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The integration of analytical functions into machine learning-based engine models represents a significant advancement in predictive performance and operational efficiency. This paper focuses on the development of hybrid approaches to model engine combustion and temperature indices and on the synergistic effects of combining traditional analytical methods with modern machine learning techniques (such as artificial neural networks) to enhance the accuracy and robustness of such models. The main innovative contribution of this paper is the integration of analytical functions to improve the extrapolation capabilities of the data-driven models. In this work, it is demonstrated that the integrated models achieve superior predictive accuracy and generalization performance across dynamic engine operating conditions, with respect to purely neural network-based models. Furthermore, the analytical corrective functions force the output of the complete model to follow a physical trend and to assume consistent values also outside the domain of values assumed by the input features during the training procedure of the neural networks. This study highlights the potential of this integrative approach based on the implementation of the effects superposition principle. Such an approach also allows us to solve one of the intrinsic issues of data-driven modeling, without increasing the complexity of the training data's collection and pre-processing. [ABSTRACT FROM AUTHOR]

The integration of analytical functions into machine learning-based engine models represents a significant advancement in predictive performance and operational efficiency. This paper focuses on the development of hybrid approaches to model engine combustion and temperature indices and on the synergistic effects of combining traditional analytical methods with modern machine learning techniques (such as artificial neural networks) to enhance the accuracy and robustness of such models. The main innovative contribution of this paper is the integration of analytical functions to improve the extrapolation capabilities of the data-driven models. In this work, it is demonstrated that the integrated models achieve superior predictive accuracy and generalization performance across dynamic engine operating conditions, with respect to purely neural network-based models. Furthermore, the analytical corrective functions force the output of the complete model to follow a physical trend and to assume consistent values also outside the domain of values assumed by the input features during the training procedure of the neural networks. This study highlights the potential of this integrative approach based on the implementation of the effects superposition principle. Such an approach also allows us to solve one of the intrinsic issues of data-driven modeling, without increasing the complexity of the training data’s collection and pre-processing.

In the last few years, car manufacturers are moving toward electrified powertrains, like Battery Electric Vehicles (BEV) and Hybrid Electric Vehicles (HEV), to reduce the levels of CO2 in the atmosphere. A promising alternative to BEVs to reduce CO2 emissions, while maintaining an existing and well-developed technology, could be the use of hydrogen for internal combustion engines (ICEs). Particularly, the zero-carbon content of hydrogen allows for guaranteeing very clean combustion and near-zero carbon footprint. Within this context, the present study aims to deeply investigate the effects of pure hydrogen fueling of Spark Ignition (SI) engines, especially on specific fuel consumption and NOx emissions. Particularly, the benefits of pure hydrogen fueling were assessed in a production small Direct Injection (DI) SI engine. The engine is intended to operate in steady state conditions as an Auxiliary Power Unit (APU) for a series hybrid powertrain. The effects of pure hydrogen fueling were investigated at two engine speeds, namely 2000 and 3000 rpm. All tests were carried out at unthrottled conditions and lean mixture. A 1-D engine model was developed within a commercial software. Combustion, heat transfer, and NOx emissions embedded sub-models were tuned based on the experimental data, to accurately reproduce the engine behavior in a wide range of operating conditions. Both experimental and simulation results demonstrated that hydrogen fueling, together with lean mixture operation, allow for significantly improving engine thermal efficiency. The results of 1-D model simulations supported the energy management strategies of the hybrid powertrain in selecting the most suitable ICE operating condition. Particularly, the best engine operating conditions in terms of fuel consumption are achieved at 2500 rpm, with λ = 2.0, while the lowest NOx emissions are reached when λ is increased up to 2.4. [ABSTRACT FROM AUTHOR]

During the last years, the sales of vehicles equipped with Spark-Ignition (SI) engines have increased, to the detriment of those powered by Compression-Ignition (CI) engines. However, SI engines provide lower efficiency than CI engines, due to the low compression ratio and stoichiometric mixture operation. A way to increase the efficiency of SI engines is lean combustion, as it allows to increase the specific heats ratio (γ) and reduces pumping losses at part loads. Nevertheless, the operation with extremely lean mixtures deals with ignition issues, promoting Cycle-to-Cycle Variability (CCV) and increasing the probability of misfire. The prechamber ignition concept, also known as Turbulent Jet Ignition (TJI), is a promising solution for enabling the implementation of lean combustion, without its drawback in SI engines. Such a concept can be implemented according to two approaches: In active TJI, there is an additional fuel supply system inside the prechamber, while in passive TJI there isn't. Therefore, the main advantage of passive TJI is its simplicity, as the prechamber can be installed into a conventional spark plug body, with obvious benefits in terms of packaging and costs. In this work, the benefits of passive TJI on combustion and performance are assessed by simulation analyses. Particularly, a 1-D engine model was developed to simulate the TJI combustion and was validated versus experimental data. Afterward, a 0-D method was applied to assess the impact of the relative air-fuel ratio on CCV. The analysis was carried out in a high speed and load operating condition, namely 4500 rpm and 13 bar of IMEP, under both stoichiometric and lean mixture. Experimental and numerical results demonstrate the effectiveness of the passive TJI concept in promoting faster and more stable combustion also in lean-burn conditions. [ABSTRACT FROM AUTHOR]

