Fuel Cell Dc Dc Converter Matlab

accredited by DGHE (DIKTI), Decree No: 51/Dikti/Kep/2010

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Received February 14th, 2011; Revised April 15th, 2011; Accepted April 23th, 2011

Converter

Ahmad Saudi Samosir*1, Tole Sutikno2, Abdul Halim Mohd Yatim3 Jln. Prof Sumantri Brojonegoro, Bandar Lampung, 35145, Indonesia, Ph./Fax: +62 721-701609/702767 Jln. Prof. Soepomo, Janturan, Yogyakarta 55164, Indonesia, Ph./Fax: +62 274-379418/381523 FKE-UTM, Skudai, 81310 Johor, Malaysia, Ph./Fax: +607-5535200/5566272

Abstrak

Sel bahan bakar adalah sumber energi alternatif baru yang memiliki prospek yang baik untuk pembangkitan energi listrik terdistribusi dan aplikasi kendaraan listrik. Namun, sel bahan bakar mempunyai respon yang lambat disebabkan respon elektrodinamik dan termodinakmik internal. Untuk mengoptimasi kinerja dari sistem sel bahan bakar, diperlukan sebuah konverter DC ke DC untul sel bahan bakar dengan pengendali yang sesuai yang dapat meregulasi aliran daya dan secara otomatik mengatur tegangan keluaran konverter. Paper ini mengusulkan sebuah teknik kendali baru untuk konverter daya DC ke DC sel bahan bakar. Dilakukan desain dari metoda kontrol yang diusulkan. Sebuah pendekatan baru untuk sintesa pengendali konverter berbasis teori pengendali evolusi dinamik di paparkan. Pada paper ini didiskusikan contoh sintesa pengendali konverter DC ke DC tipe boost. Kinerja dari pengendali evolusi dinamik yang diusulkan disimulasikan menggunakan Matlab-Simulink untuk kondisi perubahan beban.

Hasil simulasi menunjukkan bahwa teknik yang diusulkan adalah berkemampuan untuk mengendalikan konverter DC ke DC sel bahan bakar. Kata kunci: boost, konverter DC ke DC, pengendali evolusi dinamik, sel bahan bakar

Abstract

Fuel cells are new alternative energy resource that has a great promise for distributed generation and electric vehicle application. However, fuel cells have a slow response due to their slow internal electromechanical and thermodynamic response. To optimize the fuel cell system performance, a fuel cell DC-DC converter with an appropriate controller which can regulate the power flow and automatically adjust the converter output voltage is needed. This paper proposes a new control technique for fuel cell DC-DC power converter. Design of the proposed control method for fuel cell DC-DC power converter is provided.

A new approach for converter controllers synthesis based on dynamic evolution control theory is presented. In this paper, synthesis example of boost DC-DC converter is discussed. Performance of the proposed dynamic evolution control under step load variation condition is simulated under Matlab-Simulink environment. Simulation results show that the proposed techniques are capable for controlling fuel cell DC- DC converter.

Keywords: boost, DC-DC Converter, dynamic evolution control, fuel cell

1. Introduction

Due to economic problems and depletion of world fossil fuel supplies, renewable energy development for power generation received many attentions. Fuel cells are one of the alternative energy resources that have recently attracted a great deal of attention. Fuel cell is a device that converts the chemical energy of a fuel directly to electrical energy. Fuel cell has higher energy storage capability and is a clean energy source . Fuel Cell can serve as an emergency energy source during long-term power outages. Fuel cells can be used as a portable power system. The fuel cells are use in every aspect because of their clean and efficient way of supplying electric power. The fuel cells are used in the standalone purposes at homes, hospitals, industries and now are use in numerous vehicles. Compared with any other energy

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production technology, the fuel cells have a wider range of applications. Their potential application ranges from systems of a few watts to megawatts. Among the various types of fuel cell, Proton exchange membrane (PEM) fuel cell is the most popular. PEM fuel cells show great promise for use in distributed generation electric vehicle applications. Compared with other distributed generation technologies, such as wind and photovoltaic (PV) generation, PEM fuel cells have the advantage that they can be placed anywhere within the distribution system, without geographic limitations, to achieve the best performance. In electric vehicles application, the increased desire for vehicles with less emission has made PEM fuel cells attractive for vehicular applications since they are essentially no pollutants emission and have high-power density and quick start .

