Fuel Cell Power System Matlab

Matlab/Stateflow P&O and ICMPPT Implementation for PEM Fuel Cell Power System

Accepted: 22 December 2020

This paper outlines an innovative way in the assessment of proton exchange membrane fuel cell maximum power point tracking using Matlab/Stateflow implementation of variable step size version of perturb and observe and incremental conductance maximum power point tracking algorithms. In this study, the perturb and observe as well as the incremental conductance maximum power point tracking controllers have been completely implemented as Matlab/Stateflow models having as inputs: cell voltage, cell current and the variable step size; the model's output is the pulse width modulation ratio to drive the DC-DC boost converter for supplying the maximum power available from the 7kW proton exchange membrane fuel cell to a 50 resistive load. Simulation obtained results under different test scenarios prove the effectiveness of the proposed Matlab/Stateflow maximum power point tracking models that can provide accurate results and giving a strong tool to test and validate maximum power point tracking controllers.

Figure: System Model & Simulation Flow for Fuel Cell Power System Matlab

1. Introduction

The world faces several difficult challenges, especially with the increase in demographic growth, which could reach nine billion in the early fifties of this century. This population increase is accompanied by a significant increase in the percentage of electricity consumption, which is one of the most important requirements of life. Fossil fuels have been, to this day, the main source of electric power generation.

Figure: System Model & Simulation Flow for Fuel Cell Power System Matlab

However, These Sources Cause An Imbalance In The

environmental balance due to the exacerbation of global warming. Therefore, it has become imperative to search for alternative renewable energy sources and find new ways to use energy more efficiently. It can be said that this era is the era of renewable energies .

Figure: System Model & Simulation Flow for Fuel Cell Power System Matlab

Technological development is accelerating daily, which has positively affected the development and improvement of the efficiency of renewable energy sources. As a result, its contributions to the production of electrical energy have increased. For example, in some Western European countries, its contribution has reached 20-30% of the total energy consumption. Therefore, with the achievement of such results in the production of clean energy, the research has begun to raise the production capacity to cover most of our demand in the medium and long term. However, renewable energy sources such as solar and wind power suffer from a drawback that must be taken into account, which is their intermittent nature. In fact, we may record a lack or absence of power produced that may coincide with the moment of the high demand for it from the loads. As a result, it is necessary to apply modern technologies to ensure the storage of energy in order to exploit it in times of inability to supply energy from these sources [1, 2].

Figure: System Model & Simulation Flow for Fuel Cell Power System Matlab

In this context, batteries emerge as a primary energy storage solution, as they are always found in all systems that include PV and wind energy sources, both for stand-alone and grid- connected systems. However, energy storage using batteries does not allow for long periods of power supply. Hydrogen and fuel cells play an important role in that fuel cells are highly efficient sources and hydrogen is a well-known energy carrier.

Hydrogen can be produced in several ways as it is one of the main elements in all components of materials, and perhaps the use of electrolysis of water is one of the most important ways to produce hydrogen by using photovoltaic system to feed the electrolyzer. The hydrogen produced is stored for later use during low production periods by fuel cells [1, 3, 4].

Fuel cells can be defined as a thermodynamic system that converts the energy contained in hydrogen and oxygen into electrical energy under oxidation and reduction reactions.

These reactions are located in two electrodes; one named anode is the center of the hydrogen (H2) oxidation reaction, while the second named the cathode is the center of oxygen (O2) reduction reaction. The anode and cathode is separated by a dense electrolyte, a material that ideally is an ionic conductor but electronic insulator. Unlike batteries, fuel cells are power sources that use hydrogen and oxygen as reactants and work as long as these gases are supplied to the both electrodes.

Although there are several types of fuel cells on the market,

They Offer Common Features As Shown Below :

• Efficiency, usually between 40 and 60%. • Low environmental impact. • Modularity due to their flexibility.