The present paper proposes a novel methodology of turbocharging automotive engines to reach targeted performance. The actual method is tested and validated against simulation test results of two turbocharged diesel engines; engine I, three cylinders, 1.5 L, and engine II, six cylinders, 5.9 L. The present procedure is subdivided into four key parts; namely, database construction, selection procedure, turbocharger preliminary design, and engine modeling. Based on geometric dimensions and aerodynamic parameters provided by the preliminary design procedure, 3D geometries of the turbine and compressor are generated for each studied engine. After integrating previous data into a constructed turbocharger database, two turbochargers are selected for the engine I, while only one turbocharger for the engine II. The findings show that, at the engine speed of 4000 rpm, engine I matched with the adequate turbocharger reached a target power about 2.7%, compared to the original turbocharger equipping engine I. Furthermore, engine II reached a rated power of 299.3 kW at 2500 rpm which is slightly under the original one by 2.64 kW. The superimposition of the engine operating area on compressor and turbine maps provided satisfactory results in terms of turbocharger-engine output performance, fuel consumption, secure functioning and engine thermal strength. Finally, the main advantage of the developed methodology consists of its ability to be applied at both earlier and last stages of the engine turbocharging process or to find new adequate turbochargers to replace the original one for economic, mechanical or for safety reasons. [ABSTRACT FROM AUTHOR]

In the paper, the effect of water injection and different air/fuel ratio (AFR) on the gasoline engine performance and emissions was investigated theoretically. A four cylinder SI engine model was built using GT-Power professional software. The gasoline fuel was injected directly to the cylinder and the water was injected into the intake manifold with different mass flow rate. The calculated engine outputs were: cylinder pressure and temperature, brake torque, brake power, mean effective pressure, thermal efficiency, carbon monoxide, hydrocarbon, and nitrogen oxide emissions. The results of simulations shows that the increased water mass flow rate resulted in improved engine performance and decreased emissions compared to neat gasoline fuel. However, we found that the excessive increase of the water amount inside the cylinder resulted in deteriorated engine performance and, consequently, poorly combustion efficiency.

Vehicle control problems have been historically using simple control structures with lookup tables that were designed to use vehicle steady-state characterization to calibrate and are suited better for older systems with lower complexity/dimension. However, the increasing complexity of the vehicle systems and stringent regulations requires optimal control of the dynamic vehicle systems. In this brief, we present an efficient online identification methodology with a composite local model structure that will be critical to the new control paradigm. We first introduce the concept of evolving spatial–temporal filters (STFs) that dynamically transform an incoming input–output data stream into a nonlinear combination of local models. The local models are weighed by an array of weights corresponding to the compatibility of the input–output data to a set of ellipsoidal clusters that partition the input–output space. The filters exploit ellipsoidal-shape evolving clusters as function bases and a distance metric defined as a combination of Mahalanobis distance and scaled local model prediction error. Parameters of the filters and local models can be updated simultaneously online, making adaptive optimization a possibility for vehicle systems. Evolving clustering and recursive least square techniques are exploited to simultaneously update the cluster parameters and local linear models. We apply the developed algorithm to the modeling of a turbocharged internal combustion engine that is a highly nonlinear and complex system. Promising performance is demonstrated both in a high-fidelity simulator and on an experimental vehicle. [ABSTRACT FROM AUTHOR]

: Cylinder deactivation (CDA) is an effective technique to improve fuel economy in spark ignition (SI) engines. This technique enhances volumetric efficiency and reduces throttling loss. However, practical implementation is restricted due to torque fluctuations between individual cylinders that cause noise, vibration, and harshness (NVH) issues. To ease torque deviation of the CDA, we propose an in-cylinder pressure based 48V mild-hybrid starter-generator (MHSG) control strategy. The target engine realizes CDA with a specialized engine configuration of separated intake manifolds to independently control the airflow into the cylinders. To handle the complexity of the combined CDA and mild-hybrid system, GT-POWER simulation environment was integrated with a SI turbulent combustion model and 48V MHSG model with actual part specifications. The combustion model is essential for in-cylinder pressure-based control; thus, it is calibrated with actual engine experimental data. The modeling results demonstrate the precise accuracy of the engine cylinder pressures and of quantities such as MAF, MAP, BMEP, and IMEP. The proposed control algorithm also showed remarkable control performance, achieved by instantaneous torque calculation and dynamic compensation, with a 99% maximum reduction rate of engine torque deviation under target CDA operations. [ABSTRACT FROM AUTHOR]

The goal of this paper is to highlight the advantage fulfilled by a novel engine mechanism, the concept of which is based on an oscillating arm relative to the classical engine mechanism. Further, the results of this paper demonstrate the benefits of a novel type of mechanism and the major advantages in terms of functioning parameters of an engine. Their performances highly depend on the joint positions of the oscillating arm. The increases in the functional performances rate of success (i.e., piston stroke, volume of the combustion chamber or compression ratio) enable a superior engine power parameter (higher power, torque) and bring some additional improvement on the eco parameters of the engine related to consumption, emission, etc. [ABSTRACT FROM AUTHOR]

