Gianfranco Rizzo. Born in Naples, 1952. Received in 1975 a laurea degree "cum laude" in Mechanical Engineering at University of Naples. He worked at FIAT in Turin, at Research National Council of Italy, at University of Naples and at University of Salerno (since 1992), where he is Full Professor in Mechanical Engineering. Vice-President of SAE Naples Group (2001-2005), Chair of the IFAC TC “Automotive Control” (2008-). Author of about 120 papers on: Modeling and Control of Automotive Engines, Hybrid and Solar Vehicles, Modeling and Optimization of Conventional and Hybrid Power Plants, Cogeneration, Turbomachines, Optimal Management of Bio-Economic Systems. Full list available at http://www.gianfrancorizzo.it/Papers.asp Detailed CV available here.
Abstract: The paper focuses on the simulation, analysis and control of the energy flow in a parallel hybrid electric vehicle (HEV). HEVs operation is concerned with the on board conversion of chemical, electric and mechanic energy and its optimal control is essential in order to increase the global system efficiency.
A dynamic model is used to describe the driver-vehicle interaction for a generic transient and to simulate the vehicle driveline, the internal combustion engine (ICE) and the electric motor/generator (EM). An estimate of future vehicle load is performed with a neural network to optimize the supervisory control strategy during the estimated future time window.
A description of the whole model is presented and the simulation results carried out for a real driving cycle are reported.
Abstract: 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 Shannonâs Statistic Entropy and on the Mean Error Variance respectively. These techniques have been applied to assist the training of
artificial Neural Networks for the estimation of engine torque and exhaust emissions of an S.I. engine, to be embedded into a powertrain dynamic model for the optimal design of engine control strategies (O.D.E.C.S.), now in use at Magneti Marelli.
Abstract: 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 nonlinear 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. The proposed regression models,
characterized by a very limited computational demand, exhibit excellent performance over a large set of experimental data, with less than ten parameters but requiring a rather complex engine geometrical and operative description. On the other hand, the Neural Network model has been developed considering as independent variables only four measurable engine parameters and the training has been performed using a reduced set of experimental data. The results presented show a relevant precision improvement with respect the available models cited in literature. The different model structures developed are suitable for several uses, both for off-line and on-line applications.
Abstract: The development and the identification of a model for the analysis of an aero-derivative gas turbine power plant employed in natural
gas pipeline stations are presented. The purpose is establishing a method for prediction and remote monitoring of pollutant
emissions, in particular nitric oxide, starting from the operating data provided by the usual gas turbine control unit.
Thermodynamic models of both plant and components are employed, including a chemical kinetics based simulation of NO
formation. The full set of thermal cycle parameters is identified via proper optimisation techniques, starting from a limited set of
operating data. A good agreement with experimental data is achieved. In addition, unreliable operating data can be interpreted and
detection of either component or sensor fault is allowed. Continuous on-line prediction of both performance and emissions during the
normal gas turbine operation, when only the normal control unit is active, can be then achieved. Some preliminary results obtained
by use of components matching approach are also presented, and the potential advantages discussed.
The methodology can be also considered a basis for the definition of strategies for part-load operation planning and load sharing
between several gas turbine units, with the objective of minimum pollutant emission.
Notes: Presented at the 6th IEEE Med. Conference, Alghero (Italy), June 9-11, 1998.
Abstract: In the last years a hierarchical model structure has been developed by the authors for the optimal design of engine control strategies.
This structure is composed of a group of models based on different approaches ranging from fast black-box models to
phenomenological ones. The black-box models together with grey-box mean value dynamical models are linked with the computer
code O.D.E.C.S. which is used in industrial environment for the optimization of engine control strategies.
In the framework of black-box models development, some approaches have been considered in order to enhance model precision and
to maximize the level of information derivable from experimental tests. The former set of models is based on classical regression
techniques to compute steady state engine performance (i.e. fuel consumption and HC, CO, NOx emission levels) together with
interpolating techniques in order to evaluate performances in the domain not investigated during experimental tests.
