data, with satisfactory coefficients of determination and average absolute errors (AAE). Further, the contributions and influence directions of the input parameters to the outputs were provided based on a sensitivity analysis, which is consistent with the existing knowledge, and therefore, verifies the reliability of the predictive model built based on the active subspace method. Finally, the relation between the training group size and the prediction performance was explored. It is shown that a reduction, up to 66%, in the training group size is still able to maintain good predictive performances of the models, indicating the substantial capability of the active subspace method to reduce the experimental efforts.

Integration of Virtual Engine Calibration into the Real-world Development Environment

Integration of Virtual Engine Calibration into the Real-world Development Environment

An interdisciplinary review on calibration strategies of engine management system for diverse alternative fuels in IC engine applications

As the vehicle technology evolves and becomes more complex, the ability to monitor certain uncontrollable factors is possible through various engine management system calibration methods. Additionally, different optimization approaches assist in improving engine output characteristics. Advances in fuel production from unique feedstocks have paved a way for the introduction of innovative flex-fuel engines. These engines are made multi-fuel adaptive by utilizing appropriate calibration and optimization approaches. Since numerous researches have been carried out in internal combustion engine calibration, operating on alternative fuels, it is quite hard to maintain a record for this wealth of information. Therefore to bridge this issue, this study intends to organize and provide the reader with a retrospective view of selective studies undergone in the field of engine calibration using different tuning techniques. The objective of this study is to assist engine calibration enthusiasts on selecting an apt engine calibration technique for their application which can be flexible for multiple fuel types. Studies revealed that model-based calibration assisted by Gaussian process modelling and Design of Experiment application is adequate for calibrating an engine operating on alternative fuels. Additionally, calibration methodologies followed for certain fuel types and compositions are highlighted in detail. Furthermore, an advanced calibration methodology is proposed for flex-fuel engines in this study based on the knowledge gained through the preparation of this article. To further enhance accuracy, complex optimizers like neural networks can be used in the proposed methodology to initiate real-time calibration for ideal flex-fuel engine output responses.

Variable smoothing of optimal diesel engine calibration for improved performance and drivability during transient operation

The model-based method to define the optimal calibration maps for important diesel engine parameters may involve three major steps. First, the engine speed and load domain – in which the engine is operated – are identified. Then, a global engine model is created, which can be used for offline simulations to estimate engine performance. Finally, optimal calibration maps are obtained by formulating and solving an optimisation problem, with the goal of minimising fuel consumption while meeting constraints on pollutant emissions. This last step in the calibration process usually involves smoothing of the maps in order to improve drivability. This article presents a method to trade off map smoothness, brake-specific fuel consumption and nitrogen oxide emissions. After calculating the optimal but potentially non-smooth calibration maps, a variation-based smoothing method is employed to obtain different levels of smoothness by adapting a single tuning parameter. The method was experimentally validated on a heavy-duty diesel engine, and the non-road transient cycle was used as a case study. The error between the reference and actual engine torque was used as a metric for drivability, and the error was found to decrease with increasing map smoothness. After having obtained this trade-off for various fixed levels of smoothness, a time-varying smoothness calibration was generated and tested. Experimental results showed that, with a time-varying smoothness strategy, nitrogen oxide emissions could be reduced by 4%, while achieving the same drivability and fuel consumption as in the case of a fixed smoothing strategy.

Operational Profile Based Optimization Method for Maritime Diesel Engines

This paper presents an approach to a new engine calibration method that takes the engine’s operational profile into account. This method has two main steps: modeling and optimization. The Design of Experiments method is first conducted to model the engine’s responses such as Brake Specific Fuel Consumption (BSFC) and Nitrogen Oxide ( N O x ) emissions as the functions of fuel injection timing, common rail pressure and charged air pressure. These response surface models are then used to minimize the fuel consumption during a year, according to a typical load profile of a ferry, and to fulfill the N O x limits set by International Maritime Organization (IMO) regulations, Tier II, test cycle E2. The Sequential Quadratic Programming algorithm is used to solve this minimization problem. The results showed that the fuel consumption can be effectively reduced with the flexibility to trade it off with the N O x emissions while still fulfilling the IMO regulations. In general, this method can decrease the manual calibration effort and improve the engine’s performance with a tailored setting for individual operational profiles.

Clean and efficient dual-fuel combustion using OMEx as high reactivity fuel: Comparison to diesel-gasoline calibration

From previous results in a single-cylinder engine platform, it can be concluded that the dual-mode dual-fuel (DMDF) concept can be a potential solution to overcome the major constraints found with other single-fuel low temperature combustion modes. To extend these findings to a real application, this work evaluates the potential of the diesel-gasoline DMDF concept on a multi-cylinder 8L engine in terms of performance and emissions. To do this, a full engine calibration map was obtained following a specific methodology. The emissions results show that diesel-gasoline DMDF allows to achieve EURO VI NOx and soot emissions in a great portion of the engine map. Nonetheless, the levels of these pollutant at high load conditions exceed the EURO VI limits by far due to the need of implementing a diffusive combustion strategy with high EGR levels to avoid excessive in-cylinder pressure gradients. To mitigate this issue, the use of Oxymethylene ether (OMEx) instead of diesel fuel is proposed. A dedicated engine calibration was developed for the OMEx-gasoline DMDF concept following the same methodology. The results show that the oxygen content in the OMEx molecule allows to achieve a fully EUVI compliant engine calibration in terms of NOx with engine-out soot levels lower than 0.01 g/kWh. Moreover, due to the lower stoichiometric air–fuel ratio with this fuel, the air management system requirements are lower, reducing the pumping losses and increasing the brake thermal efficiency in most of the calibration map.

