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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 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.

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 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 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.

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 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 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.

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.

A set of models for the prediction of mechanical efficiency as function of the operating conditions for an automotive spark ignition engine is presented. The models are embedded in an integrated system of models with hierarchical structure for the analysis and the optimal design of engine control strategies. The validation analysis has been performed over a set of more than 400 steady-state operating conditions, where classical engine variables and pressure cycles were measured. Models with different functional structures have been tested; parameter values and indices of statistical significance have been determined via non-linear and step-wise regression techniques. The Neural Network approach (Multi Layer Perceptrons with Back-Propagation) has been also used to evaluate the feasibility of using such an approach for fast black-box modelization.