To overcome some limitations coming out from the use of regressions and interpolating techniques, a Neural Network model
structure has been developed. The Neural Networks, well suited for non linear phenomena modelization, are able to deal with high
uncertainty input level (independent data variables) or noised data as well as are able to operate outside their range of training
experience. For the purpose of the present application a Multi Layer Perceptron (MLP) Neural Network structure has been selected
with a Backpropagation training procedure.
The results of simulations obtained by using the Neural Network model developed are compared with the previously used regression
technique and the advantages emerging from the new approach are discussed.
Notes: Presented at the 6th IEEE Med. Conference, Alghero, June 9-11, 1998.
Abstract: The structure of a hierarchical system of models for model based optimization and rapid prototyping of control strategies in
automotive spark ignition engines is presented. The advantages of a distributed and hybrid modeling approach are discussed, in order
to meet the conflicting goals of high flexibility and precision with limited experimental cost and computational time.
The critical role played by the identification phase is discussed, and a comparative analysis of the estimation procedures adopted
during model validation is presented, analyzing their connections with the model development process.
Notes: Presented at the 6th IEEE Med. Conference, Alghero, June 9-11.
Abstract: A study on optimal energy management on a Hybrid Solar Vehicle (HSV) with series structure is presented. Previous results obtained by optimal design analysis for HSV confirmed the relevant benefits of such vehicles with respect to conventional cars in case of intermittent use in urban driving (city-car), and that economical feasibility could be achieved in a near future. In order to develop a supervisory control for a HSV prototype under development at University of Salerno, a study on the performance achievable by an intermittent use of the ICE powering the electric generator is presented. In particular, the effects of engine thermal transients on fuel consumption and HC emissions are studied and discussed. The optimal ICE power trajectory is found by solving a non-linear constrained optimization that suitably accounts for fuel mileage and state of charge, also considering solar contribution during parking mode.
Abstract: The paper deals with the modeling, control and testing of a Hybrid Solar Vehicle (HSV) prototype. Vehicle set-up and instrumentation are accomplished at University of Salerno (UNISA), within an EU funded Leonardo project, starting from an existing electric vehicle. Suited experimental activities were performed to identify and validate a comprehensive model of the propulsion system resulting from the integration of a series hybrid powertrain with a photovoltaic (PV) array.
Then, a simulation analysis was performed to address on-board energy management issues as well as assess prototype performance over a selected driving cycle. Simulation results show that appropriate components sizing and supervisory control strategies concur in improving fuel economy significantly, up to 30 kilometers per liter of Diesel fuel.
Abstract: This paper deals with the development of a prototype of Hybrid Solar Vehicle (HSV) with series structure. This activity has been also conducted in the framework of the EU funded Leonardo project âEnergy Conversion Systems and Their Environmental Impactâ, a project with research and educational objectives. A study on supervisory control for hybrid solar vehicles and some preliminary tests performed on the road are presented. Previous results obtained by a model for HSV optimal design have confirmed the relevant benefits of such vehicles with respect to conventional cars in case of intermittent use in urban driving (city-car), and that economical feasibility could be achieved in a near future. Due to the series-powertrain adopted for the HSV prototype, an intermittent use of the ICE powering the electric generator is possible, thus avoiding part-load low-efficient engine operations. The best ICE power trajectory is determined via genetic algorithm optimization accounting for fuel mileage as well as battery state of charge, also considering solar contribution during parking mode. The experimental set up used for data logging, real-time monitoring and control of the prototype is also presented, and the results obtained with different road tests discussed.
Abstract: Hybrid Solar Vehicles (HSV), derived by integration of Hybrid Electric Vehicles with Photo-Voltaic sources, may represent a valuable solution to face both energy saving and environmental issues, particularly in urban driving. Previous studies have also shown that economic feasibility could be achieved in a near future. After a presentation of the perspectives and the problems related to the use of such vehicles, the paper focuses on their management strategies, evidencing some significant differences with respect to the case of Hybrid Electric Vehicles.
