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Beside the automotive industry, where 2-cylinder inline engines are catching attention again, twin-cylinder configurations are quite usual in the small engine world. From stationary engines and range-extender use to small motorcycles up to big cruisers and K-Cars this engine architecture is used in many types of applications. Because of very good overall packaging, performance characteristics and not least the possibility of parts-commonality with 4-cylinder engines nearly every motorcycle manufacturer provides an inline twin in its model range. Especially for motorcycle applications where generally the engine is a rigid member of the frame and vibrations can be transferred directly to the rider an appropriate balancing system is required.

Different hybrid powertrains for a European mid-size passenger car were evaluated in this paper through numerical simulation. Different degrees of hybridizations, from micro to mild hybrids, and different architectures and power sources management strategies were taken into account, in order to obtain a preliminary assessment of the potentialities of different hybrid systems for the European passenger car market. Both diesel and gasoline internal combustion engines were considered: a 1.6 dm₃ Common Rail turbocharged diesel, and a 1.4 dm₃ spark ignition turbocharged engine, equipped with an innovative Variable Valve Actuation system. Diesel hybrid powertrains, although being subject to NOx emissions constraints that could jeopardize their benefits, offered substantial advantages in comparison with gasoline hybrid powertrains. Potentialities for fuel consumption reductions up to 25% over the NEDC were highlighted, approaching the 2020 EU 95 g/km CO₂ target.

Engine simulation can be performed using model approaches of different depths in capturing physical effects. The present paper presents a comprehensive comparison study on seven different engine models. The models range from transient 1D cycle resolved approaches to steady-state non-dimensional maps. The models are discussed in the light of key features, amount and kind of required input data, model calibration effort and predictability and application areas. The computational performance of the different models and their capabilities to capture different transient effects is investigated together with a vehicle model under real-life driving conditions. In the trade-off field of model predictability and computational performance an innovative approach on crank-angle resolved cylinder modeling turned out to be most beneficial.

In spark-ignition engines, fluctuations of the in-cylinder pressure trace and the apparent rate of heat release are usually observed from one cycle to another. These Cycle-to-Cycle Variations (CCV) are affected by the early flame development and the subsequent flame front propagation. The CCV are responsible for engine performance (e.g. fuel consumption) and the knock behavior. The occurrence of the phenomena is unpredictable and the stochastic nature offers challenges in the optimization of engine control strategies. In the present work, CCV are analyzed in terms of their impact on the engine knock behavior and the related efficiency. Target is to estimate the possible fuel consumption savings in steady-state operation and in the drivecycle, when CCV are reduced. Since CCV are immanent on real engines, such a study can only be done by means of simulation.

Predicting the fuel economy capability of hybrid electric vehicle (HEV) powertrains by solving the related optimal control problem has been available for a few decades. Dynamic programming (DP) is one of the most popular techniques implemented to this end. Current research aims at integrating further powertrain modeling criteria that improve the fidelity level of the optimal HEV powertrain control behavior predicted by DP, thus corroborating the reliability of the fuel economy assessment. Dedicated methodologies need further development to avoid the curse of dimensionality which is typically associated to DP when increasing the number of control and state variables considered. This paper aims at considerably reducing the overall computational effort required by DP for HEVs by removing the state term associated to the battery state-of-charge (SOC).

One of the first steps in powertrain design is to assess its best performance and consumption in a virtual phase. Regarding hybrid electric vehicles (HEVs), it is important to define the best mode profile through a cycle in order to maximize fuel economy. To assist in that task, several off-line optimization algorithms were developed, with Dynamic Programming (DP) being the most common one. The DP algorithm generates the control actions that will result in the most optimal fuel economy of the powertrain for a known driving cycle. Although this method results in the global optimum behavior, the DP tool comes with a high computational cost. The charge-sustaining requirement and the necessity of capturing extremely small variations in the battery state of charge (SOC) makes this state vector an enormous variable. As things move fast in the industry, a rapid tool with the same performance is required.

Feed-forward low-throughput models have been developed to predict MFB50 and to control SOI in order to achieve a specific MFB50 target for diesel engines. The models have been assessed on a GMPT-E Euro 5 diesel engine, installed at the dynamic test bench at ICEAL-PT (Internal Combustion Engine Advanced Laboratory at the Politecnico di Torino) and applied to both steady state and transient engine operating conditions. MFB50 indicates the crank angle at which 50% of the fuel mass fraction has burned, and is currently used extensively in control algorithms to optimize combustion phasing in diesel engines in real-time. MFB50 is generally used in closed-loop combustion control applications, where it is calculated by the engine control unit, cycle-by-cycle and cylinder by-cylinder, on the basis of the measured in-cylinder pressure trace, and is adjusted in order to reduce the fuel consumption, combustion noise and engine-out emissions.

