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The deployment of electric and hybrid electric vehicle is accounted to be the most feasible solution for lowering the transportation sector pollution emissions and energy consumption. However, the transition to electrified mobility is not behind the corner and many challenges, such as battery recharging issues, free-carbon electricity and grid sustainability, remain unsolved. A sustainable solution, also from a Life-Cycle Assessment perspective, is the conversion of existing vehicles into hybrid solar cars. In this study, the latest updates of the LIFE-SAVE project for the development of an aftermarket kit for vehicles hybridization are presented. In particular, the application of the Pontryagin’s Minimum Principles for the optimal control of a transformed vehicle is presented. Results show that fuel economy on the vehicle both in charge sustaining and in charge depleting operations are improved by about 1% and 13%, respectively.

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.

In last years, increasing fleet electrification, improvement in solar panel efficiency and reduction in their costs are concurring toward an increasing attention to the integration of photovoltaic in road vehicles. As in fixed plants, the adoption of solar tracking systems would allow to enhance the solar contribution. But a mobile solar systems for a car must have specific features, due to space constraints and to specific exigencies of a mobile application such as instabilities, energy and aerodynamic losses. Due to these reasons, they should operate only in parking mode. A kinematic model of mobile solar roof, as a parallel robot, has been developed and used to optimize the roof geometry; the benefits of a mobile roof, in terms of solar energy gain at different latitudes and months, have been assessed. A second prototype of solar tracking roof with two degrees of freedom has been then realized, to overcome some mechanical problems.

In last decade, Hybrid Electric Vehicles (HEV) have emerged as real alternatives to engine-driven vehicles, in order to reduce fuel consumption and emissions. But their market share is still limited, as their impact on global fossil fuel demand and CO2 production. In parallel, the possibility of upgrading conventional vehicles to HEV is gaining interest. A research work on the development of a kit for converting a conventional vehicle into a Through-The-Road (TTR) Hybrid Solar Vehicle (HSV) has been recently performed at the University of Salerno, where flexible solar cells, an additional Lithium-Ion battery and two electrically driven wheel-motors have been mounted on a FIAT Punto. Preliminary studies performed by simulation have shown the technical and economic feasibility of this solution. In the proposed vehicle, the control of wheel motors is performed via a Vehicle Management Unit (VMU), which in turn reads data from the OBD port.

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.

The integration of photovoltaic (PV) panels on electric and hybrid vehicles is gaining interest, thanks to the increasing fleet electrification, the improvement in solar panel efficiency and the reduction in their costs. In order to maximize the solar contribution, the adoption of self-orienting solar roof when the vehicle is parked can be considered. In the paper, the authors present a study on the energy management of a moving solar roof, as a 3 d.o.f. parallel robot, in a solar assisted vehicle. A model based control is developed, based on combined use of measured solar power, image processing form a digital camera and data provided by a GPS module, and implemented over a small scale prototype. An optimal tracking strategy, considering the effects of different insolation and of mechanical losses, is also presented.

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 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 deals with the development of a multi-zone phenomenological model for the combustion process in a common rail multi-injection Diesel engine. The model simulates the fuel jet and its interaction with surrounding gases by dividing the jet core into many parcels in order to describe the thermal gradient and the chemical composition within the combustion chamber. This is mandatory for the simulation of the NO pollutant formation, carried out via the Zeldovich mechanism. The air entrainment into the fuel jet is modeled by means of the momentum balance applied to each zone and to the air zone. The stratification of the chemical composition within the cylinder and the details of the spray and its interaction with the air zone are simulated to estimate the spray penetration and speed, the mass of entrained air and the equivalence ratio in each zone. The combustion model is based on the laminar-and-turbulent characteristic-time approach.

A two zones combustion model suitable for the engine control design of common rail multi-jet Diesel engines is presented. The modeling approach is based on a semi-empirical two-zone combustion model coupled with identification analysis in order to implement a predictive tool for simulating the effects of control injection strategies on combustion and exhaust emissions. Fuel jet formation and combustion for both premixed and diffusive regimes are predicted, by dividing the combustion chamber into two control volumes; these account for the fuel jet and the surrounding air, composed by fresh air and residual gases; the fuel jet is divided into two zones to separate liquid and vapor phases. The simulation results have shown that the model predicts the effects of different injection parameters in case of single and multiple injection in a short computational time, suitable for the accomplishment of intensive simulations or optimization analyses over generic engine driving cycles.

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.

An integrated system of phenomenological models is applied in conjunction with identification techniques to simulate SI engine performance and emissions. In the framework of a hierarchical model architecture, the model structure provides the steady state engine data required for the design and validation of synthetic engine models. This approach allows limiting the recourse to the experimental data and speeds up the engine control strategies prototyping. The model structure is composed of a multi-zone thermodynamic engine model linked to a 1-D commercial fluid-dynamic model for intake and exhaust gas flow and to a physical model for NOx exhaust emissions. In order to improve model accuracy and generalization, an identification methodology is applied to estimate the optimal parameters for the turbulent combustion model. Due to the built-in physical content, the proposed methodology requires a relatively limited amount of experimental data for characterizing the under-study engine.

The paper deals with the application of two techniques for the selection of the training data set used for the identification of Neural Network black-box engine models; the research starts from previous studies on Sequential Experimental Design for regression based engine models. The implemented methodologies rely on the Active Learning approach (i.e. active selection of training data) and are oriented to drive the experiments for the Neural Network training. The methods allow to select the most significant examples leading to an improvement of model generalization with respect to a heuristic choice of the training data. The data selection is performed making use of two different formulation, originally proposed by MacKay and Cohn, based on the Shannon's Statistic Entropy and on the Mean Error Variance respectively.

A computer code oriented to S.I. engine control and powertrain simulation is presented. The model, developed in Matlab-Simulink® environment, 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, transmission gearing and vehicle. The model, derived from the code O.D.E.C.S. for the optimal design of engine control strategies now in use at Magneti Marelli, is suitable both for simulation analysis and to achieve optimal engine control strategies for minimum consumption with constraints on exhaust emissions and driveability via mathematical programming techniques. The model is structured as an object oriented modular framework and has been tested for simulating powertrain system and control performance with respect to any given transient and control strategy.