In order to develop a supervisory control for an HSV prototype under development at University of Salerno, a study on the performance achievable by an intermittent use of the ICE powering the electric generator is presented. The results obtained by the application of Genetic Algorithms (GA) to the optimal energy management of an HSV with series structure are discussed. The optimal powering strategy accounts for fuel mileage and state of charge, also considering solar contribution during parking mode and the effects of engine thermal transients on fuel consumption and HC emissions. The effects of power-train, vehicle and external variables on optimal strategies are also studied and discussed.
Abstract: The paper focuses on the experimental identification and validation of recurrent neural network (RNN) models for air-fuel ratio (AFR) estimation and control in spark-ignited engines. Suited training procedures and experimental tests are proposed to improve RNN precision and generalization in predicting AFR transients 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 simulations performed on the test-sets show
the ability of the RNN to reproduce the target patterns with satisfactory accuracy. Finally, real time implementation of RNN has been accomplished by developing and testing an inverse neural network controller acting on the injection time to limit AFR excursions from stoichiometry.
Abstract: Some of the major limitations of renewable energy sources are represented by their low power density and intermittent nature, largely depending upon local site and unpredictable weather conditions. These problems concur to increase the unit costs of wind power, so limiting their diffusion. By coupling storage systems with a wind farm, some of the major limitations of wind power, such as a low power density and an unpredictable nature, can be overcome. Furthermore, the use of time-series neural network-based prediction models aims at reducing the stochastic uncertainty of wind power. A Matlab/Simulink model of a hybrid power plant consisting of a wind farm coupled with Compressed Air Energy Storage (CAES) is presented. In CAES energy is stored as compressed air in a reservoir during off-peak periods, while it is used on demand during peak periods to generate power with a turbo-generator system. Such plants can offer significant benefits in terms of flexibility in matching a fluctuating power demand, particularly when coupled with renewable sources. The model employs ANN-based wind speed forecasting to determine the optimal daily operation strategy for the storage system. As shown in the paper, the knowledge of the expected available energy is a key factor to optimize the management strategies of the proposed hybrid power plant. A detailed economic analysis has been carried out: investment and maintenance costs are estimated based on literature data, while operational costs and revenues are calculated according to the Italian energy market prices.
Abstract: A study on optimal energy management on a hybrid solar vehicle (HSV) with series structure is presented. Previous results obtained by optimal design analysis for HSV have confirmed the relevant benefits of such vehicles with respect to conventional cars in case of intermittent use in urban driving (city-car), and that economical feasibility could be achieved in a near future. In order to develop a supervisory control for a HSV prototype now under development at University of Salerno, a study on the performance achievable by an intermittent use of the ICE powering the electric generator is presented. In particular, the effects of engine thermal transient on fuel consumption are studied and discussed. The optimal ICE power trajectory is found by solving a non-linear constrained optimization that suitably accounts for fuel mileage and state of charge, also considering solar contribution during parking mode.
Abstract: Hybrid electric vehicles (HEVs) gain more and more attention, as they represent a more environmental friendly alternative to conventional vehicles. If combined with
photovoltaic panels, they can lead to further reduction of emissions. The paper focuses on a solution for minimizing the fuel consumption in a series hybrid solar vehicle (HSV). After briefly introducing the model, first a global optimum in fuel
consumption is presented using dynamic programming, as a reference value. As a real-time control strategy, Model Predictive Control (MPC) is considered. A fuel consumption equivalent quantity is defined which is used for calculating the fuel needed to bring the battery state of charge to a starting value (set in this case for 0.7 in relative units). For different values of the MPC tuning parameters simulations are
performed using the urban section of the New European Drive Cycle. Conclusions based on the simulations are presented.
