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This work aims at addressing the challenge of reconciling the surge in road transportation with the need to reduce CO2 emissions. The research particularly focuses on exploring the potential of fuel cell technology in long-distance road haulage, which is currently a major solution proposed by relevant manufacturers to get zero local emissions and an increased total payload. Specifically, a methodology is applied to enable rapid and accurate identification of techno-economically effective fuel cell hybrid heavy-duty vehicle (FCH2DV) configurations. This is possible by performing model-based co-design of FCH2DV powertrain and related control strategies. Through the algorithm, it is possible to perform parametric scenario analysis to better understand the prospects of this technology in the decarbonization path of the heavy-duty transportation sector, changing in an easy way all the parameters involved.

This article presents a novel approach for predicting fuel consumption in vehicles through a recurrent neural network (RNN) that uses only speed, acceleration, and road slope as input data. The model has been developed for real-time vehicle monitoring, route planning optimization, cost and emissions reduction and it is suitable for fleet-management purposes. To train and test the RNN, chosen after addressing several structures, experimental data have been measured on-board of a heavy-duty truck representative of a heavy-duty transportation company. Data have been acquired during typical daily missions, making use of an advanced connectivity platform, which features CANbus vehicle connection, GPS tracking, 4G/LTE - 5G connectivity, along with on-board data processing. The experimental data used for RNN train and test have been treated starting from on-board acquired raw data (e.g., speed, acceleration, fuel consumption, etc.) along with road slope downloaded from map providers.

Hybrid electric vehicles (HEVs) represent one of the main technological options for reducing vehicle CO2 emissions, helping car manufacturers (OEMs) to meet the stricter targets which are set by the European Green Deal for new passenger cars at 80 g CO2/km by 2025. The optimal power-split between the internal combustion engine (ICE) and the electric motor is a challenge since it depends on many unpredictable variables. In fact, HEV improvements in fuel economy and emissions strongly depend on the energy management strategy (EMS) on-board of the vehicle. Dynamic Programming approach (DP), direct methods and Pontryagin’s minimum principle (PMP) are some of the most used methodologies to optimize the HEV power-split. In this paper two online strategies are evaluated: an Adaptive-RuleBased (A-RB) and an Adaptive-Equivalent Consumption Minimization Strategy (A-ECMS). At first, a description of the P2 HEV model is made.

In the global quest for preventing fossil fuel depletion and reducing air pollution, hybridization plays a fundamental role to achieve cleaner and more fuel-efficient automotive propulsion systems. While hybrid powertrains offer many opportunities, they also present new developmental challenges. Due to the many variants and possible architectures, development issues, such as the definition of powertrain concepts and the optimization of operating strategies, are becoming more and more important. The paper presents model-based fuel economy analyses of different hybrid vehicle configurations, depending on the position of the electric motor generator (EMG). The analyses are intended to support the design of powertrain architecture and the components sizing, depending on the driving scenario, with the aim of reducing fuel consumption and CO2 emissions.

1 The wall-flow Diesel Particulate Filter (DPF) is currently the most common after-treatment system used to meet the particulate emissions regulations for automotive engines. Today’s technology shows the best balance between filtration efficiency and back-pressure in the engine exhaust pipe. During the accumulation phase the pressure drop across the filter increases, thus requiring periodic regeneration of the DPF through after and post fuel injection strategies. This paper deals with the development of a control oriented model of a catalytic silicon carbide (SiC) wall flow DPFs with CuFe2O4 loading for automotive Diesel engines. The model is intended to be used for the real-time management of the regeneration process, depending on back-pressure and thermal state.

In the last few years, several studies have proved the benefits of exploiting information about the road topography ahead of the vehicle to adapt vehicle cruising for fuel consumption reduction. Recent technologies have brought on-board more road information enabling the optimization of the driving profile for fuel economy improvement. In the present paper, a cruise controller able to lowering vehicle fuel consumption taking into account the characteristics of the road the vehicle is traveling through is presented. The velocity profile is obtained by minimizing via discrete dynamic programming the energy spent to move the vehicle. In order to further enhance vehicle fuel efficiency, also the gear shifting schedule is optimized, allowing to avoid useless gear shifts and choose the most suitable gear to match current road load and keeping the engine in its maximum efficiency range. Despite the optimality of the solution provided, dynamic programming entails high computational time.

