Corrigendum to “Energy flow modeling and real-time control design basing on mean values for maximizing driving mileage of a fuel cell bus” [Int J Hydrogen Energy 40 (2015) 15052–66]

2016 ◽  
Vol 41 (4) ◽  
pp. 3316 ◽  
Author(s):  
Liangfei Xu ◽  
Jianqiu Li ◽  
Minggao Ouyang
Keyword(s):  
Fuel Cell ◽  
Real Time ◽  
Energy Flow ◽  
Control Design ◽  
Hydrogen Energy ◽  
Flow Modeling ◽  
Real Time Control ◽  
Mean Values ◽  
Time Control ◽  
Fuel Cell Bus

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

2009 ◽  
Vol 193 (1) ◽  
pp. 258-268 ◽  
Author(s):  
J. Hasikos ◽  
H. Sarimveis ◽  
P.L. Zervas ◽  
N.C. Markatos
Keyword(s):  
Fuel Cell ◽  
Real Time ◽  
Real Time Control ◽  
Cell Systems ◽  
Time Control ◽  
Operational Optimization

1119 Real-Time Control Design with Feedback Structure

2007 ◽  
Vol 2007.82 (0) ◽  
pp. _11-19_
Author(s):  
Tomoaki Kobayashi ◽  
Junichi Maenishi ◽  
Joe Imae ◽  
Guisheng Zhai
Keyword(s):  
Real Time ◽  
Control Design ◽  
Real Time Control ◽  
Time Control ◽  
Feedback Structure

Development of a Multiphysical 2-D Model of a PEM Fuel Cell for Real-Time Control

2018 ◽  
Vol 54 (5) ◽  
pp. 4864-4874 ◽  
Author(s):  
Daming Zhou ◽  
Fei Gao ◽  
Ahmed Al-Durra ◽  
Elena Breaz ◽  
Alexandre Ravey ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Real Time ◽  
Pem Fuel Cell ◽  
Real Time Control ◽  
Time Control ◽  
D Model

Real time control of air feed system in a PEM fuel cell by means of an adaptive neural-network

2014 ◽  
Vol 39 (29) ◽  
pp. 16750-16762 ◽  
Author(s):  
Victor M. Sanchez ◽  
Romeli Barbosa ◽  
L.G. Arriaga ◽  
Juan M. Ramirez
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Real Time ◽  
Pem Fuel Cell ◽  
Real Time Control ◽  
Feed System ◽  
Adaptive Neural Network ◽  
Time Control

Dynamic Test and Real-time Control Platform of Anode Recirculation for PEM Fuel Cell Systems

2006 ◽  
Vol 3 (3) ◽  
pp. 333-345 ◽  
Author(s):  
Cheng Bao ◽  
Kexun Zhang ◽  
Minggao Ouyang ◽  
Baolian Yi ◽  
Pingwen Ming
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Real Time ◽  
Test Bench ◽  
Future Application ◽  
Real Time Control ◽  
Cell Systems ◽  
Time Control ◽  
Injection Pump ◽  
Wide Range

Anode recirculation is essential to the pure-hydrogen proton exchange membrane fuel cell system. Keeping the pressure difference between the anode and the cathode is also important to the membrane health. In this paper, a dynamic platform was designed for the recirculation test of injection pump and real-time control of the anode pressure tracking. The test bench can work in a wide range of conditions for high- and low-pressure application. Based on the MATLAB/xPC Target environment, some S functions were written to drive the PC board for the hardware-in-loop application. Then an analytical full-order and a reduced-order model were built with good accuracy. By linearization of the nonlinear dynamic model, a linear quadratic Gaussian algorithm based on state feedback was used for set-point tracking. Moreover, an adaptive fuzzy neural network with an on-line neural network identifier was also designed to improve the control robustness. The foundation of the test bench and realization of the real-time control algorithms are meaningful to the future application in fuel cell systems.

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