Abstract: A model of a hybrid power plant consisting of Compressed Air Exergy Storage (CAES) coupled with a wind farm is presented. The model employs neural network-based wind speed forecasting . By coupling CAES with a wind farm, some of the major limitations of wind power, such as a low power density and an unpredictable nature, can be overcome. The use of time-series neural network-based prediction models aims at reducing the stochastic uncertainty of wind power. As shown in the paper, knowledge of the future
incoming energy can be a powerful means for planning the daily operation strategy of the storage system.
A detailed economic analysis has been carried out, evaluating investment, maintenance and operational costs using actual energy market prices. The benefits critically depend on the performance of each subsystem, incoming energy, user load and economic regulations. Results show that advantages in terms of Net Present Value, energy savings and CO2 mitigation can be achieved.
Abstract: In last years, as a consequence of the diffusion of Hybrid Electric Vehicles and to the growing recourse to renewable sources, increasing attention is being given to the integration of these vehicles with photovoltaic panels. Hybrid Solar Vehicles might represent a valuable solution to face both energy saving and environmental issues, particularly in case of intermittent use in urban conditions, but relatively little research effort has been spent in this direction. This paper focuses the main problems related to the development of these vehicles, with specific attention on photovoltaic panels and power electronics. Some results obtained by a model for the optimal design of a hybrid solar vehicle with series structure, including effects of vehicle dimensions, weight and costs, are presented and discussed.
Abstract: A comprehensive model for the study and the optimal design of a solar hybrid vehicle with series architecture has been presented. The model describes energy flows between horizontal and/or vertical solar panels, internal combustion engine, electric generator, electric motor and batteries, considering vehicle longitudinal dynamics and the effect of control strategies. Vehicle weight is predicted, starting from a database of commercial vehicles, considering the effects of power-train sizing, vehicle dimensions and possible use of aluminum. The effects of vehicle dimensions on aerodynamic losses and maximum panel area also can be accounted for. The model predicts the additional costs with respect to
conventional vehicles, and the pay-back.
It has been shown that significant savings in fuel consumption and emissions can be obtained with an intermittent use of the vehicle at limited average power, compatible with typical use in urban conditions during working days. This result has been obtained with commercial PV panels and with realistic data and
assumptions on the achievable net solar energy for propulsion. The future adoption of last generation photovoltaic panels, with nominal efficiencies approaching 35%, may result in an almost complete solar autonomy of this kind of vehicle for such uses. By adopting up to date technology for electric motor and generator, batteries and chassis, power to weight ratio comparable with the ones of commercial cars can be achieved, thus assuring acceptable vehicle performance.
Future developments may concern a systematic study of optimal configuration for various driving cycles and latitudes, also considering seasonal variations of the
solar energy, more accurate study of control strategies, including possible application of on-board optimization coupled with provisional methods for car load and solar energy based on Recurrent Neural Network. More detailed models for component weights and costs, including non-linear effects, also can be necessary, as well as further studies on the interactions between vehicle and propulsion system.
The results obtained by optimization analysis over a ECE/EUDC cycle have shown that the hybrid solar vehicles, although still far from economic feasibility, could reach acceptable payback values if large but not unrealistic variations in costs, prices and panel efficiency will occur: considering recent trends in renewable energy field and actual geo-political scenarios, it is reasonable to expect further reductions in costs for PV panels, batteries and advanced electric motors and generators, while relevant increases in fuel cost could not be excluded. Moreover, the recent and somewhat
surprising commercial success of some electrical hybrid cars indicates that there are grounds for hope that a significant number of users is already willing to spend
some more money to contribute to save the planet from pollution, climate changes and resource depletion. In order to validate the model, a prototype of Hybrid Solar Vehicle with series structure is being developed at DIMEC, within a project funded by EU.
Abstract: A model for the optimal design of a solar hybrid vehicle is presented. The model can describe the effects of solar panels area and position, vehicle dimensions and propulsion system components on vehicle performance, weight, fuel savings and costs for different sites. 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 expected trends in costs and prices.
