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Ivan Arsie

eProLab 
DIIN - Dept. of Industrial Engineering
University of Salerno
84084 Fisciano (Salerno), Italy
Ph. +39 089 964080 FAX +39 089 964037
iarsie@unisa.it
Ivan Arsie

Degree cum laude in Mechanical Engineering from University of Salerno (1995). Ph.D. in "Thermo-Mechanical Systems Engineering" from University of Naples "Federico II" (1999).
Since 1995, he has been involved in the research field of ‘Energy Conversion Systems’ and ‘Internal Combustion Engines’ at University of Salerno, where he is currently Associate Professor of ‘Energy Conversion Systems’ and lecturer of ‘Fluid Machines’ and ‘Experiments and Control on Internal Combustion Engines’ .
In 1997, he took lessons in controls applied to internal combustion engines under prof. Elbert Hendricks at the Institute of Automation of the Technical University of Denmark, where he worked on the observer based control technology.
In 2000, he was at the Measurement and Control Laboratory of the Swiss Federal Institute of Technology where he worked under prof. Lino Guzzella on modeling and control of variable valve timing engines.
Author of over 100 papers on modelling, control and optimization of SI and Diesel engines, hybrid electric vehicles and energy systems.
SAE member. Member of the IFAC TC on Automotive Control. Teaching staff member of the Ph.D. in Mechanical Eng., Univ.of Salerno.
He has served as reviewer for “IEEE Transactions on Control Systems Technology”, “Control Engineering Practice”, “Mechanical Systems and Signal Processing”, “International Journal of Mechanical Sciences”, “ASME Journal of Engineering for Gas Turbines and Power, the biennal SAE-ICE conference and the triannual IFAC World Congress.
In 2004 received the Best Paper Award in the area "Control of Vehicle Propulsion & Motorcycles" at the AVEC '04 Symposium on Advance Vehicle Control Technologies (HAN - University, Arnhem, The Netherlands, August 23-27).
In 2005 received the SAE Award for Excellence in Oral Presentation at the SAE World Congress and Exposition (Detroit, April 11-15 ).
In 2010 received the Award "Energy and Mobility" at the H2Roma 2010 show.

Journal articles

2010
2009
2008
2007
2006
2005
I Arsie, M Graziosi, C Pianese, G Rizzo, M Sorrentino (2005)  Control Strategy Optimization for Hybrid Electric Vehicles via Provisional Load Estimate   REVIEW OF AUTOMOTIVE ENGINEERING, Society of Automotive Engineers of Japan (JSAE), ISSN 1349-4724 26: 3. 341-348  
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.
Notes: Best Paper Award at AVEC04, Arnhem (NL).
2004
2003
2002
2001
2000
I Arsie, C Pianese, G Rizzo, R Flora, G Serra (2000)  Development and Validation of a Model for Mechanical Efficiency in a Spark Ignition Engine   SAE 1999 TRANSACTIONS - JOURNAL OF ENGINES (SAE Paper 1999-01-0905) 108-3: 1312-1323  
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.
Notes:
1999
1997
I Arsie, M Gambino, C Pianese, G Rizzo (1997)  Development and Validation of Hierarchical Models for the Design of Engine Control Strategies,   "Meccanica", Kluwer Academic Publisher. 32: 397-408  
Abstract:
Notes: Presented at the International Conference on "Control and Diagnostic in Automotive Applications", Genova 3-4/10/1996,

Book chapters

2010
2008
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