2018 AIChE Annual Meeting
(184i) Discrete-Time Nonlinear Observer-Based Globally Linearizing Control of a PEM Fuel Cell
energy demand and continuous depletion of fossil fuels have forced us to search
and utilize the alternate energy sources [1]. Accordingly, among many renewable
energy devices, the fuel cell is chosen as one of right candidate for clean and
sustainable future development. Again, amongst various fuel cells, the proton
exchange membrane (PEM) fuel cell is preferred mostly due to its
low operating temperature, high power density, fast start-up and, more
importantly zero emission and less operating noise [2]. Because of these
notable advantages, its practical applications are widely found in transport,
residential and, stationary and portable applications [3]. With this, we have
started working towards modelling and automation of the PEM fuel cell for its
stable operation.
fuel cell blooming, there are several steady state and linearized models reported.
But these models may not produce satisfactory response if the operating conditions
slightly deviate from its base condition. Later, a few nonlinear dynamic
control oriented models have been developed [4,5]. Again, regarding control of
PEM fuel cell, apart from conventional controllers, nonlinear control works have
been found in open literature [6-9].
utilizes the reactants, namely hydrogen and oxygen to convert the chemical
energy into electrical energy using platinum as a catalyst. As per the energy
requirement (load demand), the reactants flow are to be altered accordingly to
avoid irregularities (i.e., cell breathing issue, membrane damage and voltage
degradation) during the operation. As the PEM fuel cell is inherited with
fluid-heat-electrochemistry interacting complex nonlinear dynamics, we need to
use the nonlinear controller for its efficient operation.
discrete-time nonlinear globally linearizing control (GLC) system is developed
(Fig. 1) for the PEM fuel cell [10]. The GLC control structure includes a
nonlinear transformer, an adaptive state observer (ASO) and a dual-loop PI
controller. The ASO is formulated based on shortcut model (i.e., only two
measured component balance equations) to estimate the unknown state information
as per the GLC requirement. To reduce the resulting process/model mismatch in
the observer, a polynomial equation based dynamic tuning parameter is used. Finally,
the hybrid GLC-ASO shows its supremacy in tracking the set point as well as
rejecting the disturbances compared to the conventional dual-loop PI
controller.
PEM fuel cell; modelling and simulation; adaptive state observer; discrete-time
globally linearizing controller; conventional dual-loop PI controller
1. GLC-ASO control system of PEMFC.
transformation on residential energy demand. Applied Energy, 143, pp. 228-237,
2015.
Dicks A. Fuel cell systems explained, John Wiley & Sons Ltd, West Sussex,
England, 2001.
applications in cars-bringing the future closer. Journal of Power Sources, 71, 36-8,
1998.
R. and Sammes N. System level lumped parameter dynamic modelling of PEM fuel
cell. Journal of Power Sources, 133, pp. 188-204, 2004.
Han M. Nonlinear robust control of proton exchange membrane fuel cell by state
feedback exact linearization. Journal of Power Sources, 194, pp. 338-48, 2009.
and Wack M. Robust control of the PEM fuel cell air-feed system via sub-optimal
second order sliding mode. Applied Energy, 104, 945-57, 2013.
Stefanopoulou A.G. and Peng H. Control of fuel cell breathing, IEEE Control
Systems Magazine, 24, pp. 30-46, 2004.
R. Real-time implementation of a constrained MPC for efficient airflow control
in a PEM fuel cell, IEEE Transaction on Industrial Electrononics, 57,
pp. 1892-1905, 2010.
Feedback-linearization-based nonlinear control for PEM Fuel cells. IEEE Transaction
on Energy Conversion, 23, 179-90, 2008.
estimator-based nonlinear control of a PEM fuel cell. IEEE Transaction on
Control Systems Technology, accepted, 2017.