Abstract: After a general overview of Hybrid Power Plants (HPP) and Compressed Air Energy Storage (CAES), the authors present a thermo-economic model for the simulation and optimization of a HPP consisting of a wind turbine coupled with CAES. In the proposed scheme, during periods of excess power production, atmospheric air is compressed in a multi-stage compressor and cooled; when there is power demand, the compressed air is heated in multiple expansion stages using the stored heat and conventional thermal sources. Such plants can offer significant benefits in terms of flexibility in matching a fluctuating power demand, particularly when renewable sources, characterized by high and often unpredictable variability, are utilized. The possible advantages in terms of energy and cost savings with respect to other solutions must be carefully assessed, critically depending on performance and efficiencies of each sub-system, most of them operating in transient and off-design conditions. To this purpose, a thermodynamic model composed of several sub-systems describing wind turbine, multi-stage compressor, intercooler, aftercooler, heat recovery system, compressed air storage and turbine has been developed in Matlab/Simulink® environment. In the paper, several scenarios are compared by simulation and optimization analysis and a parametric study of the plant performance with respect to the main design variables is presented.
Abstract: The paper deals with the simulation and the optimal design of steam power plants for power generation and cogeneration. The simulation is carried out making use of a thermodynamic model of a general steam power plant with regeneration, reheating and cogeneration. Furthermore, the model analyzes the economic feasibility of the power-plant, estimating the investment and operation costs. The paper shows the results of optimization analyses performed on a case study by varying the exogenous variables (i.e. fuel price, electrical and thermal loads) evidencing the achievement of the optimal trade-off between performance and investment. The computer code is built in Matlab® and performs optimization analyses with a short computational demand.
Abstract: The paper presents a model for thermoeconomic analysis and optimal design of steam power plants, for power generation and cogeneration use. The model, including both thermodynamic and economic description of a general steam power plant with regeneration, reheating and cogeneration, can be used for simulations, parametric analysis and optimization, with mathematical programming techniques.
The variability of components performance and efficiency versus costs can be also analyzed, and optimal trade-off between costs and performace obtained by optimization analysis. Significant applications to cogeneration are presented in the paper, discussing the effects of electrical and thermal loads, fuel price and changes in technology. The computer code, built in Matlab®, allows to perform complex
optimization analyses with very limited computational time.
Abstract: The paper focuses on the simulation, analysis and control of the energy flow in a parallel hybrid electric vehicle (HEV).
HEVs operation is concerned with the on board conversion of chemical, electric and mechanic energy and its optimal control is essential in order to increase the global system efficiency.
A dynamic model is used to describe the driver-vehicle interaction for a generic transient and to simulate the vehicle driveline, the internal combustion engine (ICE) and the electric motor/generator (EM). A Genetic Algorithm has been implemented to design the rules of a fuzzy logic controller for the optimal management of the energy flow between EM and ICE, accounting for the battery state of charge (SOC) and the
route typology (urban o extra-urban cycle).
The methodology has been applied for a standard driving ECE-EUDC cycle with a significant improvement of the fuel efficiency.
Abstract: A model for the simulation of a parallel hybrid powertrain is proposed. The model is oriented to support the development of on-board energy flows control strategies to reduce both fuel consumption and emission levels and to improve electric driving range electric through optimal battery recharging cycles.
The model has been developed in Matlab/Simulink environment with a modular structure. The adoption of a mixed modelling approach, based on different classes of models ranging from black-box Neural Network to grey-box meanvalue dynamic models, allows a satisfactory accuracy with reasonable computational demand. Moreover,
Fuzzy controllers have been implemented to simulate the Driver-Vehicle interaction, the torque management strategy (i.e. the splitting between electric motor and thermal engine) and the battery recharging strategy.
Since the main goal of the research is the optimal management of on-board energy flows, an optimization procedure is underdevelopment to design the most suitable controllers. In this paper, a detailed description of the whole model is presented and the simulation results carried out for a real driving cycle are reported.