Beside hard facts as performance, emissions and fuel consumption especially the brand specific attributes such as styling and sound are very emotional, unique selling prepositions. To develop these emotional characters, within the given boundary conditions of the future pass-by regulation, it is necessary to define them at the very beginning of the project and to follow a consequent development process. The following paper shows examples of motorcycle NVH development work on noise cleaning and sound engineering using a hybrid development process combining front loading, simulation and testing. One of the discussed solutions is the investigation of a piston pin offset in combination with a crankshaft offset for the reduction of friction. The optimization of piston slap noise as a result of the piston secondary motion was performed by simulation. As another example a simulation based development was performed for the exhaust system layout.

This paper presents the modeling of the transient phase of catalyst heating on a high-performance turbocharged spark ignition engine with the aim to accurately predict the exhaust thermal energy available at the catalyst inlet and to provide a “virtual test rig” to assess different design and calibration options. The entire transient phase, starting from the engine cranking until the catalyst warm-up is completed, was taken into account in the simulation, and the model was validated using a wide data-set of experimental tests. The first step of the modeling activity was the combustion analysis during the transient phase: the burn rate was evaluated on the basis of experimental in-cylinder pressure data, considering both cycle-to-cycle and cylinder-to-cylinder variations.

In this paper a novel approach to mimic through numerical simulation Cycle-to-Cycle Variations (CCV) of the combustion process of Spark Ignition (SI) engines is described. The proposed methodology allows to reproduce the variability in combustion which is responsible for knock occurrence and thus to replicate the stochastic behavior of this abnormal combustion phenomenon. On the basis of the analysis of a comprehensive database of experimental data collected on a typical European downsized and turbocharged SI engine, the proposed approach was demonstrated to be capable to replicate in the simulation process the same percentage of knocking cycles experimentally measured in light-knock conditions, after a proper calibration of the Kinetics-Fit (KF), a new phenomenological knock model which was recently developed by Gamma Technologies.

Usually, both an experimental modal analysis or a numerical modal analysis performed on reduced model present the problem of master nodes selection. A methodology based on the experience is normally used or computationally heavy criterion can be applied. In that paper, the Modal-Geometrical Selection Criterion (MoGeSeC) is applied to a crankshaft, both for an EMA (experimental modal analysis) and for a reduction procedure. Then the results are compared with other literature criteria. As far as the EMA is concerned, the nodes suggested by MoGeSeC and other criteria are used for identification of the component. The connection conditions between components are origin of uncertainty but in that case the comparison is done for each methodology in the same conditions. In that way MoGeSeC proves to be a very quick and accurate method because the nodes it selects depicts very well the dynamic behavior of the components.

The electrification of the powertrain is a prerequisite to meet future fuel consumption limits, while the internal combustion engine (ICE) will remain a key element of most production volume relevant powertrain concepts. High volume applications will be covered by electrified powertrains. The range will include parallel hybrids, 48V- or High voltage Mild- or Full hybrids, up to Serial hybrids. In the first configurations the ICE is the main propulsion, requiring the whole engine speed and load range including the transient operation. At serial hybrid applications the vehicle is generally electrically driven, the ICE provides power to drive the generator, either exclusively or supporting a battery charging concept. As the ICE is not mechanically coupled to the drive train, a reduction of the operating range and thus a partial simplification of the ICE is achievable.

A new test bench has been set up and equipped in order to analyze the air mean motion and turbulence quantities in the combustion system of an automotive diesel engine with one helicoidal intake duct and a conical type in-piston bowl. A sophisticated HWA technique employing single- and dual-sensor probes was applied to the in-cylinder flow investigation under motored conditions. The anemometric probe was also operated as a thermometric sensor. An analytical-numerical procedure, based on the heat balance equations for both anemometric and thermometric wires, was refined and applied to compute the gas velocity from the anemometer output signal. The gas property influence, the thermometric sensor lag and the prong temperature effects were taken into account with this procedure. The in-cylinder velocity data were reduced using both a cycle-resolved approach and the conventional ensemble-averaging procedure, in order to separate the mean flow from the fluctuating motion.

Numerical simulation can be effectively used to reduce the experimental tests which are nowadays required for the analysis and calibration of engine control systems. In particular in this paper the use of a one-dimensional engine model to analyze the response of an UEGO sensor in the exhaust manifold of a 4 cylinder s.i. engine (with multipoint fuel injection) is described: numerical simulation has been used to simulate a misfunction of the fuelling system, which caused one of the four cylinders to be fuelled with an air/fuel ratio that was 10% richer than the others. The simulated UEGO response was then compared with experimental measurements, and after this validation process, the sensor model can be used to study a proper fuel injection control strategy thus reducing the required experimental tests, as outlined in a test case presented at the end of the paper.

