Bldc Motor Control Matlab Simulink

International Journal of Electrical and Computer Engineering (IJECE)

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Journal homepage: http://iaescore.com/journals/index.php/IJECE Modelling of sensored speed control of BLDC motor using

Accepted Apr 9, 2019

Recent developments in the field of magnetic materials and power electronics, along with the availability of cheap powerful processors, have increased the adoption of brushless direct current (BLDC) motors for various applications, such as in home appliances as well as in automotive, aerospace, and medical industries. The wide adoption of this motor is due to its many advantages over other types of motors, such as high efficiency, high dynamic response, long operating life, relatively quiet operation, and higher speed ranges. This paper presents a simulation of digital sensor control of permanent magnet BLDC motor speed using the MATLAB/SIMULINK environment. A closed loop speed control was developed, and different tests were conducted to evaluate the validity of the control algorithms. Results confirm the satisfactory operation of the proposed control algorithms.

Speed Control

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Introduction

Brushless direct current (BLDC) variable speed drives are increasingly applied in many new industrial applications. Recent developments in power electronics and semiconductor technology have led to their widespread use . This type of motor is now more popular in applications, such as for electric vehicles, due to its energy-efficent consumption . Furthermore, the BLDC motor has many advantages over the induction motor and brushed DC motor, including better efficiency, power factor, less maintenance, longer life, and less rotor inertia. BLDC motor is also easier to control with its trapezoidal configuration.

This study utilizes a three-phase BLDC motor with trapezoidal back EMF . The brushes and commutators have been eliminated, and the windings are connected to the control circuits. Commutation is done electronically instead of using brushes . Because such motors have no brushes, they need a solid state commutation circuit in order to supply the stator windings according to the rotor position . Rotor position can be obtained by either a shaft encoder or, more often, by Hall Effect sensors .

The dynamic features and digital control of the BLDC motor furthered its wide utilization in different high and low power applications, compared with other types of motors. Moreover, these motors became one of the major components used to develop 3D printers due to its compatibility and easy integration with used digital controllers , which are digitally controlled through power electronic converters integrated with high speed microcontroller. The use of such devices enabled an easy adaption of BLDC motors in 3D printers and Internet of Things (IoT) devices . Nowadays, real-time connection technologies, either at the residential or industrial level, is considered as the primary technology that established a wide range of IoT applications, such as smart homes and automated industrial applications .

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A review of the variety of microcontroller-based applications shows the rapid developments in science and technology. The advantages in developing embedded microcontrollers in many industrial applications realized remote monitoring and using wireless/wired techniques of different systems .

A BLDC motor drive can be considered a digitally controlled drive system. Therefore, sensors are implemented to realize control and drive system . This is mainly required for rotor position. The commutation process was accomplished using a digitally controlled inverter based on Hall-effect sensors signals. The BLDC motors are characterized by their rectangular current, which needs six discrete rotor positions .

Permanent magnets create the rotor flux, and the energized stator windings create electromagnet poles. By using the appropriate sequence to supply the stator phases, a rotating field on the stator is created and maintained. According to the rotor position, the phase windings are switched in a sequence to obtain the rotation . The speed of a motor can be controlled using open loop control. However, accurate speed control is necessary in various applications, which can only be achieved by closed loop speed control .

The torque produced in a BLDC motor with trapezoidal back electromotive force (BEMF) is not constant due to torque ripples that appear on the delivered output torque. These torque ripples are an issue that could highly affect the BLDC drive system performance [7, 11]. In this paper, digital closed loop speed control is used by implementing control algorithms in MATLAB/SIMULINK .

The performance of a BLDC motor control based on a single sensor for position detection is presented . The proposed design, which replaces the three conventional sensors with a single one, reduces cost and complexity. In addition, the proposed drive system will be powered directly from the PV system, based on the designed high voltage-gain DC-DC converter. MATLAB/SIMULINK results showed a proper operation of BLDC motor for variable ramped up and down speeds with fixed torque .

Another study presented the speed control of BLDC motor control using Single Input Fuzzy PI Controller as a replacement for the commonly used conventional linear controller. The advantages of the proposed system include a single control configuration which combines the performance of different systems.

The performance of the proposed system, compared with the conventional system, showed a better dynamic response . 2.

