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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.

Nowadays, the need to focus on clean and eco-sustainable mobility is increasingly felt, also considering the more stringent regulations in favor of the ecological transition. A viable solution that is being consolidated is vehicle hybridization. Among different hybrid technologies, a promising one is the fuel cell hybrid electric vehicle (FCHV), particularly because this solution is based on hydrogen, a resource foreseen in all the future policies about environmental sustainability. However, FCHVs are still not widespread, mainly due to high costs; thus, their performance enhancing and design optimization are strategic goals to be pursued so as to make them more competitive. This paper presents and discusses the optimization of several FCHV design and control parameters, such as fuel cell system power, battery specific energy, power to weight ratio and final battery state of charge target.

The successful introduction of low-carbon footprint and highly efficient fuel cell vehicles represents nowadays a key action to achieve sustainable mobility worldwide. The main technological barriers (i.e., market price, lifetime and performance) to be overcome justifies an increasing attention towards the development of mathematical tools featuring co-optimization capabilities, so as to adequately account for the strong interactions and mutual influence between design criteria and selected control strategies. This paper thus presents and discusses the integration of a comprehensive model of a generic FCHV architecture with a specifications independent control strategy within a modular constrained optimization algorithm, the latter conceived in such a way to simultaneously find the optimal FCHV powertrain design and real-time applicable control strategies.

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.

Despite the recent commercial success of HEVs, their market share is still insufficient to produce a significant impact on energy consumption on a global basis. Moreover, it is unlikely that, in next few years, the scenario will drastically change, since relevant investments on production plants would be needed and the market does not seem to provide the expected growth for such technologies. Therefore, the possibility of upgrading conventional vehicles to hybrid electric vehicles is gaining interest. Among the diverse options for hybridization, researchers are focusing on electrification of rear wheels in front-driven vehicles, by adopting in-wheel motors and adding a lithium-ion battery. Thus, the vehicle is transformed in a Through-The-Road parallel hybrid electric vehicle. This paper presents an energy-based model, developed in Matlab/Simulink environment, of a conventional vehicle hybridized by means of such conversion kit.

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 efficiency achievable with effective energy management strategies represents a key issue for modern hybrid electric vehicles (HEVs). In this paper, by comparing different HEVs architectures with the same power to weight ratio, the dependence of energy consumption on different degrees of hybridization and powertrain architectures is analyzed. The fuel economy achievable by using dynamic programming based strategies is considered as the benchmark. The comparative study analyzes also the influence of driving cycles and the impact of plug-in concepts both on fuel economy and battery lifetime. Numerical results on realistic vehicles highlight the higher energy saving potentialities offered by parallel HEVs, while series HEVs remain of interest because of their simpler energy management and higher suitability for plug-in operations.

In the paper, a rule-based (RB) control strategy is proposed to optimize on-board energy management on a Hybrid Solar Vehicle (HSV) with series structure. Previous studies have shown the promising benefits of such vehicles in urban driving in terms of fuel economy and carbon dioxide reduction, and that economic feasibility could be achieved in a near future. The control architecture consists of two main loops: one external, which determines final battery state of charge (SOC) as function of expected solar contribution during next parking phase, and the second internal, whose aim is to define optimal ICE- EG power trajectory and SOC oscillation around the final value, as addressed by the first loop. In order to maximize the fuel savings achievable by a series architecture, an intermittent ICE scheduling is adopted for HSV. Therefore, the second loop yields the average power at which the ICE is operated as function of the average values of traction power demand and solar power.

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 deals with a detailed study on the optimal sizing of a solar hybrid car, based on a longitudinal vehicle dynamic model and considering energy flows, weight and costs. The model describes the effects of solar panels area and position, vehicle dimensions and propulsion system components on vehicle performance, weight, fuel savings and costs. It is shown that significant fuel savings can be achieved for intermittent use with limited average power, and that economic feasibility could be achieved in next future, considering the expected trends in costs and prices.

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 development of a system of phenomenological models for the simulation of combustion and NOx-Soot emissions in Common-Rail Multi-Jet Diesel engines. The system has been built by following a modular modeling approach and is suitable for the implementation in the framework of Hardware In the Loop (HIL) ECU rapid prototyping. A single-zone model simulates the ignition delay and the combustion during a sequence of pilot, pre and main fuel injections for a production 1,9 liters Diesel engine equipped with High Pressure Injection system, electronically controlled. The heat release model is based on the synthetic description of both premixed and diffusive combustion. The Zeldovich mechanism has been used to simulate the formation of NO emissions while the Soot model is based on the approach proposed by Hiroyasu. The models have been tested vs. a wide set of experimental data with a good accuracy in predicting pressure cycle and heat release.

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.