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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.

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.

Advanced technologies such as direct injection DI, turbocharging and variable valve timing, have lead to a significant evolution of the gasoline engine with positive effects on driving pleasure, fuel consumption and emissions. Today's developments are primarily focused on the implementation of improved full load characteristics for driving performance and fuel consumption reduction with stoichiometric operation, following the downsizing approach in combination with turbocharging and high specific power. The requirements of a relatively small cylinder displacement with high specific power and a wide flexibility of DI injection specifications lead to competing development targets and additionally to a high number of degrees of freedom during optimization. In order to successfully approach an optimum solution, FEV has evolved an advanced development methodology, which is based on the combination of simulation, optical diagnostics and engine thermodynamics testing.

This paper introduces a new approach based on a statistical investigation and finite element analysis (FEA) methodology to predict the crankshaft torsional stiffness and stress concentration factors (SCF) due to torsion and bending which can be used as inputs for simplified crankshaft multibody models and durability calculations. In this way the reduction of the development time and effort of passenger car crankshafts in the pre-layout phase is intended with a least possible accuracy sacrifice. With the designated methodology a better approximation to reality is reached for crank torsional stiffness and SCF due to torsion and bending compared with the empirical approaches, since the prediction does not depend on the component tests with limited numbers of specimen, as in empirical equations, but on various FE calculations.

Future HD Diesel engine technology is facing a combination of both extremely low exhaust emission standards (US 2002/2004, EURO IV and later US 2007, EURO V) and new engine test procedures such as the European Transient Cycle (ETC) in Europe and the Not-to-Exceed Area (NTE) in the US). Customers furthermore require increased engine performance, improved efficiency, and long-term durability. In order to achieve all targets simultaneously, future HD Diesel engines must have improved fuel injection and combustion systems and utilize suitable technologies such as exhaust gas recirculation (EGR), variable geometry turbine turbocharger systems (VGT) and exhaust gas after-treatment systems. Future systems require precision controlled EGR in combination with a VGT-turbocharger during transient operation. This will require new strategies and calibration for the Electronic Engine Control Unit (ECU).

The fulfillment of the aggravated demands on future small-size High-Speed Direct Injection (HSDI) Diesel engines requires next to the optimization of the injection system and the combustion chamber also the generation of an optimal in-cylinder swirl charge motion. To evaluate different port concepts for modern HSDI Diesel engines, usually quantities as the in-cylinder swirl ratio and the flow coefficient are determined, which are measured on a steady-state flow test bench. It has been shown that different valve lift strategies nominally lead to similar swirl levels. However, significant differences in combustion behavior and engine-out emissions give rise to the assumption that local differences in the in-cylinder flow structure caused by different valve lift strategies have noticeable impact. In this study an additional criterion, the homogeneity of the swirl flow, is introduced and a new approach for a quantitative assessment of swirl flow pattern is presented.

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.

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.

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.

The battery of a vehicle with an electrified powertrain (Hybrid Electric Vehicle or Battery Electric Vehicle), is required to operate with highly dynamic power outputs, both for charging and discharging operation. Consequently, the battery current varies within an extensive range during operation and the battery temperature also changes. In some cases, the relationship between the current flow and the change in the electrical energy stored seems to be affected by inefficiencies, in literature described as current losses, and nonlinearities, typically associated with the complex chemical and physical processes taking place in the battery. When calculating the vehicle electrical energy consumption over a trip, the change in the electrical energy stored at vehicle-level has to be taken into account. This quantity, what we could call the vehicle electricity balance, is typically obtained through a time-based integration of the battery current of all the vehicle batteries during operation.

Component sizing generally represents a demanding and time-consuming task in the development process of electrified powertrains. A couple of processes are available in literature for sizing the hybrid electric vehicle (HEV) components. These processes employ either time-consuming global optimization techniques like dynamic programming (DP) or near-optimal techniques that require iterative and uncertain tuning of evaluation parameters like the Pontryagin’s minimum principle (PMP). Recently, a novel near-optimal technique has been devised for rapidly predicting the optimal fuel economy benchmark of design options for electrified powertrains. This method, named slope-weighted energy-based rapid control analysis (SERCA), has been demonstrated producing results comparable to DP, while limiting the associated computational time by near two orders of magnitude.

The technology used in hybrid vehicle concepts is significantly different from conventional vehicle technology with consequences also for the noise and vibration behavior. In conventional vehicles, certain noise phenomena are masked by the engine noise. In situations where the combustion engine is turned off in hybrid vehicle concepts, these noise components can become dominant and annoying. In hybrid concepts, the driving condition is often decoupled from the operation state of the combustion engine, which leads to unusual and unexpected acoustical behavior. New acoustic phenomena such as magnetic noise due to recuperation occur, caused by new components and driving conditions. The analysis of this recuperation noise by means of interior noise simulation shows, that it is not only induced by the powertrain radiation but also by the noise path via the powertrain mounts. The additional degrees of freedom of the hybrid drive train can also be used to improve the vibrational behavior.

Meticulous design of the energy management control algorithm is required to exploit all fuel-saving potentials of a hybrid electric vehicle. Equivalent consumption minimization strategy is a well-known representative of on-line strategies that can give near-optimal solutions without knowing the future driving tasks. In this context, this paper aims to propose an adaptive real-time equivalent consumption minimization strategy for a multi-mode hybrid electric powertrain. With the help of road recognition and vehicle speed prediction techniques, future driving conditions can be predicted over a certain horizon. Based on the predicted power demand, the optimal equivalence factor is calculated in advance by using bisection method and implemented for the upcoming driving period. In such a way, the equivalence factor is updated periodically to achieve charge sustaining operation and optimality.

Two single-cylinder diesel engines were optimised for advanced combustion performance by means of practical and cumulative hardware enhancements that are likely to be used to meet Euro 5 and 6 emissions limits and beyond. These enhancements included high fuel injection pressures, high EGR levels and charge cooling, increased swirl, and a fixed combustion phasing, providing low engine-out emissions of NOx and PM with engine efficiencies equivalent to today's diesel engines. These combustion conditions approach those of Homogeneous Charge Compression Ignition (HCCI), especially at the lower part-load operating points. Four fuels exhibiting a range of ignition quality, volatility, and aromatics contents were used to evaluate the performance of these hardware enhancements on engine-out emissions, performance, and noise levels.

A broad range of diesel, kerosene, and gasoline-like fuels has been tested in a single-cylinder diesel engine optimized for advanced combustion performance. These fuels were selected in order to better understand the effects of ignition quality, volatility, and molecular composition on engine-out emissions, performance, and noise levels. Low-level biofuel blends, both biodiesel (FAME) and ethanol, were included in the fuel set in order to test for short-term advantages or disadvantages. The diesel engine optimized in Part 1 of this study included cumulative engine hardware enhancements that are likely to be used to meet Euro 6 emissions limits and beyond, in part by operating under conditions of Homogeneous Charge Compression Ignition (HCCI), at least over some portions of the speed and load map.

Different diagnostic techniques were experimentally tested on a common rail automotive 4 cylinder diesel engine in order to evaluate their capabilities to fulfill the California Air Resources Board (CARB) requirements concerning the monitoring of fuel injected quantity and timing. First, a comprehensive investigation on the sensitivity of pollutant emissions to fuel injection quantity and timing variations was carried out over 9 different engine operating points, representative of the FTP75 driving cycle: fuel injected quantity and injection timing were varied on a single cylinder at a time, until OBD thresholds were exceeded, while monitoring engine emissions, in-cylinder pressures and instantaneous crankshaft revolution speed.