Diego Feroldi

Description of PEM Fuel Cells System

This chapter provides a description of polymer electrolyte membrane (PEM) fuel cell-based systems and different modeling approaches. First, it shows the structure of a single cell, the advantages and disadvantages of this type of fuel cell, the expressions of the generated voltage and the efficiency, and the generic structure of a generation system based on PEM fuel cell. Second, the chapter provides a review of the principal models presented in the literature to describe the behavior of the system. Different types of PEM fuel cell models are presented, focusing on dynamic models suitable for control purposes. Particularly, this chapter describes in detail the dynamic model used as a base to represent the system in the subsequent chapters of the book. Then, the described model is used to study the optimal operation of a fuel cell at different loads, showing the benefits of an optimal operation in terms of hydrogen reduction and greater peak power.

Fault Diagnosis and Fault Tolerant Control of PEM Fuel Cell Systems

The energy generation systems based on fuel cells are complex since they involve thermal, fluidic and electrochemical phenomena. Moreover, they need a set of auxiliary elements such as valves, compressor, sensors, regulators, etc. to make the fuel cell work at the pre-established optimal operating point. For these reasons, they are vulnerable to faults that can cause the stop or the permanent damage of the fuel cell. Therefore, it is useful to use systematic techniques, like the recent methods of fault-tolerant control (FTC), to guarantee the safe operation of the fuel cell systems. The Fault Diagnosis of PEM fuel cell systems using a model-based methodology is addressed in Sect. 2.2, whereas the FTC based on Model Predictive Control is addressed in Sect. 2.3). This chapter is complementary to Chap. 3, showing that the proposed control architecture with two actuators adds FTC capabilities to the fuel cell systems.

Adaptive predictive robust control for fuel cells hybrid vehicles

The transient behavior of a Polymer Electrolyte Membrane Fuel Cell System (PEMFCS) under an efficient Adaptive Predictive Control with Robust Filter (APCWRF) is analyzed. This control scheme is tested to evaluate its performance when sudden changes in the load occur. It is produced by the demands of the electric motor of a hybrid vehicle, powered by a PEMFC and a supercapacitor bank to fulfil Standard Driving Cycles. The objective of the proposed advanced strategy is to control the oxygen excess ratio in the cathode to improve the system efficiency and to ensure a safe operation for the PEM. Several results through a simulation environment are presented. They are useful for showing the potentiality of the APCWRF for the proposed exigent scenarios.

Hydrogen production based on bio-ethanol and solar energy for feeding PEM fuel cells

This work focuses on the preliminary study of an integrated hybrid system to produce hydrogen from bio-ethanol using solar energy as an auxiliary power source. It is analyzed for mobile applications, particularly, for a system that can use an on-board bio-ethanol processor. The solar power is used for promoting the reforming reaction for hydrogen production to feed a PEM fuel cell. This new concept increases the fuel economy because it avoids burning a part of bio-ethanol for producing the reforming reaction. A dynamic model of this integrated system with the control structure is presented. It allows to test an energy management strategy (EMS) to best satisfy the power demand of the fuel cell. The simulation results are carried out to illustrate the applicability and effectiveness of this new proposed hybrid system.

Dynamic Modeling and Control of a Fuel Cell Hybrid Vehicle with Onboard Fuel Processor

Abstract The transient behavior of a Fuel Processor System to produce Hydrogen from bio-ethanol with high performance, coupled with a Proton Exchange Membrane Fuel Cell is modeled. The Ethanol Processor is based on a previous steady state design, optimized to work with maximum efficiency around 10 kW of rated power. From the dynamic rigorous model, a linearized model is identified to apply a systematic procedure to synthesize the control structure. The Fuel Cell System is then hybridized with supercapacitors as auxiliary power source, to lower the overall consumption of hydrogen, hence of bio-ethanol too. The entire vehicle is tested using standard driving cycles, widely utilized in related literature and to measure pollutant emissions. The overall behavior reaches the expectations and is capable of fulfilling the requirements of urban and highway scenarios, and also suggests the possibility of resizing the components to improve fuel economy.