Nowadays numerical simulations play a major role in the development of future sustainable powertrain thanks to their capability of investigating a wide spectrum of innovative technologies with times and costs significantly lower than a campaign of experimental tests. In such a framework, this paper aims to assess the predictive capabilities of an 1D-CFD engine model developed to support the design and the calibration of the innovative highly efficient spark ignition engine of the PHOENICE (PHev towards zerO EmissioNs & ultimate ICE efficiency) EU H2020 project. As a matter of fact, the availability of a reliable simulation platform is crucial to achieve the project target of 47% peak indicating efficiency, by synergistically exploiting the combination of innovative in-cylinder charge motion, Miller cycle with high compression ratio, lean mixture with cooled Exhaust Gas Recirculation (EGR) and electrified turbocharger.

Spark Ignited (SI) combustions engines in combination with different degrees of hybridization are expected to play a major role in future vehicle propulsion. Due to the combustion principle and the related thermodynamic efficiency, it is especially challenging to meet future CO2 targets. The layout and optimization of the overall system requires novel methods in the development process which feature a seamless transition between real and virtual prototypes. Herein, engine models need to predict the entire engine operating range in steady-state and transient conditions and must respond to all relevant control inputs. In addition, the model must feature true real-time capability. This work presents a holistic and modular modeling framework, which considers all relevant processes in the complex chain of physical effects in SI combustion.

Over the next decade, CO2 legislation will be more demanding and the automotive industry has seen in vehicle electrification a possible solution. This has led to an increasing need for advanced powertrain systems and systematic model-based control approaches, along with additional complexity. This represents a serious challenge for all the OEMs. This paper describes a novel reverse engineering methodology developed to estimate relevant powertrain data required for fuel consumption-oriented hybrid electric vehicle (HEV) modelling. The estimated quantities include high-voltage battery internal resistance, electric motor and transmission efficiency, gearshift thresholds, torque converter performance diagrams, engine fuel consumption map and front/rear hydraulic brake torque distribution. This activity provides a list of dedicated experimental tests, to be carried out on road or on a chassis dynamometer, aiming at powertrain characterization thanks to a suitable post-processing algorithm.

In the present work, a scalable simulation methodology is presented that enables the assessment of the impact of SI-engine cycle-to-cycle combustion variations on fuel consumption and hence CO2 emissions on three different levels of modeling depth: in-cylinder, steady-state engine and transient engine and vehicle simulation. On the detailed engine combustion chamber level, a 3D-CFD approach is used to study the impact of the turbulent in-cylinder flow on the cycle-resolved flame propagation characteristics. On engine level, cycle-to-cycle combustion variations are assessed regarding their impact on indicated mean effective pressure, aiming at estimating the possible fuel consumption savings when cyclic variations are minimized. Finally, on the vehicle system level, a combined real-time engine approach with crank-angle resolved cylinder is used to assess the potential fuel consumption savings for different vehicle drivecycle conditions.

Despite the legislation targets set by several governments of a full electrification of new light-duty vehicle fleets by 2035, the development of innovative, environmental-friendly Internal Combustion Engines (ICEs) is still crucial to be on track toward the complete decarbonization of on road-mobility of the future. In such a framework, the PHOENICE (PHev towards zerO EmissioNs & ultimate ICE efficiency) project aims at developing a C SUV-class plug-in hybrid (P0/P4) vehicle demonstrator capable to achieve a -10% fuel consumption reduction with respect to current EU6 vehicle while complying with upcoming EU7 pollutant emissions limits. Such ambitious targets will require the optimization of the whole engine system, exploiting the possible synergies among the combustion, the aftertreatment and the exhaust waste heat recovery systems.

To achieve future fleet CO2 emission targets, all powertrain types, including those with internal combustion engines, need to achieve higher efficiency. Next to others the reduction of friction is one contributor to increase powertrain efficiency. The piston bore interface (PBI) accounts for up to 50 % of the total engine friction losses [1]. Optimizations in this area combined with the use of low viscosity oil, which can reduce the friction of further engine sub-systems, will therefore have a high positive impact. To assess the friction of the PBI whilst considering cross effects of other relevant parameters for mechanical function (e.g. blow-by & wear) and emissions (e.g. oil consumption) AVL has established a holistic development method based around the AVL FRISC (FRIction Single Cylinder) engine with a floating liner measurement concept.