Road topography has a remarkable impact on vehicle fuel consumption for both passenger and heavy duty vehicles. In addition, erroneous or non-optimized scheduling of both velocity set-point and gear shifting may be detrimental for fuel consumption and performance. Recent technologies have made road data, such as elevation or slope, either available or measurable on board, thus making possible the exploitation of this additional information in innovative controllers. The aim of this paper is the development of a smart, fuel-economy oriented controller adapting cruising speed and engaged gear to current road load (i.e. local slope). Unlike traditional cruise controllers, the velocity set-point is not constant, but it is set by applying a mathematical transformation of the current slope, accounting for the mission time duration as well.

The development of more affordable sensors together with the enhancement of computation features in current Engine Management Systems (EMS), makes the in-cylinder pressure sensing a suitable methodology for the on-board engine control and diagnosis. Since the 1960’s the in-cylinder pressure signal was employed to investigate the combustion process of the internal combustion engines for research purposes. Currently, the sensors cost reduction in addition to the need to comply with the strict emissions legislation has promoted a large-scale diffusion on production engines equipment. The in-cylinder pressure signal offers the opportunity to estimate with high dynamic response almost all the variables of interest for an effective engine combustion control even in case of non-conventional combustion processes (e.g. PCCI, HCCI, LTC).

In the last years, the research effort of the automotive industry has been mainly focused on the reduction of CO2 and pollutants emissions. In this scenario, concepts such as the engines downsizing, stop/start systems as well as more costly full hybrid solutions and, more recently, Waste Heat Recovery technologies have been proposed. These latter 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 modeling of ORC power plant for simulation analyses aimed at evaluating the opportunities and challenges of its application for the waste heat recovery in a compact car, powered by a turbocharged SI engine.

Main aim of this paper was to exploit the well-known Willans line method in a twofold manner: indeed, beyond the usual identification of Willans line parameters to enable internal combustion engine scaling, it is also proposed to infer further information from identified parameters and correlations, particularly aiming at characterizing mechanical and frictional losses of different engine technologies. The above objectives were pursued relying on extended experimental performance data, which were gathered on different engine families, including turbo-charged Diesel and naturally aspirated gasoline engines. The matching between Willans line scaled performance and experimental ones was extensively tested, thus allowing to reliably proceed to the subsequent objective of characterizing mechanical losses on the basis of identified Willans parameters.

International regulations continuously restrict the standards for the exhaust emissions from automotive engines. In order to comply with these requirements, innovative control and diagnosis systems are needed. In this scenario the application of methodologies based on the in-cylinder pressure measurement finds widespread applications. Indeed, almost all engine thermodynamic variables useful for either control or diagnosis can be derived from the in-cylinder pressure. Apart for improving the control accuracy, the availability of the in-cylinder pressure signal might also allow reducing the number of existing sensors on-board, thus lowering the equipment costs and the engine wiring complexity. The paper focuses on the detection of the engine thermal state, which is fundamental to achieve suitable control of engine combustion and after-treatment devices.

This paper deals with modeling and analysis of the integration of ThermoElectric generators (TEG) into a conventional vehicle, specifically aimed at recovering waste heat from exhaust gases. The model is based on existing and commercial thermoelectric materials, specifically Bi2Te3, having ZTs not exceeding 1 and efficiency below 5%, but a trade-off between cost and performance that would be acceptable for automotive applications. TEGs operate on the principle of thermoelectric energy conversion via Seebeck effect, utilizing thermal gradients to generate electric current, with exhaust gases at the hot side and coolant at the cold side. In the simulated configuration the TEG converters are interfaced with the battery/alternator supporting the operation of the vehicle, reducing the energy consumption due to electrical accessories and HVAC.

In the last years the automotive industry has been involved in the development and implementation of CO2 reducing concepts such as the engines downsizing, stop/start systems as well as more costly full hybrid solutions and, more recently, waste heat recovery technologies. These latter include ThermoElectric Generator (TEG), Rankine cycle and Electric Turbo Compound (ETC) that have been practically implemented on few heavy-duty application but have not been proved yet as effective and affordable solutions for the automotive industry. The paper deals with the analysis of opportunities and challenges of the Electric Turbo Compound for automotive light-duty engines. In the ETC concept the turbine-compressor shaft is connected to an electric machine, which can work either as generator or motor. In the former case the power can satisfy the vehicle electrical demand to drive the auxiliaries or stored in the batteries.

The paper describes suited methodologies for developing Recurrent Neural Networks (RNN) aimed at estimating NOx emissions at the exhaust of automotive Diesel engines. The proposed methodologies particularly aim at meeting the conflicting needs of feasible on-board implementation of advanced virtual sensors, such as neural network, and satisfactory prediction accuracy. Suited identification procedures and experimental tests were developed to improve RNN precision and generalization in predicting engine NOx emissions during transient operation. NOx measurements were accomplished by a fast response analyzer on a production automotive Diesel engine at the test bench. Proper post-processing of available experiments was performed to provide the identification procedure with the most exhaustive information content. The comparison between experimental results and predicted NOx values on several engine transients, exhibits high level of accuracy.