• Location: can be placed almost anywhere, without restrictions due to their size. • Noiseless as consequence of non-existing of moving parts.

• A variety of fuels can be used for fuel cells such as hydrogen and methane.

1

Among the six types of fuel cells available in the market, the proton exchange membrane fuel cell (PEMFC) appears as one of the most widely used types in several applications especially in the stationary and portable. This is due to its distinct characteristics such as: high power density, low weight, pollutant free operation and no noise. On the other hand, a relevant aspect is their low temperature of operation (typically 60–80℃), which allows fast starting times .

The output power of the PEM fuel cell shows a non-linear power-current (P-I) characteristic that its maximum power point (MPP) differs with changes in many parameters such as: temperature, partial gases pressures, membrane water content, density current, reactants humidity level, gas speed and stoichiometry. Therefore, the operation of a fuel cell in applications with varying load demands without power electronics controlled by a maximum power point tracking (MPPT) algorithm to adjust the operating point on the V/I curve corresponding to the actual power demand is impossible .

Over the past decade, the technique of the maximum power point tracking (MPPT) controller for fuel cell power systems has remarkable development [16, 17]. Perturb and Observation

(P&O) And Incremental Conductance (Ic) Mppts Are

considered the most used algorithms for their simplicity and

Easy Implementation:

Karami et al. introduced a new hybrid technique of MPPT to extract the MPP, which includes the use of the conventional P&O driving a buck converter with a fuel flow rate controller. From results of simulation, the hybrid technique proposed extracts with high efficiency the MPP from the FC at various fuel flow rates. In addition, this proposed technique avoids overheating, allowing the formation of excess water, thereby protecting the fuel cell from deterioration of the mechanical structure and membrane.

Kiruthiga et al. designed a P&O based MPPT for solar photovoltaic system along with interleaved boost converter and boost converter is employed for fuel cell. The hybrid photovoltaic-Fuel cell based system has been studied under various load conditions for the mitigation of voltage sag in the power systems using a PI controller. The proposed approach has been simulated using Matlab/Simulink environment considering different load conditions.

Harrag et al. presented a variable step size P&O MPPT technique with adaptive duty cycle step for fuel cell energy system. This study aims to demonstrate the efficiency of the proposed MPPT controller by using a boost converter connected to a PEMFC source. The simulation results presented are an analysis and comparison with the classical fixed step size P&O. In terms of transient, steady-state and dynamic responses presented in this paper, the proposed MPPT proves its high efficiency and performance.

Harrag and Messalti introduced a technique of MPPT controller for the PEMFC system called variable step size IC MPPT. To validate this technique, it applied to PEMFC of 7kW used to feed resistive load through DC-DC boost converter. Under variable operating conditions of pressures and temperatures, the whole system has been simulated. The results obtained confirm the high performances of the proposed MPPT compared to the fixed step size IC MPPT concerning dynamic and static performances leading to a gross optimization of the system output power.

Harrag and Bahri proposed a neural network variable step size IC-based MPPT controller for the PEMFC power system. Using the Matlab/Simulink environment, the effectiveness of the proposed technique has been successfully demonstrated.

The

superiority of the technique of neural network IC-based variable step size MPPT compared to the conventional fixed step size in terms of dynamic and static performances. In addition, the proposed MPPT technique has the advantage of better reducing voltage and current ripple, which positively affects the PEMFC in terms of improving efficiency and increasing its life.

To extract the maximum output power from the PEMFC source, Harrag and Messalti introduced a variable step size fuzzy based MPPT controller. To prove the technique proposed, the authors compared it with conventional fixed step size IC, the variable step size IC, the fuzzy auto-scaled variable step size IC. The simulation results show that the proposed MPPT controller has high performances in many aspects such as dynamic and static performances compared to other techniques introduced.

