Alicia Arce

Real-Time Implementation of a Constrained MPC for Efficient Airflow Control in a PEM Fuel Cell

Fuel cells represent an area of great industrial interest due to the possibility to generate clean energy for stationary and automotive applications. It is clear that the proper performance of these devices is closely related to the kind of control that is used; therefore, a study of improved control alternatives is fully justified. The air-supply control is widely used to guarantee safety and to achieve a high performance. This paper deals with this control loop, proposing and comparing two control objectives aimed at satisfying the oxygen starvation avoidance criterion and the maximum efficiency criterion, respectively. The control architecture is based on a constrained explicit model predictive control (MPC) law suitable for real-time implementation due to its low computational demands. The proposed controller is implemented and evaluated on a 1.2-kW polymer electrolyte membrane or proton exchange membrane fuel-cell test bench, thus obtaining real data which show that the maximum efficiency criterion does not conflict with the starvation avoidance criterion and allows system performance improvements of up to 3.46%. Moreover, experimental results utilizing the explicit MPC approach also show improved transient responses compared to those of the manufacturers control law.

Constrained predictive control strategies for PEM fuel cells

This paper presents some results in advanced control of PEM fuel cells. The work analyzes the use of control strategies that try to fulfill different operational objectives. A preliminary study of PEM fuel requirements leads to three different control criteria: tracking of a desired output voltage, avoidance of starvation and maximization of efficiency. These criteria can be achieved by the design of different control algorithms based on generalized predictive control (GPC), extended with the consideration of measurable disturbances. This controller can also consider two constraints: one physical limit such as compressor voltage, and one operational limit which is introduced in order to avoid starvation: oxygen excess ratio

Hybrid model predictive control of a two-generator power plant integrating photovoltaic panels and a fuel cell

This paper describes the application of hybrid modeling control techniques to a two-generator power system connected to the grid. The plant consists of a solar field and a secondary power source formed by an electrolyzer, hydrogen tank and fuel cell stack. The system is inherently hybrid as it combines both continuous and hybrid dynamics, since it can operate in four distinct modes, depending on the power circuit configuration and the fuel cell stack state. Firstly, a mixed-logical-dynamical (MLD) description of the system is obtained. A hybrid receding horizon finite-time optimal controller based on on-line multiparametric programming techniques is then tuned. Finally, the effectiveness of such a control design is shown through the simulation results.

Constrained explicit predictive control strategies for PEM fuel cell systems

This paper presents the development of an explicit predictive control strategy for a stand-alone PEM (polymer electrolyte membrane) fuel cell. This fuel cell can be considered as a good benchmark since it is representative of the state of the art of PEM technology and is used by many research groups. The experiments are performed on a detailed nonlinear simulator of a fuel cell which has been validated experimentally. In order to achieve real-time implementation of the control strategy, the predictive control algorithm must be computed in a explicit way because of the sampling time of this system, which is in the order of milliseconds. The work shows the development and simulation results of the constrained explicit predictive controller that reduces the computational effort needed.

Model Predictive Control for power management in hybrid fuel cell vehicles

Fuel Cell Hybrid Electric Vehicles (FCHEV) are being investigated in many research and development programs motivated by the urgent need for more fuel-efficient vehicles that produce fewer harmful emissions. Hybridization can greatly benefit fuel cell technology. There are many potential advantages such as the improvement of transient power demand, the ability of regenerative braking and the opportunities for optimization of the vehicle efficiency. The coordination among the various power sources requires a high level of control in the vehicle.

An Integrated Framework for Distributed Model Predictive Control of Large-Scale Power Networks

This work proposes a novel integrated framework in order to model, simulate, and optimize potentially large-scale and complex networks, fusing an extended network modeling methodology and distributed model predictive control structures based on Lagrange multipliers. In concrete, a revised network modeling formulation is utilized, allowing extended networking capabilities with a novel, generic, and compact way of organizing network topology information. This optimization framework offers a number of very convenient features: distribution of the overall network control effort among the local agents; no overall network knowledge required; the introduction/elimination of a node only affects to its neighbors. Thus, this integrated framework offers a powerful and easy-to-use mathematical tool in order to analyze and manage a wide variety of todays large-scale network. Specifically, this methodology is applied here to a power network to show the benefit of the work developed.

Predictive Control for battery performance improvement in hybrid PEM fuel cell vehicles

This paper presents a hybrid control for PEM fuel cell vehicle. The main goals of this controller are the improvement of battery performance taking into account operational modes such as charge control, continuous discharge and step discharge, and also including fuel cell switching on-off constraints. The problem proposed, which has been successfully implemented, implies handling with continuous and discrete variables and constraints. Regarding the control strategy, the controller tracks motor power demand and keeps batteries close to the state of discharge, which is adequately chosen in order to obtain the most efficient performance. Moreover, nonlinear and linear models of the vehicle have been developed and particularized for a laboratory prototype in order to allow future real implementation of the hybrid control design. Finally, the simulation results are illustrated in several figures which depict the control performance for different driving cycle and initial conditions.

Design and Experimental Validation of an Explicit MPC Controller for Regulating Temperature in PEM Fuel Cell Systems

Abstract This paper proposes a temperature controller for PEM fuel cell systems with an air blower as thermal circuit. The objective of this controller is to maintain the stack temperature over a given set-point which is obtained from the results of a real-time optimization algorithm with the goal of minimizing the stack degradation and maximizing the global efficiency. An Explicit MPC is proposed to deal with this control problem which presents delays, the critical sampling time, constraints and disturbances. The simulation results show good performance of the controller which accurately tracks the temperature reference over the overall range of operating conditions. Furthermore, the controller is implemented in real-time on a PEM fuel cell test-bench which is installed in the Fuel Cell Laboratory at the University of Seville.

Water Management in PEM Fuel Cells: Controllability Analysis and Steady-state Optimization for Temperature Control

Abstract This paper presents a controllability study of the water management inside anode channel by regulating the stack temperature for PEM fuel cell systems with dead-ended anode. Moreover, this work includes the design of a steady-state target optimizer which calculates the temperature set-point profiles that minimize the stack degradation and the hydrogen leaks. The control architecture is successfully simulated and the results show promising performance.

A risk-based strategy for power system optimization

This paper introduces a risk-based method to optimize the planning of power systems. This works uses the formulation of Energy Hub previously presented in the literature, as an interface among energy producers, consumers and the transportation infrastructure. Risk factors such as demands, operating and maintenance cost or power cost are considered in the optimization of the system. Mitigation actions reduce the risk impacts that may affect the system. A model predictive control approach is used to optimize the demand satisfaction and to determine the set of mitigation actions to be executed. An example of a hybrid storage system is considered. In particular, the architecture incorporates a wind generator, a Combined Heat and Power Plant (CHPP) and an intermediate hydrogen storage. A set of possible risks has been identified in the system in order to select the mitigation actions that reduce the impacts of these risks. Different simulations are executed to illustrated the benefits of the proposal.