Abstract: In this paper the problem of engine thermal state detection is approached by means of measured in-cylinder pressure cycle analysis for SI engine. The proposed procedure could overcome the difficulties related with the direct cylinder wall temperature measurement which is fundamental for engine control application and cycle modeling, due to its influence on engine heat flow, emissions and friction losses.
The proposed technique is based on the treatment of measured in-cylinder pressure cycle which brings in itself most of the information concerning heat flow, engine operation and emission formation processes. The developed methodology relies on the identification of the instantaneous polytropic compression coefficient as function of crank angle and pressure data in order to estimate the occurrence of an adiabatic condition between cylinder wall and engine gas mixture during compression stroke. The crank angle position where the detected polytropic coefficient approaches the value corresponding to the local adiabatic one is
assumed as the inversion point for the net heat flux between cylinder wall and gas mixture. Under steady-state heat flow condition it is customary to accept that both gas mixture temperature and cylinder wall temperature are the same at the occurrence of the detected adiabatic condition. Therefore, the mean gas mixture temperature is computed from a simple thermodynamic relationship
as function of pressure, volume displacement and polytropic coefficient.
The technique is explained in detail, also discussing the influence of measurement errors and the computed wall temperature profiles for a large set of experimental data during steady-state engine operating condition. A preliminary numerical analysis has been conducted by modeling unsteady heat flow between cylinder wall surface, thermal boundary layer and mixture gas in order to evaluate the accuracy of the steady-state hypothesis assumed for the proposed technique.
Abstract: A procedure for the identification of emission models for the design of optimal control of spark ignition engines is presented. The procedure is based on a decomposition technique for the definition of optimal model structure with limited number of parameters.
A two step scheme has been built: in the first step the available physical models, based on a multi-zone thermodynamic model with emission sub-models, are parametrized and an intermediate model, based on Taylor approximation, is derived in order to describe the non linear influence exerted by the physical parameters; in the second step the physical parameters are modeled by means of non linear regression, taking into account the effect of operating engine variables, and the optimal parameters obtained via stepwise approach. The features of the identification technique and preliminary results over a set of more than 300
experimental data are presented.
Abstract: A model relating sensors and acturators errors to control variable values in an engine control system based on multi-dimensional maps is presented, and the complex influences of engine and control laws on the equilibrium values are outlined.
The model is utilized for the evaluation of engine fuel consumption and emissions in a driving cycle, considering stochastic effects in the control system.
Optimal control laws where stochastic behaviour is concerned are obtained by a mathematical programming approach, using an Augmented Lagrangian method.
A set of results is discussed and compared with the ones obtained by means of classical deterministic optimization., for different limits on pollutant emissions.
The stochastic approach proposed allows the reduction of performance deterioration occurring in real engine operation, and the quantification of the effects of single sensor and actuator precision.
Abstract: Second International Workshop on Hybrid and Solar Vehicles, University of Salerno, September 14, 2007
Program
Invited Lecture:
R.O.SCHAUM - SAE PRESIDENT 2007 - EVOLUTIONARY TRENDS IN POWERTRAIN TECHNOLOGY
Papers:
R.DI MARTINO, A.GIUSTINIANI, G.PETRONE, G.RIZZO, M.SORRENTINO - A PROTOTYPE OF HYBRID SOLAR VEHICLE WITH SERIES STRUCTURE
BAUER P.,PREITL ZS., GÃSPÃR P., SZABÃ Z., BOKOR J. - IMPROVED MODEL OF A SERIES HYBRID SOLAR VEHICLE
T.DONATEO, G.SERRAO, G.RIZZONI - MULTI-OBJECTIVE OPTIMIZATION OF A HEAVY-DUTY HYBRID ELECTRIC VEHICLE
I.V.ION,I.IONITA - FINDING 3D POSITION OF THE HSV MASS CENTER
V. MARANO, T. G. CHOI, Y. GUEZENNEC, G. RIZZONI, C. PANZERI, W. CHOI - OPPORTUNITIES OF PLUG-IN HYBRIDS IN SELF-SUSTAINING HOMES
T.DONATEO, F.ZECCA, D.LAFORGIA - EXPERIENCES ON HYBRID ELECTRIC VEHICLES
BAUER P., SPAGNUOLO G., BOKOR J. - A GLOBAL MAXIMUM POWER POINT TRACKING SOLUTION FOR MISMATCHED PHOTOVOLTAIC ARRAYS
M.CACCIATO, A.CONSOLI, G.SCARCELLA, G.SCELBA - ACCURATE IMPLEMENTATION OF A STATE OF CHARGE ESTIMATOR FOR HYBRID AND ELECRIC VEHICLE BATTERY PACKS
P. PUDDU, M. PADERI,F.NURZIA - SISTEMA DI PROPULSIONE IBRIDA PER VEICOLI INDUSTRIALI LEGGERI (CLASSI M3 E N1). PARTE I: IL VEICOLO IBRIDO.