In this work, an innovative way in the assessment of PEM fuel cell MPPT using Matlab/Stateflow implementation of variable step size version of P&O and IC MPPTs is presented and investigated. The P&O as well as the IC MPPTs are completely implemented as Matlab/Stateflow models having as inputs: cell voltage, cell current and the variable step size; the model's output is the PWM ratio used to drive the DC-DC boost converter for supplying the maximum power available from the used 7kW PEM fuel cell to a 50 resistive load.

Simulation results using different test scenarios show the effectiveness of the proposed Matlab/Stateflow MPPT models that can provide accurate results and giving a strong tool to test and validate MPPTs.

This paper is presented as follows. In Section 2, the PEMFC modelling, the used MPPT algorithms and the DC/DC boost converter are detailed; while Section 3 presents the P&O as well as the IC MPPTs. The proposed Matlab/Stateflow P&O and IC MPPTs models are detailed in Section 4. The results of simulation and discussions are presented in Section 5. Section 6 present the essential conclusions of our study.

2. Pem Fuel Cell Modeling

PEMFC is an energy conversion system (Figure 1), which generates electricity and heat without any polluting emissions to the environment .

Figure 1. Pemfc Operating Strategy

PEM fuel cell operates under the following reactions: At anode: The oxidation of hydrogen occurs as follow:

2

At anode: The reduction of oxygen occurs as follow:

(3)

The output voltage can be presented by the below following

(4)

where, Enernst is the Nernst voltage approximated by empirical

(5)

where, T𝐹𝐶 is the temperature; PO2 and PH2 are the oxygen and hydrogen pressure respectively. The activation voltage loss Vact is approximated by

(6)

where, iFC is the cell current; CO2 is the oxygen’s concentration; and δi (i = 1 to 4) are parametric coefficients for each cell model.

(7)

where, iFC is the cell current; and RM and RC are the membrane and contact resistances, respectively. The concentration voltage loss Vconc is defined by:

(8)

where, iFC is the cell current; A is the is cell active area; Imax is the maximum current density; and b is the concentration loss constant.

3. Mppt Algorithms

In the control of PEMFC, there are three control strategies: the first relates to the output power when the presence of variation in the load requirements. The second control strategies is relates to the parameters of reactant gases (hydrogen and gases), as well as temperature and water management. While the third is relates to the maximum power and efficiency of the PEMFC. In this work, we concentrate on the control of the PEMFC output power.

From the overall output voltage of PEMFC, we can be seen that voltage source shows a highly nonlinear reliance to operating conditions such as partial pressure of reactant gases, temperature, membrane water content and electric current.

Due to this nonlinear, multi-parameter dependent conduct, precisely controlled conditions must be guaranteed for proper operation using an MPPT technique to operate around an optimal operating point corresponding to the maximum power generated by the PEMFC source. This is possible by adapting constantly the PWM ratio of the boost converter that acts as adaptive impedance.

3.1 P&O Mppt

P&O MPPT includes perturbation of the operating current based on a comparison of the generated power to track the MPP. At specified PEMFC current, the required power is the solution of the nonlinear equation given by dP/dI = 0 .

3.2 Ic Mppt

The IC algorithm concentrates directly on power changes. The both current and voltage of the PEMFC are employed to calculate the conductance and the incremental conductance . The main equations of this technique are presented as

4. Proposed Matlab Stateflow Mppts

Matlab/Stateflow is employed in conjunction with Simulink, and it is a graphical design and development tool for control and supervisory logic. It provides obvious and brief descriptions of complex system behaviour using finite state machine theory, flow diagram notations, and state-transition diagrams. In this study, the P&O as well as the IC MPPTs discussed in section 3 have been completely implemented using Matlab/Stateflow models. The implemented models have as inputs: cell voltage, cell current and the variable step size; while the model's output is the PWM ratio to drive the DC-DC boost converter for supplying the maximum power available from the PEM fuel cell to the load.