Recent trends among automotive manufacturers towards downsized, boosted engines make it imperative to understand specific fuel chemistry interactions encountered in this new operating regime. At these elevated pressure conditions a phenomenon called pre-spark heat release has recently been discovered, and is characterized by kinetically controlled heat release before spark, with resultant changes in end-gas thermodynamic state and composition. These reactions typically occur in the end-gas during normal operation, but are obscured by the deflagration heat release and therefore cannot be easily studied. A 2-zone spark-ignition engine model was utilized to determine whether chemical kinetic mechanisms are able to predict this phenomenon, and whether they accurately capture end-gas thermodynamic history. Experimental engine data at a range of boosted operating conditions demonstrating pre-spark heat release were compared with simulations using mechanisms representing the latest developments in gasoline kinetic modeling. The results demonstrated significant discrepancies between mechanisms, and between experimental and simulated results in terms of low-temperature heat release magnitude, end-gas thermodynamic state, and autoignition propensity. The results highlight shortcomings in low-temperature reaction pathways, and indicate the necessity of simultaneously matching first-stage ignition delay and heat release magnitude, in addition to second-stage ignition delay, in order to accurately predict end-gas thermodynamics and autoignition. [ABSTRACT FROM AUTHOR]

Aeroengine modeling plays an important role in the development of advanced controls and other intelligent functions such as engine health management etc. This is particularly true for those on-board applications, where identification-based modeling approaches are often utilized. While linear modeling methodologies have been extensively introduced in the literature, identification-based nonlinear modeling should be investigated, due to the inherent nonlinear nature of an aeroengine's dynamical properties. Three approaches, i.e., back propagation (BP) neural networks (BPNN), improved BP neural networks (IBPNN), and a piecewise nonlinear autoregression with extra inputs (PNARX) model, are presented. A comparative study is given for systematic development and illustration of nonlinear modeling techniques. Some practical considerations are also given for guidance of implementing the identification-based nonlinear modeling methodologies. [ABSTRACT FROM AUTHOR]

With civil aviation growing at around 4.7% per annum, the environmental footprint of aviation is increasing. Moreover, the use of kerosene as a fuel accelerates the depletion of non-renewable fossil fuels and increases global warming. Hence, the aviation industry has to come up with new technologies to reduce its environmental impact and make aviation more sustainable. An electrically assisted propulsion system can combine the benefits of an electrical power source with a conventional turbofan engine. However, the additional electrical system increases the weight of the aircraft and complexity of the power management system. The objective of this research is to analyze the effect of an assistive electrical system on the performance of a turbofan engine for an A320 class aircraft on a short-range mission. The developed simulation model consists of an aircraft performance model combined with a propulsion model. The power management strategy is integrated within the simulation model. With the proposed propulsion system and power management strategy, the electrically assisted propulsion system would be able to reduce fuel burn, total energy consumption, and emissions for short-range missions of around 1000 km. [ABSTRACT FROM AUTHOR]

In the paper, the effect of water injection and different air/fuel ratio on the gasoline engine performance and emissions was investigated theoretically. A four cylinder SI engine model was built using GT-Power professional software. The gasoline fuel was injected directly to the cylinder and water was injected into the intake manifold with different mass flow rate. The calculated engine parameters were: cylinder pressure and temperature, brake torque, brake power, mean effective pressure, thermal efficiency, carbon monoxide, hydrocarbon, and nitrogen oxide emissions. The results of simulations show that the increased water mass flow rate resulted in an improved engine performance and decreased emissions compared to neat gasoline fuel. However, we found that the excessive increase of the water amount inside the cylinder resulted in a deteriorated engine performance and, consequently, poor combustion efficiency. [ABSTRACT FROM AUTHOR]

This paper presents the simulation of the multi-cylinder 4-stroke cycle spark-ignition engine using a commercial simulation tool, AVL BOOST and Fire. Various models were examined to select the appropriate models that would best serve to analyze the main components of the exhaust systems: the plenum chamber, the muffler and the exhaust manifold branch junction. For the plenum chamber and the muffler, the whole duct model was tested. In order to analyze the exhaust manifold branch junction, a complicated model which reflects the actual shape and involves pressure drops, velocity magnitude and sound pressure was compared to a simplified one. However, the results from 1D and 3D model calculations compared with Honda K20B Engine experimental data also show that both models are applicable with satisfying accuracy for exhaust manifold branch junction. The simplified 1D model is recommended in regard to convenience in modeling and efficiency in calculation. [ABSTRACT FROM AUTHOR]

The goal of this paper is to highlight the advantage fulfilled by a novel engine mechanism, the concept of which is based on an oscillating arm relative to the classical engine mechanism. Further, the results of this paper demonstrate the benefits of a novel type of mechanism and the major advantages in terms of functioning parameters of an engine. Their performances highly depend on the joint positions of the oscillating arm. The increases in the functional performances rate of success (i.e., piston stroke, volume of the combustion chamber or compression ratio) enable a superior engine power parameter (higher power, torque) and bring some additional improvement on the eco parameters of the engine related to consumption, emission, etc.

