Energy Conversion Systems and Internal Combustion Engines
University of Salerno
Cesare Pianese was born in Naples, Italy, on December 22, 1961 and received the graduate degree cum laude in Mechanical Engineering in 1987 from the University of Naples Federico II (Department of Mechanical Engineering for Energetic). He received the graduate diploma with honours in fluid dynamics from von Karman Institute (Belgium) (1989/90) and the Research Doctorate degree from the University of Naples in the 1992. From november 1991 to october 1993 was at Istituto Motori of the Italian National Council of Research (CNR) as researcher fellow, in January 1994 received a two years Post-Doctorate fellowship from the Department of Mechanical Engineering at University of Salerno. He worked as researcher at: Fiat Research Center (1987/88); Imperial College (1988); von Karman Institute (1990/1991) and DIME-University of Naples (1991).
University Career and Teaching Activity From 1994 to 2000 Cesare Pianese has served as lecturer in Internal Combustion Engine at the College of Engineering of the University of Salerno, since March 2001 is professor of Energy Systems and Internal Combustion Engines. From November 1999 to February 2001 Cesare Pianese has served as assistant professor and from March 2001 to December 2003 as associate professor of FluidMachinery at the Department of Mechanical Engineering of the University of Salerno. Since January 2004 is full professor (tenured) at the University of Salerno. He’s member of the Mechanical Engineering Doctorate commission at the University of Salerno
Professional experience Cesare Pianese has developed his main research activity in the framework of project funded by industries, regional and national organizations. He’s also took the scientific responsibility of research project with industries and cooperates with the Center for Automotive Research of The Ohio State University. He was member of the group for the development of the Campania’s Regional Energy Program and had developed the energy program for ground transportation. Cesare Pianese participates in the Italian Hydrogen and Fuel Cell Technological Platform as expert for the applications of fuel cells to railway systems. He's representative of the University of Salerno in the New European Research Grouping on Fuel Cells and Hydrogen - N.ERGHY. He was appointed evaluator of project proposals to the European VII Framework Program.
Scientific work He has been visiting professor at Swiss Federal Institute of Technology (2001), Zurich and professor at the Summer School on Automotive Control–Modelling and Control of SI Engine hosted by the Laboratoire d’Automatique of Grenoble CNRS–INPG (2002). Has served as member of Ph.D. defence committee at the Technical University of Denmark and at the University of Belfort (Fr). From March 1 to July 31, 2006 was visiting (Honda Partnership Program) at the Center for Automotive Research of The Ohio State University (USA), his main reserach topic was on fuel cell systems and hybrid vehicles. Cesare Pianese has served as reviewer for: IEEE/ASME Int. Conference on Advanced Intelligent Mechatronic, Int. Conference Control and Diagnostics in Automotive Applications, international journal on Inverse Problems in Engineering-Taylor&Francis Group and he’s referee for the ASME and SAE Journals , the Transaction of the Institute of Measurement and Control-Arnold Publisher and Information Science-Elsevier. He also works actively as conference organizer and session chairman of international conferences.
Research interests During his professional and academic activity Cesare Pianese has published more than 100 scientific papers in the field of automotive internal combustion engines, alternative propulsion systems, non-conventional energy systems and computational fluid dynamics:
Automotive engines Electronic control of spark ignition engines: Development of models and computational codes for the optimal design of engine control strategies. Simulation and experimental analysis of dynamic processes. Numerical and experimental methods for On-Board-Diagnosis of automotive engine control system (OBD). Modelling of Diesel engines: Development of numerical models for the simulation of combustion in High Speed Direct Injection Common-Rail Multi-Jet Diesel engines. Emissions: Development of models for the simulation of pollutant emissions from SI and Diesel automotive engines.
Fuel Cells Modeling and experiments on PEM fuel cells for water and thermal management, dynamics of stacks and auxliaries, electronic control. Dynamic modeling of SOFC for thermal analysis and control. Application of fuel cells and hydrogen for propulsion systems and electric energy production.
Alternative propulsion systems Modelling of hybrid propulsion systems and on-board energy flow management for hybrid vehicles. Application of fuel cells to ground transportation systems.