5. Simulation Results

In this par, we will analyze the main results of simulation by using Matlab/Simulink environment. The whole system implemented in Matlab/Simulink is composed of 7 kW PEMFC used to supply a 50  resistive load through a boost converter controlled using the implemented stateflow MPPT controllers. Table 1 and Table 2 give the PEMFC and the DC/DC boost converter parameters respectively.

Deployment In Out-Of-Position Situations

D. Bendjaballah1, A. Bouchoucha1, M. L. Sahli1,2* and J-C. Gelin2

Abstract

Side-impact collisions represent the second greatest cause of fatality in motor vehicle accidents. Side-impact airbags have been installed in recent model year vehicle due to its effectiveness in reducing passengers’ injuries and fatality rates. In meeting these requirements, simulations of folding and deploying airbags are very useful and are widely used. The paper presents a simulation method for the deploying airbags using three materials in different working conditions. Finite element analysis is primarily used to evaluate this concept. In these simulations, the gas flow is described by the conservation laws of mass, momentum, and energy. The numerical results indicate that the FE method in this paper is capable of capturing airbag deploying process accurately.

Figure: System Model & Simulation Flow for Ansys Airbag Injury Simulation

Keywords: Airbag simulations, Out-of-position, Crash, Modeling, Out-of-position

Background

The passive safety of cars has become a very high prior- ity issue for the automotive industry. Today, there are not only one or two airbags in a car; certain models have ten times more than that. With the increasing usage of airbags, the number of accidents where the airbag itself can cause an injury to the occupant also increases

(Augenstein Et Al. 2003; Gabauer And Gabler 2010;

Audrey et al. 2011). As is well known, safety belts are also now devices designed to provide protection to the users of vehicles during crash events, minimizing the loads necessary to adapt their movement to the move- ment of the car (Freesmeier and Butler 1999; Schmitt et al. 1997). In general, the seat belt is designed to restrain the occupant in the vehicle and prevent the

Occupant From Having Harsh Contacts With Interior

surfaces of the vehicles. The airbag acts to cushion any impact with vehicle structure and has positive internal pressure, which can exert distributed restraining forces over the head and face. As a safety component of auto- mobile, an airbag decreases occupants’ injury likelihood effectively in case of an accident (Ruff et al. 2007). These safety elements can reduce the death rates on the roads, and its protection effects have been widely approved (Crandall et al. 2001; Teru and Ishikawa 2003). With computational tools such as finite element methods designed for dynamic contact problems, crashworthiness simulations can now be used with reliable accuracy to evaluate occupant protection in various collision condi- tions with safety metric/parameters such as acceleration, head injury criteria, intrusion distance, intrusion vel- ocity, and neck forces (neck injury risk or whiplash).

Figure: System Model & Simulation Flow for Ansys Airbag Injury Simulation

Thus, new types of airbag products are being developed to handle different collision scenarios.

Become Standard Equipment On Most New Passenger

vehicles (Braver and Kyrychenko 2004; Teng et al. 2007; Yoganandan et al. 2007). The airbag cushion is com- posed of a woven fabric which is rapidly inflated during a car crash. The airbag dissipates the passenger’s kinetic energy thereby reducing injury through biaxial stretching of the fabric bag and escaping gas through vents. There- fore, the performance of the airbag is greatly influenced by the mechanical properties of the fabric. Generally, air bags are designed to deploy in a crash that is equivalent to a vehicle crashing into a solid wall at 8 to 14 mph.

Figure: System Model & Simulation Flow for Ansys Airbag Injury Simulation

Air bags most often deploy when a vehicle collides with another vehicle or with a solid object like a tree. There are various types of airbags: frontal, side-impact, and curtain airbags. In general, the passenger side airbags are usually larger than the driver airbags (see Fig. 1).