An online extreme learning machine (ELM) based modeling and optimization approach for point-by-point engine calibration is proposed to improve the efficiency of conventional model-based calibration approach. Instead of building hundreds of local engine models for every engine operating point, only one ELM model is necessary for the whole process. This ELM model is firstly constructed for a starting operating point, and calibration of this starting point is conducted by determining the optimal parameters of the model. This ELM model is then re-used as a base model for a nearby target operating point, and optimization is performed on the model to search for its best parameters. With a design of experiment strategy on the best parameters obtained, new measurements from the target operating point can be collected and used to update the model. By repeating the optimization and model update procedures, the optimal parameters for the target point can be found after several iterations. By using the model of this target point as the base model for another nearby operating point and repeating the same process again, calibration for all the operating points can be done online efficiently. The contribution of the proposed method is to save the number of experiments in the calibration process. To verify the effectiveness of the proposed approach, experiments on a commercial engine simulation software have been conducted. Three variants of online ELM are utilized in the model update process for comparison. The results show that engine calibration can be carried out with much fewer measurements and time using the proposed approach, and the initial training free online ELM is the most efficient online modeling method for this application. [ABSTRACT FROM AUTHOR]

• 1-D engine model with predictive TJI combustion is developed and validated. • Benefits of passive TJI on combustion and efficiency are assessed. • The effect of mixture leaning is assessed through model simulations. • The impact of prechamber geometry on combustion and efficiency is assessed. The increasing concern about Greenhouse Gas (GHG) emissions led the European Union to introduce increasingly stringent limits to the CO 2 from road vehicles, with an impact on the sales of passenger cars. Vehicles equipped with Spark-Ignition (SI) engines became more numerous than those equipped with Compression-Ignition (CI) engines, due to the expensive aftertreatment system needed to comply with the restrictive European emission standards. However, SI engines provide lower efficiency than CI engines, due to the low compression ratio and the operation with a stoichiometric air–fuel ratio. Lean combustion can be a solution to increase SI engines' efficiency. Nevertheless, extremely lean mixtures lead to an increase in Cycle-to-Cycle Variability (CCV). The prechamber ignition concept, also known as Turbulent Jet Ignition (TJI), is an attractive solution for lean combustion, without its drawbacks. There are two ways to implement this concept: active TJI and passive TJI. In active TJI, there is an additional fuel supply system inside the prechamber, while passive TJI operates without additional injection. Therefore, passive TJI offers advantages in terms of simplicity, easy packaging, and low cost. In this work, the effects of passive TJI on combustion and performance are investigated by simulation analyses. Particularly, a 1-D engine model was developed to simulate the TJI combustion and validated against the experimental data. Furthermore, model simulations were carried out to assess how the prechamber geometry, in terms of A/V ratio, affects the jet performance, main chamber combustion, and fuel consumption, for different λ values. The analysis was conducted in a medium-to-high speed and load operating condition, namely 4500 rpm and 13 bar of Indicated Mean Effective Pressure (IMEP), under both stoichiometric and lean mixture. Simulation results demonstrated that the best jet performance and the highest engine efficiency are obtained for medium-to-high values of prechamber volume and large diameters, both in stoichiometric and lean-burn conditions, defining a common optimum prechamber design regardless of the λ level. [ABSTRACT FROM AUTHOR]

A numerically robust approach to steady-state calibration of nonlinear dynamic models is presented. The approach is based on explicit formulation of the constraints on validity of internal model signals by set of inequalities. The constrained optimization with feasible iterates guarantees that the model will never be evaluated with invalid internal signals. This overcomes numerical difficulties often encountered when dealing with highly nonlinear models. Because the approach uses a large number of slack variables, distributed least squares algorithm is proposed. The robustness of this approach is demonstrated on a steady-state calibration of turbocharged diesel engine model starting from grossly inaccurate initial estimates. [ABSTRACT FROM AUTHOR]

Numerical prediction of cycle-to-cycle variability in spark ignition engines is extremely challenging for two key reasons: (1) high-fidelity methods such as large eddy simulation are required to accurately capture the in-cylinder turbulent flow field and (2) cycle-to-cycle variability is experienced over long time scales, and hence, the simulations need to be performed for hundreds of consecutive cycles. In this study, a methodology is proposed to dissociate this long time-scale problem into several shorter time-scale problems, which can considerably reduce the computational time without sacrificing the fidelity of the simulations. The strategy is to perform multiple parallel simulations, each of which encompasses two to three cycles, by effectively perturbing the simulation parameters such as the initial and boundary conditions. The proposed methodology is validated for the prediction of cycle-to-cycle variability due to gas exchange in a motored transparent combustion chamber engine by comparing with particle image velocimetry measurements. It is shown that by perturbing the initial velocity field effectively based on the intensity of the in-cylinder turbulence, the mean and variance of the in-cylinder flow field are captured reasonably well. Adding perturbations in the initial pressure field and the boundary pressure improves the predictions. It is shown that this new approach is able to give accurate predictions of the flow field statistics in considerably less time than that required for the conventional approach of simulating consecutive engine cycles. [ABSTRACT FROM AUTHOR]