Identification of PEM fuel cells based on support vector regression and orthonormal bases

Polymer Electrolyte Membrane Fuel Cells (PEMFC) are efficient devices that convert the chemical energy of the reactants in electricity. In this type of fuel cells, the performance of the air supply system is fundamental to improve their efficiency. An accurate mathematical model representing the air filling dynamics for a wide range of operating points is then necessary for control design and analysis. In this paper, a new Wiener model identification method based on Support Vector (SV) Regression and orthonormal bases is introduced and used to estimate a nonlinear dynamical model for the air supply system of a laboratory PEMFC from experimental data. The method is experimentally validated using a PEMFC system based on a ZBT® 8-cell stack with Nafion 115® membrane electrode assemblies.

Sizing and energy management for fuel cell hybrid vehicles with supercapacitors

The sizing and energy management for fuel cell hybrid vehicles with supercapacitors is addressed in this work. The hybridization with high specific energy elements significantly improves the advantages of fuel cells, specially in automotive applications where the load fluctuates considerably. The sizing is based on conductibility requirements while the energy management is based on the knowledge of the fuel cell efficiency map, the state of charge os supercapacitors, and the power constraints to preserve the lifetime. In order to adjust and validate the proposed methodologies, standard driving cycles and long-term stochastic driving cycles are used. The long-term stochastic driving cycles are generated from several standard driving cycles using a Markov model, which is also introduced in this paper. The proposed methodologies show a good performance both in terms of efficiency and reference tracking with very different driving situations.

Experimental model for a DMC-based control applied to a PEM fuel cell

This paper addresses the control of an experimental PEM fuel cell system. The fuel cell station is composed by an ElectroChem® 7-cell stack with Nafion 115® membrane electrodes assemblies (MEAs) and the auxiliary equipment. The control problem is focussed on the air supply in the cathode. The main objectives are to regulate the stack voltage and the cathode oxygen excess ratio. The manipulated variables are the compressor motor voltage and the command current of a proportional valve located at the cathode outlet. A linear and dynamic model at a given operational point is obtained via step tests, which is used in the context of a centralized multivariable Dynamic Matrix Control (DMC).

Advanced Control Strategies for the Oxygen in the Cathode

This chapter presents two advanced control strategies based on model predictive control to control the oxygen level in the cathode of a proton exchange membrane (PEM) fuel cell system. The objectives are to achieve a better efficiency and to maintain the necessary level of the oxygen in the cathode to prevent short circuit and membrane damage. First, a methodology of control based on dynamic matrix control (DMC) is proposed. This strategy includes a stationary and dynamic study of the advantages of using a regulating valve for the cathode outlet flow in combination with the compressor motor voltage as manipulated variables in a PEM fuel cell system. The influence of this input variable is exploited by implementing a predictive control strategy based on DMC, using these manipulated variables. The objectives of this control strategy are to regulate both the fuel cell voltage and oxygen excess ratio in the cathode, and thus, to improve the system performance. Second, a methodology of control based on adaptive predictive control with robust filter (APCWRF) is proposed. The APCWRF is designed for controlling the compressor motor voltage. Because of the wide working range the algorithm is improved with three different zones supported by three nominal linear models.

Fuel Cell Hybrid Systems

The control of fuel cell systems was studied in Chaps. 3, 6 analyzing the system composed by the fuel cell stack and its auxiliary subsystems (e.g., compressor, valves, etc.), with the following objectives: achieve high efficiency, reduce the hydrogen consumption, improve the dynamic behavior and guarantee its safe operation. We continue in this chapter and Chap. 8 with the study of fuel cell-based systems approaching the fuel cell hybrid systems with some energy storage. Hybridization has important advantages in fuel cell hybrid vehicles (FCHV), a fuel cell application that is central in this book. Therefore, the process of designing a hybrid system, or methodology of design, is addressed in this chapter. We concentrate our attention on FCHVs because this application is particularly attractive, although some general aspects studied in this chapter also apply to other applications such as stand-alone residential PEM fuel cell power systems.