Figure: System Model & Simulation Flow for Ansys Airbag Injury Simulation

Besançon, France

© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Figure: System Model & Simulation Flow for Ansys Airbag Injury Simulation

Bendjaballah et al. International Journal of Mechanical

Doi 10.1186/S40712-016-0070-2

Extensive studies have shown that the airbag deploy- ment in load cases consists of two occupant loading phases: a punch-out effect where the airbag bursts out of its container with the airbag and airbag module cover accelerating towards the occupant and a second loading phase during which the airbag is taking on its deployed shape and volume (membrane-loading effect). Bankdak et al. (2002) developed an experimental airbag test system to study airbag-occupant interactions during close proximity deployment. The results provided insight for simulating the effect of inflation energy and mass flow on target response. Bedard et al. (2002) found that while left-side (driver-side) impacts accounted for only 13.5% of all crashes, the fatality rate among these

Crashes Was 68.3% In Comparison To Front Impact

(48.3%), right-side impact (31.3%), and rear impact (38.4%). These studies underscore the importance of oc- cupant safety during side-impact collisions. In the last years, the current market requested to reduce the time and cost airbag development. In order to achieve this result, virtual simulations play an important role since they allow to minimize the number of experimental tests (Pei et al. 2013; Cao et al. 2014). Several simulation models of airbag were established (Wang et al. 2007). It is feasible to optimize the parameters of airbag deploy- ment using simulation technology. Experimental and numerical studies have quantified injury risks to close- proximity occupants from deploying side airbags. These studies have focused on the prevention of the most ad- verse effects of airbag deployment (Duma et al. 2003).

Other studies have proposed airbag characteristics to minimize particular biomechanical responses (Haland and Pipkorn 1996). In a more recent study, Marklund and Nilsson (2003) compared deformation patterns with experimental data as well as the computational costs associated with three different airbag deployment simu- lation methods; they concluded that the SPH method is relatively inexpensive and produces incremental deform- ation patterns that compare most closely to the experi- mental results. The process of inflation of an airbag is one of the determining factors in saving lives. The duration from the initial impact of the crash to the full inflation of an airbag is about 40 ms, and during this time, the airbag goes from being in a folded state to a fully inflated state, with a high internal pressure. After achieving this state, the airbag begins to deflate, thus providing a nice cushion for the body impacting it.

Ideally, the person in the crash should come into contact with the airbag at this time. In the present study, a large volume passenger side airbag model is developed to handle different collision scenarios. The main aim is evaluate the performance of deploying of passenger side airbag using finite element methods (FEM).

Materials

The tensile specimens were made in different airbags (P: Peugeot, R: Renault, and VW: Volkswagen) with a length of 200 mm long and a width of 40 mm. Table 1 shows the mechanical properties of the airbag.

Tensile Tests

To determine the mechanical properties of the material of airbag used in the test pieces, tensile tests were performed on Lloyd EZ20 universal testing machine in Constantine. These tests were conducted using rect- angular samples. The axial force and axial displacement acquired during a test are converted into stress and the strain in order to be used for the fabric material model.

The continuous recording of the stress-strain data was performed during both the load and unload phases. A minimum of five samples were made in order to check the repeatability of the measurements. All the data was collected by using a PC-based data acquisition system and analyzed by commercial software. The picture frame test device that is made for this study is shown in Fig. 2.

Fig. 1 a Frontal and side airbags. b Oblique view of facet occupant model in sitting posture following airbag deployment (Lim et al. 2014)

0.150

Bendjaballah et al. International Journal of Mechanical and Materials Engineering (2017) 12:12

Page 2 Of 9

Figure 3 shows the stress-strain relationship of the airbag sample under axial tensile loads. The results are showing a linear increase in extension with the increas- ing stresses. This is an expected output and it confirms with the theoretical behavior of a sample subjected to tensile stress. The rupture strain values for different airbags (R/P/VW) were 0.322, 0.441, and 0.472, respect- ively. The measured elastic parameters (i.e., Young’s modulus E and initial yield strength) and Poisson’s ratio are summarized in Table 2. The tensile tests of the woven fabrics can show differences on mechanical prop- erties because woven fabrics can resist in-plane shear loads once the yarn lock-up angle has been reached. The differences of material property on material direction can affect the shape of fully deployed bag (see Fig. 3b).