• Mean, light knock, and heavy knock engine cycle trajectories were characterized. • Six reaction rates were optimized in a propane mechanism with a genetic algorithm. • Over 50% reduction in absolute error was achieved in ignition delay predictions. • Optimized mechanism enables engine combustion chamber and geometry design. Propane has demonstrated significant potential for reductions in greenhouse gas and pollutant emissions in medium- and heavy-duty engine applications, but further improvements require accurate, compact, and scalable chemical kinetic mechanisms to design the next generation of propane fueled engines, particularly at the boosted operating conditions necessary to meet the power density demand of medium- and heavy-duty applications. In this work, six key chemical reactions were identified in a reduced mechanism with 70 species and 352 reactions through a sensitivity analysis performed at conditions typical of thermodynamic trajectories observed in a high compression ratio, long stroke engine operated on propane from throttled to boosted operating conditions. While the original mechanism was validated against rapid compression machine (RCM) data, it was found to overpredict experimental autoignition tendencies in 2-zone, 0-D SI engine simulations performed in Chemkin Pro. Subsequently, a genetic algorithm approach was used to optimize the six reaction rate parameters within established uncertainty bounds by performing RCM simulations and comparing to two independent sets of literature ignition delay times for propane, thus generating two new kinetic mechanisms. The first optimization achieved a mean absolute percent error (MPE) reduction in 2nd stage ignition delay of 61.4% in seven generations, while the second optimization utilized a newer experimental RCM dataset, and achieved MPE reduction of 56.7% in seven generations, and further marginal improvement to 57.8% reduction in 34 generations. The two mechanisms were then evaluated again in the 2-zone 0-D SI engine model in Chemkin Pro comparing typical mean and knocking cycle trajectories, and it was found that the second optimized mechanism provided better prediction of knock onset at the representative conditions evaluated in this work, particularly for higher load operating conditions. [ABSTRACT FROM AUTHOR]

Volumetric efficiency and air charge estimation is one of the most demanding tasks in control of today's internal combustion engines. Specifically, using three-way catalytic converter involves strict control of the air/fuel ratio around the stoichiometric point and hence requires an accurate model for air charge estimation. However, high degrees of complexity and nonlinearity of the gas flow in the internal combustion engine make air charge estimation a challenging task. This is more obvious in engines with variable valve timing systems in which gas flow is more complex and depends on more functional variables. This results in models that are either quite empirical (such as look-up tables), not having interpretability and extrapolation capability, or physically based models which are not appropriate for onboard applications. Solving these problems, a novel semi-empirical model was proposed in this work which only needed engine speed, load, and valves timings for volumetric efficiency prediction. The accuracy and generalizability of the model is shown by its test on numerical and experimental data from three distinct engines. Normalized test errors are 0.0316, 0.0152 and 0.24 for the three engines, respectively. Also the performance and complexity of the model were compared with neural networks as typical black box models. While the complexity of the model is less than half of the complexity of neural networks, and its computational cost is approximately 0.12 of that of neural networks and its prediction capability in the considered case studies is usually more. These results show the superiority of the proposed model over conventional black box models such as neural networks in terms of accuracy, generalizability and computational cost. [ABSTRACT FROM AUTHOR]

Carbon dioxide emission reduction is the most important challenge concerning on the road transportation. Stringent quantitative commitments have been set, both for automotive and for light and heavy duty vehicles. Future engine (and vehicle) technologies will consider a portfolio of new components, engine layouts, control strategies, integrated functions which will match also new comfort standards and fun to drive options, and complying fuel consumption savings. Engine thermal management is an area of intervention aimed at reducing the warm up time: in most part of the homologation cycle, both in passenger cars and in light duty vehicles, engines do not reach a stabilized thermal state. This produces a significant pollutants and fuel consumption increase. Engine thermal management can match also more effective cabin heating requirements, improving comfort, which is an important market advantage. Basic idea of engine thermal management is to reduce or make nil the cooling fluid circulation inside the engine. In this paper, the oil warm up is considered as the main focus: a faster temperature grow up decreases oil viscosity and improves mechanical efficiency and organic efficiency, during engine cold state. An experimental activity has been conducted on a dynamic test bench, testing the cooling fluid and oil dynamics of a widely known commercial engine, in fixed engine points and during the New European Driving Cycle (NEDC). A comprehensive mathematical model reproducing both the integrated circuits has been developed and validated: this required an overall engine and vehicle modeling whose input data was determined by the vehicle mission profile. By means of such model, three new technologies have been proposed and verified in terms of warm up time. They are: (a) by pass of the oil cooling, (b) oil heating using exhaust gases, and (c) a partial reduction of the oil quantity inside the sump. All of them were applied during transients, which revealed they are able to reduce warm up time of about 65–70% with a significant benefit in terms of CO 2 reduction. [ABSTRACT FROM AUTHOR]

It is feared that the increasing population of vehicles in the world and the depletion of fossil-based fuel reserves could render transportation and other activities that rely on fossil fuels unsustainable in the long term. Concerns over environmental pollution issues, the high cost of fossil-based fuels and the increasing demand for fossil fuels has led to the search for environmentally friendly, cheaper and efficient fuels. In the search for these alternatives, liquefied petroleum gas (LPG) has been identified as one of the viable alternatives that could be used in place of gasoline in spark-ignition engines. The objective of the study was to present the modeling and multi-objective optimization of brake mean effective pressure and hydrocarbon emissions for a spark-ignition engine retrofitted to run on LPG. The use of a one-dimensional (1D) GT-Power™ model, together with Group Method of Data Handling (GMDH) neural networks, has been presented. The multi-objective optimization was implemented in MATLAB® using the non-dominated sorting genetic algorithm (NSGA-II). The modeling process generally achieved low mean squared errors (0.0000032 in the case of the hydrocarbon emissions model) for the models developed and was attributed to the collection of a larger training sample data using the 1D engine model. The multi-objective optimization and subsequent decisions for optimal performance have also been presented. [ABSTRACT FROM AUTHOR]

