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In response to the current and future energy and environment challenges, the automotive industry is strongly focusing on improving the fuel efficiency of vehicles. Although the electrification of automotive powertrains is clearly the principal path towards sustainable transportation, many opportunities still exist to improve the fuel economy of conventional vehicles. However, some of the technical solutions representing the state of the art in research and advanced development are difficult to benchmark in terms of their potential benefits for fuel consumption improvement. A greater understanding of the fuel energy utilization on the vehicle (here intended as a ‘system’ ) is therefore necessary in order to identify the readily available opportunities for efficiency improvements and, ultimately, to develop automobiles which are more fuel efficient. To this extent, this paper presents a review of the state of the art and technology trends in the field of energy management and recovery for automotive systems, with the primary focus on conventional powertrains. An understanding of the fuel energy utilization and dissipation associated with the vehicle subsystems (the engine, transmission, and chassis) is provided, as well as an overview of the opportunities and potential challenges in improving the fuel economy through system-level energy management, recovery, and harvesting. Finally, an overview of the most important solutions for managing energy dissipation, energy recovery, and harvesting is presented, discussing their potential for fuel economy improvement, technical readiness, and challenges. Wherever possible, projections on fuel economy improvements, based on either experimental data or simulations, are reported to provide opportunity for the assessment and comparison of current and future technologies.

Modelling the flow and efficiency of turbochargers for engine system simulation and control applications is an established practice that relies on the steady-state maps provided by manufacturer suppliers. However, as often occurs in practice, only a limited fraction of data is available in the compressor and turbine operating domain. For this reason, several modelling techniques have been proposed to interpolate and extrapolate flow and efficiency data. Most of the modelling approaches, based on black-or grey-box approaches, have limited predictive ability and typically low accuracy in off-design conditions, such as at engine idle or low engine speed. The current paper presents a novel model-based approach for overcoming the sparse nature of the available compressor maps, characterizing the flow and efficiency outputs of automotive centrifugal compressors by using extrapolation methods that are physically consistent with the conservation principles and actual behaviour of the system. The approach relies on a predictive model based on the thermodynamic analysis of a centrifugal compressor stage. The model builds upon the mass, energy, and entropy balance equations for compressible fluids. Specific sub-models are then introduced to account for the effects of slip phenomena, incidence losses, friction, and heat transfer losses, leading to high fidelity and predictive ability in off-design conditions. A detailed analysis of the model calibration and validation process is presented, utilizing data from two different automotive compressors. Finally, the procedure described is applied to characterize the compressor performance in engine system simulation, in comparison with a conventional (data-driven) model.

This article describes the development and experimental validation of a controloriented, real-time capable, Diesel engine instantaneous fuel consumption and brake torque model under warmed-up conditions with only two inputs: torque request and the engine speed and no other measurements. Such a model, with the capability of reliably and computationally efficiently estimating the aforementioned variables at both steady-state and transient engine-operating conditions, can be utilized in the context of real-time control and optimization of hybrid power train systems. Although Diesel engine dynamics are highly non-linear and very complex, by considering the Diesel engine and its control system, that is, engine control unit together as an entity, it becomes possible to predict the engine instantaneous fuel consumption and torque based on only those two inputs. A synergy between different modelling methodologies including physically based grey-box and data-driven black-box approaches were integrated in the Diesel engine model. The fuelling and torque predictions have been validated by means of experimental data from a medium-duty Diesel engine at both steady-state and transient operations, including engine start-ups and shutdowns.

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Two-stage turbochargers are a recent solution to improve engine performance. The large flexibility of these systems, able to operate in different modes, can determine a reduction of the turbo-lag phenomenon and improve the engine tuning. However, the presence of two turbochargers that can be in part operated independently requires effort in terms of analysis and optimization to maximize the benefits of this technology. In addition, the design and calibration of the control system is particularly complex. The transitioning between single stage and two-stage operations poses further control issues.In this scenario a model-based approach could be a convenient and effective solution to investigate optimization, calibration and control issues, provided the developed models retain high accuracy, limited calibration effort and the ability to run in real time. To this extent, this work documents the development and validation of a control-oriented model of a Diesel engine equipped with two-stage turbocharger. A bottom-up modeling approach, based on the principle of modularity, is adopted to characterize the turbocharger system, as well as the engine airpath, inclusive of a high-pressure EGR loop. The approach characterizes each component as a dynamical system, applying mass and energy conservation laws to derive a set of ordinary differential equations. The reliance on physical principles limits the number of calibration parameters, which can be identified from simple steady-state engine data at few operating points. The validated model is able to fully characterizing the flows, pressures and temperatures throughout the engine systems. In addition, prediction of engine performance (i.e., torque, BMEP, volumetric efficiency, specific fuel consumption) is provided to support further analysis, optimization and control studies. 1 2 1 2 J J J J J J J J · © § J J J in crit 2 1 2 J J J J J J J J (4) ¹ · © § J J

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