Theoretical Background

Numerical simulations of airbags use very complex and techniques such as an orthotropic model to identify the mechanical behaviors during the airbag inflation and the fluid mechanics (gas flow) to describe the inflator gas flow (pressure gradient) and improve the representation of the pressures within the airbag. To model the airbag as an orthotropic model, three material constants have to be provided. Assuming a plane stress condition, the

Ð1Þ

where σ is the normal stress and τ is the shear stress, the subscript refers to the principal material directions, i.e., the fill and warp directions. Also, ε and γ are the strain components. The material elastic constants Qij are

Ð2Þ

where E1 and E2 are the Young’s modulus in the fill and wrap directions and G12 is the shear modulus of the fabric material. νij is the Poisson ratio of the material.

The gas exerts a pressure load on the airbag causing it to expand. This expansion puts the airbag under tensile stress lowering the expansion rate. In this study, heat conduction and heat transfer is not taken into account.

Fig. 2 A photograph of Lloyd EZ20 universal testing Fig. 3 Stress versus strain using Lloyd EZ20 machine for a three different airbags at 0° and 90° and b VW airbag test specimens at

Different Angles

Table 2 Physical and mechanical properties of the airbag

Page 3 Of 9

In the deployment of an airbag, an inflator supplies high velocity gas into an airbag causing it to expand rapidly. The gas inside the airbag is assumed to be ideal, to be of constant entropy, and to satisfy the equation of state:

Ð3Þ

Here p, ρ, and e are respectively the pressure, density, and specific internal energy, and γ is the ratio of the heat capacities of the gas. The gas flow is described by the conservation laws for mass, momentum, and energy that

Ð4Þ

here, V is a volume, A is the boundary of this volume,

N Is The Normal Vector Along The Surface A, And U

denotes the velocity vector in the volume. Applying Bernoulli’s equation in the case of an ideal gas with

Ð5Þ

Here, the subscript ex denotes quantities at the throat of the tube. Furthermore u, p, and ρ denote the quan- tities inside that part of the tube that is supplying mass.

Materials And Boundary Conditions

The airbag system mainly consists of three parts: the airbag itself, the inflator unit, and the crash sensor or diagnostic unit. Thus, to study the behavior of the airbag using FE simulations, we need to have an FE model of the airbag in the folded position. A FE model of the airbag was used to simulate the test condition as shown in Fig. 5. LS-DYNA® material model FABRIC (MAT_34) is used to simulate the airbag material. It is a variation of the layered orthotropic material model. Additionally, in the LS-DYNA® material model, fabric leakage can be accounted for. However, for this CAB material, the leak- age is almost negligible and therefore no leakage is specified. The mechanical properties can be determined from the physical test. Typical material properties for airbag fabrics are taken as given in Chawla et al. (2004a) (Table 3). These properties are used to simulate inflation process of airbag (see Table 1). The car dashboard is modeled as the rectangular thin plate using a MAT_RI-

Gid Material, And The Degrees Of Freedom Are Con-

strained in all the directions. The similar properties of thermoplastic polymer are assigned for contact purposes. The porosity of the fabric is assumed zero. The nitro- gen gas is taken for inflating the airbag. Properties of nitrogen gas and initial bag conditions are shown in Table 4. The example on which we perform the study is a typical passenger side airbag. The geometric de- tails have been measured from a commercially avail- able airbag. The initial state of the airbag is a closed rectangular whose sides are to be finished to 482 × 635 mm2 and is shown in Fig. 4.

Table 3 Material properties of airbag and rigid plate used in FE

–

Table 4 Initial values used for FE simulation of the swelling of

3.33 × 10−4

Fig. 4 The initial airbag geometry in the form of a rectangular Bendjaballah et al. International Journal of Mechanical and Materials Engineering (2017) 12:12

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