Research projects Cesare Pianese participated to the EU funded (FP6) HyRail-Hydrogen Railway Applications International Lighthouse Project (Hydrogen and Fuel cells propulsion for the railway sector). He's the coordinator of the EU funded project (FP7 - FCHJU) D-CODE-(DC/DC COnverter-based Diagnostics for PEM systems) and is involved into the EU funded projects GENIUS (GEneric diagNosis InstrUment for SOFC Systems Project) and DESIGN (Degradation Signatures identification for stack operation diagnostics). Other projects are funded by the Italian Ministry of Industry (BITRAS - bio-ethanol for automotive engines), by the Italian Ministry of Economy (Industria 2015, AMICO - automation and monitoring of fuel consumption of marine engines). The main activity with industrial partners refers to engine modeling and control (Magneti Marelli, Élasis, Istituto Motori CNR), turbogas modeling (Snam), energy management (Telecom Italia).
Abstract: The exploitation of an SOFC-system model to define and test control and energy management strategies is presented. Such a work is motivated by the increasing interest paid to SOFC technology by industries and governments due to its highly appealing potentialities in terms of energy savings, fuel flexibility, cogeneration, low-pollution and low-noise operation.
The core part of the model is the SOFC stack, surrounded by a number of auxiliary devices, i.e. air compressor, regulating pressure valves, heat exchangers, pre-reformer and post-burner. Due to the slow thermal dynamics of SOFCs, a set of three lumped-capacity models describes the dynamic response of fuel cell and heat exchangers to any operation change.
The dynamic model was used to develop low-level control strategies aimed at guaranteeing targeted performance while keeping stack temperature derivative within safe limits to reduce stack degradation due to thermal stresses. Control strategies for both cold-start and warmed-up operations were implemented by combining feedforward and feedback approaches. Particularly, the main cold-start control action relies on the precise regulation of methane flow towards anode and post-burner via by-pass valves; this strategy is combined with a cathode air-flow adjustment to have a tight control of both stack temperature gradient and warm-up time. Results are presented to show the potentialities of the proposed model-based approach to: (i) serve as a support to control strategies development and (ii) solve the trade-off between fast SOFC cold-start and avoidance of thermal-stress caused damages.
Abstract: It has been well documented that water production in PEM fuel cells occurs in discrete locations, resulting
in the formation and growth of discrete droplets on the gas diffusion layer (GDL) surface within the gas
flow channels (GFCs). This research uses a simulated fuel cell GFC with three transparent walls in conjunction
with a high speed fluorescence photometry system to capture videos of dynamically deforming
droplets. Such videos clearly show that the droplets undergo oscillatory deformation patterns. Although
many authors have previously investigated the air flow induced droplet detachment, none of them have
studied these oscillatory modes. The novelty of this work is to process and analyze the recorded videos
to gather information on the droplets induced oscillation. Plots are formulated to indicate the dominant
horizontal and vertical deformation frequency components over the range of sizes of droplets from formation
to detachment. The system is also used to characterize droplet detachment size at a variety of
channel air velocities. A simplified model to explain the droplet oscillation mechanism is provided as
well.
Abstract: This paper deals with on-board energy management of hybrid fuel cell vehicles equipped with a polymer electrolyte membrane fuel cell (FC) stack and a battery pack as main power source and hybridizing device, respectively. A multilevel architecture was conceived to separately manage on-board energy flows and mutual interaction between FC auxiliaries and powertrain components. At the highest-level, a splitting index map was designed to share the power requested by the driver among the fuel cell stack and batteries as function of traction power demand and batteries' state of charge. At the intermediate-level are defined the set points at which to operate the fuel cell system (FCS) to achieve maximum efficiency. Then, at the low-level, specific control strategies are adopted to reach the set point as addressed by the intermediate-level. To guarantee the accuracy required for control strategy development, a mixed modeling approach was followed to simulate vehicle powertrain, FCS, electrochemistry, and water management. The simulations were carried out for a 60 kW FC powertrain running under severe transient maneuvers. The results show the potentialities of the proposed approach for energy management optimization, control, and diagnostics analyses.
Abstract: In this paper, the use of an adaptive technique aimed at controlling a polymeric electrolyte
membrane fuel cell is introduced. It is demonstrated that a hill climbing-based
method, acting on the compressor speed and/or the cathode back-pressure valve, allows
to better take into account the effect of exogenous variables on stack performance.
Particularly, the proposed technique has proven to perform better than classical
feedforward/feedback approaches when well known aging mechanisms deteriorate cell
efficiency. Numerical results based on experimentally derived models confirm the potential
of the proposed control method and its intrinsic reliability
Abstract: 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.
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: 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 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: 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.
Dr. Cesare Pianese Full Professor