P. PUDDU, M. PADERI, F.NURZIA - SISTEMA DI PROPULSIONE IBRIDA PER VEICOLI INDUSTRIALI LEGGERI (CLASSI M3 E N1). PARTE II: MISURE SPERIMENTALI SU STRADA.
A. MACOR, C. A. MICHIELAN - STUDIO DI UN SISTEMA DI TIPO HLA PER IL RECUPERO DELLâENERGIA CINETICA IN UN VEICOLO PESANTE
L. MARTELLUCCI, V. DI GIACOMO, F. TAVANI - PROGETTO MICROCAR: QUADRICICLO CON SISTEMA DI PROPULSIONE IBRIDO SERIE E SISTEMA DI ACCUMULO A SUPERCONDENSATORI
Abstract: The growth of mobility has had a positive effect on prosperity and quality of life, but its negative impact on the environment and the erosion of non-renewable resources are becoming more and more visible. As a consequence, the attention toward the sustainable mobility is rapidly increasing, spreading from specialists to final users and to public opinion. In last decade, the hybrid electric vehicles have emerged as a valid mid-term solution to reduce fuel consumption and carbon dioxide emissions. Their integration with photo-voltaic sources may
give a further contribution toward the mitigation of fossil fuels depletion, global warming and climate changes. Despite these promising perspectives, there is a certain lack of systematic research on the integration of hybrid vehicle technology with solar sources.
This Workshop is dedicated to hybrid and solar vehicles, with particular emphasis on the combined use of these two approaches. These proceedings include 13 papers, from Hungary, France, Italy, Romania, Spain, Turkey and United States. Most of the research presented is conducted in an academic context, also in cooperation with industry and research centres. The papers cover several aspects of hybrid and solar vehicles. The actual trends and the opportunities related to the integration of electric vehicles with photo-voltaic and, more
generally, with renewable sources are presented in the first paper. Five papers deal with modelling, design and control of hybrid solar vehicles, also caring for profitableness of such vehicles. Other five papers concern hybrid electric vehicles: hybridization of a small vehicle for urban transportation and of a 4WD parallel vehicle, control of super-capacitors, HEV real-time control and performance testing. Other two papers are devoted to photovoltaic sources for automotive applications, concerning MPPT modelling and power interfaces.
I would thank all the Authors for their dedication in preparing excellent technical papers, the members of Scientific Committee for their cooperation in paper review and my colleagues at the University of Salerno for their help in the Workshop organization. We acknowledge the financial and operative support of University of Salerno to this Workshop, co-sponsored by the Technical Committee âAutomotive Controlâ of International Federation of Automatic Control and by SAE Naples Section. We also recognize the significant impulse given to the studies on
hybrid solar vehicles by the European Community in supporting the Leonardo Project âEngine Conversion Systems and Their Enviromental Impactâ, with sponsorship of Automobile Club Salerno, Lombardini, Saggese and Province of Salerno.
The Workshop Chair
Gianfranco Rizzo