In order to face with the increasing EU restrictions on CO2 emissions from light-duty vehicles, concepts such as the engines downsizing, stop/start systems as well as more costly full hybrid solutions and Waste Heat Recovery (WHR) technologies have been proposed in the last years by OEMs. WHR technologies include Thermo-Electric Generator (TEG), Organic Rankine Cycle (ORC) and Electric Turbo-Compound (ETC) that have been practically implemented on few heavy-duty applications but have not been proved yet as effective and affordable solutions for passenger cars. The paper deals with the analysis of opportunities and challenges of TEG and ETC technologies for a compact car, powered by a turbocharged SI engine. Specifically, the benefits achievable by TEG and ETC have been investigated by simulation analyses carried out by a dynamic engine-vehicle model, validated against steady-state and transient experimental data. The in-cylinder processes and friction losses of the engine are modeled by a black-box scalable parametric approach while grey-box dynamic models are applied for intake/exhaust manifolds and turbocharger. The TEG model is based on existing and commercial thermoelectric materials, specifically Bi2Te3. The simulations have been carried out considering standard driving cycles (i.e. NEDC, WLTC) and the results evidence that significant improvement of fuel economy and CO2 reduction can be achieved by suitable management and configuration of the WHR systems, depending on engine speed and load and auxiliaries demand. [ABSTRACT FROM AUTHOR]

In order to face with the increasing EU restrictions on CO2 emissions from light-duty vehicles, concepts such as the engines downsizing, stop/start systems as well as more costly full hybrid solutions and Waste Heat Recovery (WHR) technologies have been proposed in the last years by OEMs. WHR technologies include Thermo-Electric Generator (TEG), Organic Rankine Cycle (ORC) and Electric Turbo-Compound (ETC) that have been practically implemented on few heavy-duty applications but have not been proved yet as effective and affordable solutions for passenger cars. The paper deals with the analysis of opportunities and challenges of TEG and ETC technologies for a compact car, powered by a turbocharged SI engine. Specifically, the benefits achievable by TEG and ETC have been investigated by simulation analyses carried out by a dynamic engine-vehicle model, validated against steady-state and transient experimental data. The in-cylinder processes and friction losses of the engine are modeled by a black-box scalable parametric approach while grey-box dynamic models are applied for intake/exhaust manifolds and turbocharger. The TEG model is based on existing and commercial thermoelectric materials, specifically Bi2Te3. The simulations have been carried out considering standard driving cycles (i.e. NEDC, WLTC) and the results evidence that significant improvement of fuel economy and CO 2 reduction can be achieved by suitable management and configuration of the WHR systems, depending on engine speed and load and auxiliaries demand. [ABSTRACT FROM AUTHOR]

• 1-D engine model with predictive TJI combustion is developed and exp. validated. • TJI engine operation is exploited by 1-D model. • A comprehensive model of series-HEV is designed by 1-D simulation results. • The benefits of TJI concept on overall efficiency and battery sizing are assessed. In the last decades, the concern about vehicles' environmental impact has progressively increased. Therefore, car manufacturers are moving towards vehicles with low emissions like Hybrid Electric Vehicles (HEV), that in the series architecture utilize an internal combustion engine (ICE) to power an electric generator for battery recharging thus extending the driving range of a Battery Electric Vehicle (BEV). For such applications, small four-stroke spark-ignition (SI) engines are a suitable solution, as they are proven low-cost and reliable systems and allow to increase vehicle driving range with reduced environmental impact. In series HEV the ICE is decoupled from the drive wheels and operates at a fixed working point regardless to the instantaneous power demand, thus promoting the lean mixture operation to achieve high efficiency and low CO 2 emissions. In this work, the performance, in terms of driving range, energy consumption and CO 2 emissions, of a series HEV are analyzed in experimental and simulation environment. Particularly, the Auxiliary Power Unit (APU) is a small ICE equipped with a passive Turbulent Jet Ignition (TJI) that allows to guarantee stable combustion also under lean mixture operation, with an increase of engine efficiency and a reduction of CO 2 emissions. The effects of lean air–fuel ratio on combustion process and engine performance are investigated by a one-dimension (1-D) model and validated vs experimental measurements at the test bench on a prototype single cylinder engine equipped with passive prechamber. The benefits of the high efficiency ICE operation on the series HEV performance, energy consumption and CO 2 emissions vs standard and arbitrary driving cycles are analyzed in simulation environment, making use of the simulation results gathered by the 1-D engine model. Simulation analyses are carried out to evaluate the best compromise between battery pack downsizing and energy consumption for a fixed target of vehicle drive range. Simulation results show that TJI concept allows to achieve an APU efficiency up to 32 %, by operating with λ equal to 1.26. Additionally, the analysis on battery pack downsizing evidences that switching from 150 % to 50 % of the reference battery pack capacity results in an increase of the overall energy consumption up to 22 %. [ABSTRACT FROM AUTHOR]

