Static and dynamic modeling of a diesel fuel processing unit for polymer electrolyte fuel cell supply

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Abstract

This article introduces the energetic macroscopic representation (EMR) as approach for the dynamic modeling of a diesel fuel processing unit. The EMR is the first step toward model-based control structure development. The autothermal fuel processing system containing: heat exchanger, reformer, desulfurization, water gas shift, preferential oxidation and condensation is divided into a multitude simple subblocks. Each subblock describes an elementary step of the fuel conversion, several of these blocks may occur in a single module. Calculations are carried out using two basic principles: mass and energy balances. For model-based control development, it is imperative that the model represents dynamic behavior, therefore temperature and pressure dynamics are taken into account in the model. It is shown that the model is capable to predict the stationary behavior of the entire fuel processing unit correctly by comparison with given data. Predictions regarding reformer heat up, temperature and pressure dynamics are also provided.

Introduction

Fuel cell systems have a high potential to become important energy converters for the supply of electric energy. They show higher efficiency potential than conventional energy converters, because they are capable to convert chemical energy directly into electrical energy without the intermediate step of thermal energy production. Fuel cell systems are considered for portable, stationary and mobile applications in the range of some watt until several mega-watt [1].

The most considered way to run a fuel cell system is to supply it with hydrogen [2]. Hydrogen can be produced from conventional energy sources by fuel processing and from renewable energies for example by using renewable produced electricity for an electrolysis process. In the case of centralized production, hydrogen has to be supplied to the fuel cell system. Especially for mobile applications a hydrogen infrastructure is needed. However, the low volumetric power density and the high fugacity of hydrogen as well as the costs connected to the development of a new infrastructure are decelerating the advance of fuel cells in mobile applications at the moment. However, the need of a new infrastructure could be avoided temporarily by producing the hydrogen needed for the fuel cell for mobile applications on board of the vehicle from conventional fuels like diesel. This is done in the fuel processing unit. Such an approach is especially interesting in areas where a fuel cell system could be used for auxiliary power supply, keeping a conventional engine for vehicle propulsion.

The work on fuel processing units for fuel cell systems can be divided into following different approaches. Firstly, it can be classified regarding the reforming process: steam reforming [3], [4], [5], [6], [7], partial oxidation [8], [9] and autothermal reforming [10], [11], [12], [13], [14]. For steam reforming water is added to the fuel, the reaction is endothermic. For partial oxidation air is added and the reaction is exothermic. For autothermal reforming a mixture of water and air is added, often this is done to obtain an autothermal reaction. Another approach is to qualify the work regarding the fuel that is transformed into hydrogen rich gas. The most considered fuels are methanol [4], [5], [10], [15], ethanol [12], gasoline [16], [17], [18], [19], diesel [6], [7], [13], [20], [21] and jet fuel [11], [22]. Furthermore, there is a difference between the considered system boundaries, taking into account only the reformer [8], [17], [18], [22] or the entire fuel processing unit [5], [7], [19], [20], [23], [24]. The kind of fuel cell operated with the reformat has considerable influence on the system complexity, fuel processors for solid oxide fuel cells [21], [25] are less complex than those for proton exchange membrane fuel cells [4], [6], [11], [15], [18], [19], [23], [26]. The use of the system, stationary [5], [6], [23] or mobile [21], [22], [27], and the aimed power output of the connected fuel cell system from the kilowatt range [11], [18], [19], [28] to the mega-watt range [6], [23] defines the system. Finally, the work is either aimed at experimental work [11], [18], [19], [26] or at modeling [7], [15], [16], [24]. For modeled fuel processor units it has furthermore to be distinguished if the modeling is stationary [3], [6], [7], [29] or if it takes into account dynamic aspects [5], [15], [16], [23]. The most mentioned modeling software used is Matlab/Simulink® [15], [23], [29]. As there are only few operational fuel processor units available for validation, not all models are validated [6], [7], [22], [23], [24] or they are validated with other theoretic results [3], [15], [19], [29].