PEM fuel cells are good energy sources to provide reliable power at steady state, but they have a slow response. This is mainly due to their slow internal electrochemical and thermodynamic responses . In order to optimize the fuel cell system performance, a fuel cell DC-DC converters is needed to develop for various applications. Another important issue is the need for appropriate control of fuel cell DC-DC converter which can regulate the power flow and automatically adjust output voltage of the converters to avoid rapid load voltage variations, which may lead to a reduction of the power quality of the system.

Based on the above issues, this paper proposes an approach for fuel cell DC-DC converter controller using dynamic evolution control. The controllers synthesis based on dynamic evolution control theory is presented. The dynamic evolution control exploits the non- linearity and time-varying properties of the system to make it a superior controller. A comprehensive simulation analysis was conducted to verify the performance of the controller.

The steady-state and transient response of the system is investigated. 2. Dynamic Evolution Controller Design for Fuel Cell DC-DC Converter

2.1 Fuel Cell Dc-Dc Converter System Model

A valid model for fuel cell and DC-DC Converter is introduced in . Circuit of this model is illustrated in Figure 1. The circuits consist of a fuel cell generator, the boost DC-DC converter and load. Using boost DC-DC converter, the provided output voltage to the load can be regulated to the required voltage. The output voltage can be controlled by change the duty cycle (d) of boost DC-DC converter.

Figure 1. Circuit model of Fuel Cell and DC-DC Converter The circuit of system in Figure 1 can be analyzed based on the boost converter switch condition. When the switch is closed, the diode is reversed bias. Figure 2 shows the equivalent

Dynamic Evolution Control for Fuel Cell DC-DC Converter (Ahmad Saudi Samosir)

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circuit of system when the switch is closed. Kirchhoff’s voltage law around the path containing

(1)

When the switch is opened, the inductor current cannot change instantly, so the diode becomes forward biased to provide a path for inductor current. Figure 3 shows the equivalent circuit of system when the switch is opened. Assuming that the output voltage VO is a constant,

The Switch Is Opened

The average inductor voltage must be zero for periodic operation. Expressing the average inductor voltage over one switching period,

(4)

The average duty cycle, D, is defined as the time relationship that the switch is on relative to the total switching period. From (4) at steady state it can be verified that the gain ratio between output and input

(5)

2.2 Synthesis of Dynamic Evolution Controller for Fuel Cell DC-DC Converter The dynamic evolution control theory has been described in reference and . In dynamic evolution control, the dynamic characteristic of system is forced to make evolution by following an evolution path. With the selected evolution path is an exponential function as shown in Figure 4, the value of the dynamic characteristic of system will decrease exponentially

To Zero By Equation

.

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where, Y is the dynamic characteristic of system, YO is the initial value of Y, and m is a design parameter specifying the rate of evolution.

Figure 4. Dynamic Evolution Path

The dynamic evolution function of this controller can be written as

(7)

Synthesis process is done to obtain the control law that guarantees the dynamic characteristic of system decrease to zero by following the evolution path. In case fuel cell boost DC-DC converter, this control law corresponds to the duty cycle equation of the converter. This duty cycle equation α(vO,Vg,iL), represents α as a function of the state vO, Vg and iL. The duty cycle equation α(vO,Vg,iL) is obtained by analyzed and substituted the dynamic equation of the converter system into the dynamic evolution function (7).

Based on the state-space average model, the dynamics voltage and current of the fuel

(9)

where L is the inductance, C the capacitance, R the load resistance,

O

v the output voltage, and d the duty cycle, respectively. Rearranging (6), the output voltage of converter can be written as: .

(10)

The dynamic evolution synthesis of the controller begins by defining the state error function (Y). In power electronic application, Y can be selected as a function of error voltage or error current. Refer to , with the selected Y is a linear function of error voltage as (11)

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.

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).

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.

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).

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.

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)

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

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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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