Existing methods of detecting the combustion phase of a diesel engine are mainly based upon high-cost in-cylinder pressure sensors. This paper presents a method of estimating the location of 75% of the mass fraction burned by using the traditional knock sensor signal. It is observed through experimental data that the knock signal has a close correlation to the location of 75% of the mass fraction burned. In this study, the knock integration is used to represent the knock intensity, while the piecewise knock integration (integration over a crank angle of 1°) is used to detect the location of 75% of the mass fraction burned. The proposed estimation method was validated using the experimental data and provided an accurate estimation of the location of 75% of the mass fraction burned. In addition, the feasibility of using the knock sensor signal to detect the biodiesel content is demonstrated using experimental data. [ABSTRACT FROM AUTHOR]

With the advancements in engine design, the air path systems have become so complex that advanced optimization and control design methods are nowadays needed to fully exploit the potential of technologies such as flexible valve actuation. While the automotive industry is employing model-based methodologies for the engine air path system control design process, control-oriented engine air path models are currently limited in their accuracy and predictive ability and rely significantly on calibration. As the engine system complexity increases, there is considerable interest for accurate yet computationally efficient control-oriented engine system models that are able to predict the cylinder charge composition and the thermodynamic conditions with limited calibration effort. This article presents a novel model-order reduction approach for lumped-parameter modeling of pressure wave propagation dynamics in fluids, with application to full-engine simulation. Instead of approximating the geometry of the system to a simple control volume (as typically done in control-oriented engine air path models), this modeling methodology reduces the governing equations for compressible fluid flows and allows for systematically handling both complex geometries, such as sudden area changes and pipe elbows, as well as complex boundary conditions. The model-order reduction procedure projects the partial differential equations governing the air and gas flows onto a set of eigenfunctions, resulting in a set of ordinary differential equations that can be easily implemented in forward-looking simulation software. The modeling approach presented in this article is applied to characterize the wave propagation dynamics in the air path system of a single-cylinder engine, specifically predicting the crank-angle-resolved intake and outlet pressure, as well as the cylinder air charge and volumetric efficiency. The results are benchmarked against both experimental data and simulation results from a well-established one-dimensional gas dynamic model. [ABSTRACT FROM PUBLISHER]

Mathematical models of vehicle subsystems have main contribution in control and simulation of active safety systems. Among the others, internal combustion engine is a subsystem having high degrees of complexity and nonlinearity, and there is no any accurate and simple model yet being able to predict the engine behavior over the entire range of its variables. In this paper, a semiempirical model is proposed for spark ignition engines that predicts steady torque in terms of throttle position and engine speed. In model development phase, both the physics of the problem and analysis of the measured data are used. Required data for model development are obtained from a validated comprehensive one-dimensional engine model, and the prediction capability of the proposed model is investigated using experimental data. The performance of the model is also compared with a conventional neural networks model. Results show the superiority of the proposed model in comparison with black box models in terms of accuracy, computational cost, and interpretability. This model can be used for determining the required throttle position based on the acceleration demand in longitudinal vehicle dynamics control tasks. [ABSTRACT FROM AUTHOR]

In order to comply with increasing consumer and regulatory demand for improved fuel economy and lower emissions, the engines and engine aftertreatment systems must be improved continuously. Since the complete system is very complex, models are useful in order to be able to cost efficiently develop new control strategies and selection of hardware. In this article, a model library for dynamic engine modeling in Dymola is presented. The library consists of models of the standard engine components such as manifolds, pipe, turbines, compressors, valves and mechanics. The combustion model is a mean value model and the focus has been on air path management and exhaust modeling with real-time-like simulation times, useful for engine optimization and for evaluation of control strategies. Engine simulation results from a 13-L Volvo engine demonstrates that the models capture the dynamics and have sufficient accuracy to be useful in engine optimization. [ABSTRACT FROM PUBLISHER]

In parallel turbocharged V-engines, with two separate air paths connected before the throttle, an oscillation in the flow can occur. If the compressor operates close to the surge line, typically during low speed and high load, and a disturbance alters the mass flow balance, the compressors can begin to alternately go into surge. This phenomenon is called co-surge and is unwanted due to high noise and risk of turbocharger destruction. Co-surge is measured on a test vehicle in a chassis dynamometer and the system analyzed and modeled using a mean value engine model. The investigation shows that alternating compressor speeds have an important role in the prolonged oscillation. A reconstruction of the negative flow from measurements is made and compared to simulation results, showing similar amplitudes, and supports the model validation. A new co-surge detection algorithm is presented, suitable for a pair of sensors measuring either mass flow, boost pressure or turbo speed in the two air paths. Furthermore, a new controller is proposed that uses a model based feedforward for the throttle, together with wastegate actuation to force the compressor speeds together and improve balance at the recovery point. This has been shown to be sufficient for moderate to high pressure ratios over the throttle, and only for zero or very low pressure drop the use of bypass valves is necessary. The advantage of not opening the bypass valves is a smaller drop in boost pressure which also reduces the torque disturbance. The performance of the controller is evaluated both in simulation and in the test vehicle. [ABSTRACT FROM AUTHOR]