The paper focuses on the experimental identification and validation of recurrent neural networks (RNN) for virtual sensing of NO emissions in internal combustion engines (ICE). Suited training procedures and experimental tests are proposed to improve RNN precision and generalization in predicting NO formation dynamics. The reference Spark Ignition (SI) engine was tested by means of an integrated system of hardware and software tools for engine test automation and control strategies prototyping. A fast response analyzer was used to measure NO emissions at the exhaust valve. The accuracy of the developed RNN model is assessed by comparing simulated and experimental trajectories for a wide range of operating scenarios. The results evidence that RNN-based virtual NO sensor will offer significant opportunities for implementing on-board feedforward and feedback control strategies aimed at improving the performance of after-treatment devices.

The paper focuses on the experimental identification and validation of recurrent neural networks (RNN) for real-time prediction and control of air-fuel ratio (AFR) in spark-ignited engines. Suited training procedures and experimental tests are proposed to improve RNN precision and generalization in predicting both forward and inverse AFR dynamics for a wide range of operating scenarios. The reference engine has been tested by means of an integrated system of hardware and software tools for engine test automation and control strategies prototyping. The comparison between RNNs simulation and experimental trajectories showed the high accuracy and generalization capabilities guaranteed by RNNs in reproducing forward and inverse AFR dynamics. Then, a fast and easy-to-handle procedure was set-up to verify the potentialities of the inverse RNN to perform feed-forward control of AFR.

The paper addresses the potentialities of Recurrent Neural Networks (RNN) for modeling and controlling Air-Fuel Ratio (AFR) excursions in Spark Ignited (SI) engines. Based on the indications provided by previous studies devoted to the definition of optimal training procedures, an RNN forward model has been identified and tested on a real system. The experiments have been conducted by altering the mapped injection time randomly, thus making the effect of fuel injection on AFR dynamics independent of the other operating variables, namely manifold pressure and engine speed. The reference engine has been tested by means of an integrated system of hardware and software tools for engine test automation and control strategies prototyping. The developed forward model has been used to generate a reference AFR signal to train another RNN model aimed at simulating the inverse AFR dynamics by evaluating the fuel injection time as function of AFR, manifold pressure and engine speed.

The paper deals with the use of Recurrent Neural Networks (RNNs) for the Air-Fuel Ratio (AFR) control in Spark Ignition (SI) engines. Because of their features, Neural Networks can perform an adaptive control more efficiently than classical techniques. In the paper, a review of the most useful control schemes based on Neural Networks is presented and the potential use in the field of engine control is analyzed. A preliminary controller has been implemented making use of a Direct Inverse Modeling approach. The controller compensates for the wall wetting dynamics and estimates the right amount of fuel to be injected to meet the target AFR during engine transients. The Direct Inverse Controller has been tested within an engine/vehicle simulator. The simulation tests have been performed by imposing a set of throttle transients at different engine speeds. The results show that the Inverse Model can satisfactorily bound the AFR excursions around the target value.

The measurement of the various pollutant species (HC, CO, NO, etc.) has become one of the main issues in internal combustion engine research. This interest concerns not only their quantitative measurement but also the study of the mechanism of their formation. In fact, pollutant species concentration can be used as an indicator for the combustion characteristics. For instance, it enables the determination of a lean or a rich combustion, the percentage of EGR, etc. The purpose of this research is to investigate the behavior of pollutant gas particles in the first part of an engine exhaust system through a detailed study of the unsteady flow in the exhaust pipe. The results are intended to designate the appropriate sensor positions which ensure accurate measurement results. This investigation wants to track an inert component in the exhaust system, namely the NO gas.

A computer code oriented to S.I. engine control and powertrain simulation is presented. The model predicts engine and driveline states, taking into account the dynamics of air and fuel flows into the intake manifold and the transient response of crankshaft, clutch, transmission gearing and vehicle. The whole model is integrated in the code O.D.E.C.S., now in use at Magneti Marelli, and is based on a hierarchical structure composed of different classes of models, ranging from black-box Neural Network to grey-box mean value models. By adopting the proposed approach, a satisfactory accuracy is achieved with limited computational demand, which makes the model suitable for the optimization of engine control strategies. Furthermore, in order to simulate the driver behavior during the assigned vehicle mission profile, two drive controllers have been implemented for throttle and brakes actuation, based on classical PID and fuzzy-logic theory.