Operational optimization and real-time control of fuel-cell systems

Hasikos, J; Sarimveis, H; Zervas, PL; Markatos, NC

dc.contributor.author	Hasikos, J	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Zervas, PL	en
dc.contributor.author	Markatos, NC	en
dc.date.accessioned	2014-03-01T01:31:35Z
dc.date.available	2014-03-01T01:31:35Z
dc.date.issued	2009	en
dc.identifier.issn	0378-7753	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19833
dc.subject	Fuel cells	en
dc.subject	Hydrogen	en
dc.subject	Meta-modeling	en
dc.subject	Model predictive control	en
dc.subject	Neural networks	en
dc.subject	Optimization	en
dc.subject.classification	Electrochemistry	en
dc.subject.classification	Energy & Fuels	en
dc.subject.other	Closed-loop	en
dc.subject.other	Controlled variables	en
dc.subject.other	Fuel cell system	en
dc.subject.other	Integrated optimization	en
dc.subject.other	Look up table	en
dc.subject.other	Meta model	en
dc.subject.other	Meta-modeling	en
dc.subject.other	Nonlinear programming problem	en
dc.subject.other	Operational optimization	en
dc.subject.other	Operational range	en
dc.subject.other	Optimal controls	en
dc.subject.other	Optimal values	en
dc.subject.other	Optimization and control	en
dc.subject.other	Power demands	en
dc.subject.other	Proton exchange membranes	en
dc.subject.other	Radial basis function neural networks	en
dc.subject.other	Simulation model	en
dc.subject.other	Stationary power generation	en
dc.subject.other	Steady-state values	en
dc.subject.other	System response	en
dc.subject.other	System variables	en
dc.subject.other	Cell membranes	en
dc.subject.other	Electrochemistry	en
dc.subject.other	Fuel cells	en
dc.subject.other	Hydrogen	en
dc.subject.other	Linear programming	en
dc.subject.other	Linearization	en
dc.subject.other	Natural language processing systems	en
dc.subject.other	Neural networks	en
dc.subject.other	Optimal control systems	en
dc.subject.other	Optimization	en
dc.subject.other	Predictive control systems	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	Real time control	en
dc.subject.other	Simulators	en
dc.subject.other	Table lookup	en
dc.subject.other	Model predictive control	en
dc.title	Operational optimization and real-time control of fuel-cell systems	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.jpowsour.2009.01.048	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.jpowsour.2009.01.048	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	Fuel cells is a rapidly evolving technology with applications in many industries including transportation, and both portable and stationary power generation. The viability, efficiency and robustness of fuel-cell systems depend strongly on optimization and control of their operation. This paper presents the development of an integrated optimization and control tool for Proton Exchange Membrane Fuel-Cell (PEMFC) systems. Using a detailed simulation model, a database is generated first, which contains steady-state values of the manipulated and controlled variables over the full operational range of the fuel-cell system. In a second step, the database is utilized for producing Radial Basis Function (RBF) neural network ""meta-models"". In the third step, a Non-Linear Programming Problem (NLP) is formulated, that takes into account the constraints and limitations of the system and minimizes the consumption of hydrogen, for a given value of power demand. Based on the formulation and solution of the NLP problem, a look-up table is developed, containing the optimal values of the system variables for any possible value of power demand. In the last step, a Model Predictive Control (MPC) methodology is designed, for the optimal control of the system response to successive sep-point changes of power demand. The efficiency of the produced MPC system is illustrated through a number of simulations, which show that a successful dynamic closed-loop behaviour can be achieved, while at the same time the consumption of hydrogen is minimized. © 2009 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Journal of Power Sources	en
dc.identifier.doi	10.1016/j.jpowsour.2009.01.048	en
dc.identifier.isi	ISI:000267561400038	en
dc.identifier.volume	193	en
dc.identifier.issue	1	en
dc.identifier.spage	258	en
dc.identifier.epage	268	en