The challenge of continuously improving engine fuel economy and emissions has pushed the automotive industry to adopt more efficient procedures for modeling, system simulation and model-based control. Such procedures require accurate and computationally efficient engine system simulation models that are able to predict the cylinder charge composition and the thermodynamic conditions. To this extent, reduced-order models for unsteady compressible flow systems, namely models derived from the fundamental conservation laws without over-simplifying the topology of the engine air path, are receiving considerable interest for performance simulation and control design.This paper presents a comparative study on modeling approaches for the prediction of one-dimensional unsteady compressible flow in the internal combustion engine air path system. Specifically, the paper compares a well-known second-order, shock-capturing finite-difference scheme with two novel solution methods, namely a finite-volume method and a model-order reduction method. The study aims at evaluating the ability of each method to trade-off the prediction accuracy and robustness with the computation time, in light of potential applications to real-time simulation. This is achieved by increasing the discretization length of each method, and evaluating the accuracy of the predicted response in the time and frequency domain.The characterization of the intake and exhaust flows in the manifolds of a single-cylinder engine is considered as a case study. The results are compared for the three solution methods at different discretization lengths to evaluate the ability of each model to retain accuracy and robustness. The computation times of the different methods are also evaluated.The results presented in this paper establish a clear trade-off between accuracy, stability and computation time for each solution method. This allows one to formulate considerations on the advantages and disadvantages of each method in light of potential applications to engine performance prediction, optimization and control design. [ABSTRACT FROM PUBLISHER]

Abstract: This study aims to determine optimal biodiesel ratio that can achieve the goals of fewer emissions, reasonable fuel economy and wide engine operating range. Different advanced machine learning techniques, namely ELM (extreme learning machine), LS-SVM (least-squares support vector machine) and RBFNN (radial-basis function neural network), are used to create engine models based on experimental data. Logarithmic transformation of dependent variables is used to alleviate the problems of data scarcity and data exponentiality simultaneously. Based on the engine models, two optimization methods, namely SA (simulated annealing) and PSO (particle swarm optimization), are employed and a flexible objective function is designed to determine the optimal biodiesel ratio subject to various user-defined constraints. A case study is presented to verify the modeling and optimization framework. Moreover, two comparisons are conducted, where one is among the modeling techniques and the other is among the optimization techniques. Experimental results show that, in terms of the model accuracy and training time, ELM with the logarithmic transformation is better than LS-SVM and RBFNN with/without the logarithmic transformation. The results also show that PSO outperforms SA in terms of fitness and standard deviation, with an acceptable computational time. [Copyright &y& Elsevier]

Abstract: Homogeneous charge compression ignition (HCCI) presents new challenges in combustion phasing control due to the lack of direct ignition trigger. In addition, recompression HCCI possesses cycle-to-cycle coupling through trapping a large portion of the exhaust gas in the cylinder. This paper examines the change in the cycle-to-cycle coupling around different operating points in recompression HCCI and show that there are three qualitative types of temperature dynamics: smooth decaying when combustion phasing is early, oscillatory when phasing is late, and strongly converging with moderate combustion phasing. As a result, a three region switching linear model that combines three linearized models can capture the qualitative change in system characteristics. Based on this model, a three region switching controller is designed and shown in experiments that it can track a wide range of desired combustion phasing and improve the variance of combustion phasing over open-loop operations. In the last part of this paper, two semi-definite programming (SDP) formulations are presented to prove the stability of the switching control/model framework. [Copyright &y& Elsevier]

A numerical engine mapping methodology is proposed for the engine performance and fuel consumption map generation. An integrated model is developed by coupling a single cylinder GT-Power® engine model with a MATLAB/ Simulink® based boost system model to simulate a turbocharged diesel engine over the entire engine operating speed and load ranges within reasonable computational constraints. A single cylinder engine model with the built-in multi-zone combustion modeling option in GT-Power® is configured as a predictive engine model. The cycle averaged simulation result from the engine model is used as the boundary conditions of the boost system including intake and exhaust manifolds and a turbocharger. The boost system model developed in MATLAB/Simulink® platform calculates the intake and exhaust conditions which are fed back to the engine model. The integrated system model predicts the performance and fuel consumption of a turbocharged diesel engine with better predictive capability than mean value engine models. Its computational time is fast enough to simulate the engine over the entire engine operation range compared to multi-cylinder engine models. [ABSTRACT FROM AUTHOR]

Abstract: In order to reduce emissions in the automotive industry, there is a trend to decrease the size of the engines and add turbocharging technologies to increase the engine efficiency while keeping performances. In this context, numerous studies have focused on the improvement of turbocharger control strategies. The strategy proposed in this paper is based on constrained motion planning and feedback linearization applied to a simplified model of the system. It takes advantage of the whole system bandwidth and requires a limited calibration effort. Thanks to its structure, it offers an interesting potential for generalization to more complex turbocharging systems. Extensive experimental tests are reported for a four-cylinder gasoline engine. [Copyright &y& Elsevier]