2.1. Motor Construction

The BLDC motor is an AC synchronous motor. It is basically inside-out DC motor as it has the windings on the stator and the rotor is a permanent magnet, as shown in Figure 1 . The main function of the brushes in the DC motor is to reverse the polarity of the electric current using the mechanical commutator. This process results in heating and sparks during the motor operation, in addition to electrical losses, which in return require periodic maintenance. The issue with current commutation can be overcome using electronic commutation. The polarity reversal in the brushless DC motor is performed by the semiconductor power switches. A group of hall-effect sensors is used to synchronize the switching with instantaneous rotor position. The most commonly used sensor control is the six step control. This control is based on capturing the rotor position at six angles using the hall sensors. The sensor signal is needed to align the applied voltage with the motor back-EMF. To validate this type of control, a power converter, with the control based on rotor position measurement, is required. The extra cost for power converter is compensated by the advantage the BLDC drive system offers over DC motors, and also by the decreasing prices of power components and control circuits. Other advantages include excellent performance and higher reliability with low maintenance requirement . Figure 2 shows the motor circuit and its connection to the inverter.

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Modelling of sensored speed control of BLDC motor using MATLAB/SIMULINK (Basim Alsayid)

Figure 2. Motor Connection To The Inverter

Excluding the mutual inductance between phase windings, the motor equations can be expressed as:

2.2. Motor Operation And Control

The BLDC motor detects the position of the rotor using Hall sensors. Three sensors, HA, HB, and HC are required for position information. With three sensors, six possible valid commutation sequences could be obtained. Every 60 electrical degrees of rotation, one of the Hall sensors changes state. Therefore, it takes six steps to complete an electrical cycle . Table 1 shows the switching sequence used to run the motor in the clockwise direction. Figure 3 shows the Hall sensor signals with respect to back EMF and the phase currents [3, 19, 20].

Table 1. Sequence for rotating the motor in clockwise direction

0

According to Table 1 and Figure 3, for sequence 1, S3 and S2 are switched on, and accordingly, Phase B current is positive, Phase C current is negative, and Phase A current is zero. In Figure 4, we can see the energized windings and the stator electromagnetic field (ST) resulting from this situation. In the same figure, we can see the rotor magnetic field (R) forming 120 electrical degrees with the stator electromagnetic field. For sequence 2, S1 and S2 are switched on, and accordingly, Phase A current is positive, Phase C current is negative, and Phase B current is zero. In Figure 5, we can see the energized windings and the stator electromagnetic field (ST) resulting from this situation. In the same figure we can see the rotor magnetic field (R) forming 120 electrical degrees with the stator electromagnetic field.

For sequence 3, S1 and S6 are switched on, and accordingly, Phase A current is positive, Phase B current is negative, and Phase C current is zero. In Figure 6, we can see the energized windings and the stator electromagnetic field (ST) resulting from this situation. In the same figure, we can see the rotor magnetic field (R) forming 120 electrical degrees with the stator electromagnetic field. For sequence 4, S5 and S6 are switched on. Accordingly, Phase C current is positive, Phase B current is negative, and Phase A current is zero. In Figure 7, we can see the energized windings and the stator electromagnetic field (ST) resulting from this situation. For sequence 5, S5 and S4 are switched on. Accordingly, Phase C current is positive, Phase A current is negative, and Phase B current is zero.

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Figure 3. Wave forms of Hall sensors, BEMF, torque and phase currents

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In Figure 8, we can see the energized windings and the stator electromagnetic field (ST) resulting from this situation. In the same figure, we can see the rotor magnetic field (R) forming 120 electrical degrees with the stator electromagnetic field. For sequence 6, S3 and S4 are switched on, and accordingly, Phase B current is positive, Phase A current is negative, and Phase C current is zero. In Figure 9, we can see the energized windings and the stator electromagnetic field (ST) resulting from this situation. In the same figure, we can see the rotor magnetic field (R) forming 120 electrical degrees with the stator electromagnetic field . From the above considerations, we can see that the relative position between stator and rotor magnetic fields at the beginning of all the six sequences is 120 electrical degrees.

Figure 9. Sequence 6

If returned back to sequence 1, at the beginning of the sequence, the relative position between the stator and rotor fields is 120 electrical degrees, as shown in Figure 10. Assuming that after 30 electrical degrees the rotor position become as shown in Figure 11, this means that the stator magnetic field and the rotor magnetic field are now perpendicular. If the same considerations are made at the end of sequence 1 and before the beginning of sequence 2, then we still have the same stator field, whereas the rotor field has moved by another 30 electrical degrees in clockwise, as shown in Figure 12. The angle between stator and rotor magnetic fields is 60 electrical degrees.

By studying the relative position between stator and rotor magnetic fields during all the 6 sequences, it is found that it varies between 60 and 120 electrical degrees with an average value of 90 degree. Figure 10 shows the relative position of stator and rotor magnetic fields at the beginning of sequence 1 (120o).

Figure 11 shows the relative position of the same magnetic fields in the middle of sequence 1 (90o). Figure 12 shows the relative position of the same two magnetic fields at the end of sequence 1 (60o). Figure 10. Relative position of stator and rotor magnetic fields at (120o)

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

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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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