The Measurement of Fast Transient Emissions During Real World Driving

Some of the most challenging aspects of preparing vehicles to comply with the new RDE (Real Driving Emissions) legislation are the calibration of engines to minimize emissions during numerous short lived transients. Fast response emissions analyzers are necessary to resolve rapid transient emissions and have been deployed simultaneously in the engine-out and tailpipe exhaust gas stream of several vehicles. The methodology presented and capability of the instrumentation enables fine resolution of emissions events leading to the opportunity for fine tuning of controller performance. Data has also been logged from the vehicles' ECUs and combined with accurate GPS data. The results show that certain road lay-outs (e.g., congested junctions and speed bumps) can provoke significant tailpipe NOx emissions; often emitting very high levels near schools where traffic calming measures have been adopted for road safety. The lean excursions and insufficient rich purge during such transients are generally shown to be the cause of such emitting events.

Detailed Analyses and Correlation of Fuel Effects on Stochastic Preignition

<div class="section abstract"><div class="htmlview paragraph">Stochastic or Low-Speed Preignition (SPI or LSPI) is an undesirable abnormal combustion phenomenon encountered in spark-ignition engines. It is characterized by very early heat release and high cylinder pressure and can cause knock, noise and ultimately engine damage. Much of the focus on mitigating SPI has been directed towards the engine oil formulation, leading to the emergence of the Sequence IX test and second-generation GM dexos® oil requirements. Engine design, calibration and fuels also contribute to the prevalence of SPI. As part of a recently completed research consortium, a series of engine tests were completed to determine the impact of fuel composition on SPI frequency. The fuel blends had varying levels of paraffins, olefins, aromatics and ethanol. Engine tests were performed on a 2-liter turbocharged, direct-injection spark-ignition engine and comprised of multiple repeats of low-speed, high-load, steady-state test segments, interspersed with low load segments, with engine calibration and boundary conditions adjusted to amplify the appearance of SPI. Comprehensive analyses of the fuel properties and chemical composition revealed the key fuel characteristics, notably the Particulate Mass Index (PMI), that were best correlated with the appearance of SPI. In addition, the impact of fuel composition and properties on the severity of preignition events were examined by comparing the peak knock values for all abnormal combustion cycles. The frequency of multi-cycle events was also investigated. The paper suggests that not only the number of preignition events but also the severity of preignition should be considered when evaluating fuel impacts on SPI. Most importantly, for this work, is the determination that particulate forming tendency of the fuel correlates to its SPI tendency, although whether this is causal or correlational cannot be discerned.</div></div>

Development and Assessment of an Integrated 1D-3D CFD Codes Coupling Methodology for Diesel Engine Combustion Simulation and Optimization

In this paper, an integrated and automated methodology for the coupling between 1D- and 3D-CFD simulation codes is presented, which has been developed to support the design and calibration of new diesel engines. The aim of the proposed methodology is to couple 1D engine models, which may be available in the early stage engine development phases, with 3D predictive combustion simulations, in order to obtain reliable estimates of engine performance and emissions for newly designed automotive diesel engines. The coupling procedure features simulations performed in 1D-CFD by means of GT-SUITE and in 3D-CFD by means of Converge, executed within a specifically designed calculation methodology. An assessment of the coupling procedure has been performed by comparing its results with experimental data acquired on an automotive diesel engine, considering different working points, including both part load and full load conditions. Different multiple injection schedules have been evaluated for part-load operation, including pre and post injections. The proposed methodology, featuring detailed 3D chemistry modeling, was proven to be capable assessing pollutant formation properly, specifically to estimate NOx concentrations. Soot formation trends were also well-matched for most of the explored working points. The proposed procedure can therefore be considered as a suitable methodology to support the design and calibration of new diesel engines, due to its ability to provide reliable engine performance and emissions estimations from the early stage of a new engine development.

Methods of sensitivity analysis in model-based calibration

The effort and time demand for the calibration of electronic control systems for internal combustion engines on test benches is rising constantly for a number of years. This is mainly driven by new engines and powertrain technologies as well as by the rising quantity of series and vehicle variations. In the engine calibration process with the objective for optimization of fuel consumption and emission values, the number of parameters is large and the evaluation on a test bench is expensive. Since a certain target quantity is usually dependent on a range of various parameters, the sensitivity of system inputs on outputs should be identified. The goal of this approach is a reduction of the dimension in the design of experiments to the most important factors. In this study, the approaches by linear models, nonlinear models and mutual information are introduced and are compared with measurement data.

Experimental assessment of emissions maps of a single-cylinder compression ignition engine powered by diesel and palm oil biodiesel-diesel fuel blends

Diesel engines applications cover a broad spectrum, ranging from vehicles that transport passengers and move goods to specialized vehicles and equipment used in the construction and agriculture industries. However, diesel engines are a significant source of pollutant emissions that contribute to poor air quality, negative human health impacts, and climate change. This experimental case study develops emission maps based on statistical models for a single-cylinder, four-stroke, air-cooled diesel engine as a function of torque and engine speed. The tested fuels were 100% diesel (B0), and blends with 5% (B5) and 10% (B10) biodiesel originating from African oil palm (Elaeis guineensis). The study explores the individual contributions of NO and NO2 to NOx and discusses the correlation between CO and O2 emission maps. The statistical models of CO, CO2, and O2 feature R2 adjusted values greater than 0.8, while the models of NO and NO2 show R2 adjusted values of around 0.6. The apparent discrepancies in CO emission trends among previous studies are explained. The emission maps developed here are a practical alternative to predictive models and can assist in engine calibration and aftertreatment optimization while saving time and costs.