The article presented here considers a complete fuel processing unit, including the elements: heat exchanger, autothermal reformer, desulfurisation, water gas shift and preferential oxidation. The system is operated with commercial diesel to run a PEM fuel cell of 25 kW power output. A system model is introduced, taking into account as well stationary behavior as the pressure and a simplified temperature dynamic of the system. The system model is developed for a running system, start-up and shut-down procedures are not considered. The temperature of the gases inside the system is not imposed, but calculated from the respective energy balance, taking into account a heat exchange with the surrounding and a cooling. The model is validated with values provided by N-GHY (stationary). The behavior with regard to temperature and pressure development is predicted. The temperature dynamic of a heat up of the autothermal reformer is validated with experimental results. The goal of the model is to facilitate a model-based control structure development.

Fuel cell systems as well as fuel reforming units are complex multidomain systems incorporating hydraulic, thermal, chemical and electrical aspects [6]. The development of a well-adapted control for such complex is not evident [29]. Nowadays, the control of such a multidomain system is mainly based on experience. Even if it is possible to run complex systems with an experience based control the use of a methodical approach is advantageous. With the help of a defined procedure it is possible to develop reproducible control structures that can be qualified and that can be developed without the need of a large experience. One approach that can lead to such a procedure is the model-based control, Romani [30] introduced such an approach for the control of the air supply in a combined reformer fuel cell system.

In this article a methodology for inversion based control development addressing complex multidomain systems is presented. First the energetic macroscopic representation (EMR), a graphic modeling tool, is introduced as well as its inversion which leads to the maximum control structure (MCS). Some considerations regarding EMR are made to apply it to thermo-pneumatic problems. In the third section a fuel processing unit capable to convert commercial diesel fuel into hydrogen with a high purity (compatible for PEM supply) is introduced. The fourth section introduces the modeling of this reformer. At first, the stationary behavior is considered, thereafter dynamic aspects regarding temperature and pressure are taken into consideration. In the fourth section modeled results are presented and partially compared to dimensioning and measured values. The article ends with conclusions and perspectives.

Section snippets

Energetic macroscopic representation (EMR)

EMR is a causal graphic modeling methodology developed for model-based control development. Unlike Bond Graph, EMR permits to develop the system control structure graphically from the model without the need to extract the systems' transfer function [31]. EMR has been developed since the beginning of the year 2000 for electromechanical application [32]. As the approach is graphic the form and the color of the respective elements have an implication, that is why the colors of the elements are

Elements of fuel processing unit and their use

The fuel processing unit converts a commercial fuel, diesel in this case, to a hydrogen rich gas mixture. Therefore, several steps have to be taken, either to convert the molecules to obtain hydrogen, or to clean the gas. This fuel processing unit is foreseen to be used in combination with a polymer electrolyte fuel cell (PEM) system. PEMs are sensitive regarding pollutions, especially pollutions of carbon monoxide and sulfur. In the following the elements of a diesel fuel processing unit and

Composition of diesel fuel

Diesel is a mixture of different hydrocarbons and other components. To model a fuel processing unit it is advantageous to deal with diesel as one single molecule, Specchia [13] is using cetane as diesel representative molecule. Here a virtual molecule with rational fractions for the different elements is used. The diesel fuel can therefore be written as CnHmOpSq with the coefficients: n = 13.4, m = 25.05, p = 0.031, q = 0.009 with a molar mass MDiesel = 186.243 kg kmol−1 and the lower heating value HuDiesel

Results

The presented model of a fuel processor unit is intended to be used for a model-based control structure development using MCS. The results of the stationary behavior are compared to dimensioning data provided by the system supplier N-GHY. The dimensioning data are based on a confidential tool that has been already used and validated successfully for other fuel processor systems developed by N-GHY. A heat up of the combined reformer–heat exchanger unit is compared to measurement data provided by

Conclusion and perspectives

This article introduces a methodology to model a fuel processing unit capable to transform commercial diesel fuel into hydrogen rich gas with low sulfur and carbon monoxide fractions so it can be used to supply PEM fuel cells. The basic elements needed for the fuel processing are introduced and their behavior is explained in more detail for stationary application. It can be seen that already the stationary behavior is quite complex to calculate. Still, the goal of this approach is to develop a

Acknowledgment

ANR the French National Research Agency through its hydrogen program PAN-H is acknowledged for its financial support. This work has been done in the frame of the French national project GAPPAC, gathering N-GHY, Airbus and Nexter as industrial partners and LMFA, Armines, IFFI, INRETS LTN and FCLAB as research institutes.

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