photovoltaic power system for autonomous mobile robots

Proceedings of International Conference On Innovations, Recent Trends And Challenges In Mechatronics, Mechanical Engineering And New High-Tech Products Development –

MECAHITECH’11, vol. 3, year: 2011

26

Photovoltaic Power System for Autonomous Mobile Robots

Rotar Dan “Vasile Alecsandri” Bacău University,

Calea Mărăşeşti, 157, Bacău, 600115, Romania [email protected]; [email protected]

Abstract

Autonomous mobile robot systems are currently used in many fields, from space exploration to military or civilian applications on the planet. One of the important characteristics of such robots is represented by their ability to secure the power supply. Among the many possibilities of autonomous robots feeding, the use of photovoltaic panels is one of the most attractive solutions. Voltaic panels are easy to use and they are environmentally friendly, producing no pollution. Photovoltaic panels also provide a very good relation between power / price.

Power solutions currently used for autonomous mobile robots use either solar panel possibly provided with concentrators or mobile panels that have certain algorithms to guide the panels.

This paper presents a new solution for the powering of a mobile autonomous robot with a photovoltaic cell panel. The orientation system of the panel uses a fuzzy controller to assess the effects of the orientation and to adopt the optimal solution. The paper presents the systems analysis, explains the system structure and analyzes the benefits of the proposed system. The obtained experimental results indicate that such a system allows the increase of the robot’s autonomy. Keywords Autonomous mobile robot, programmable system on chip, photovoltaic cell, fuzzy controller, maximum power tracking Introduction

Currently, the development mechatronics is done on two levels: the traditional mechatronics which deals with programmed systems, generally fixed, which carry out well defined tasks, and the robotics of autonomous mobile systems.

An autonomous system is a system that can find solutions to the problems with minimum assistance from a human operator or it can act independently without needing human assistance under less defined conditions. The mobile robot is a robot that can move in space. Accordingly, an autonomous mobile robot is a robot that can travel on land with natural and artificial obstacles and can independently solve the problems that arise during the operations. Intense research is currently underway in the field of autonomous mobile robots that are considered as the bots of the XXI century.

The researches are conducted both in terms of equipping the robot with intelligent control and towards equipping the robot with the ability to seek its own resources including both the energy and the spare parts.

The first and easiest solution used to power the robot was that of a power cord of the robot (umbilical cord). This method severely limits the movement possibilities of the robot and can be used in a very limited number of cases.

The second solution is the use of batteries. This method allows increasing the mobility of the robot, but it is still limited by the amount of energy stored in batteries. Even if the robot is equipped with a monitoring system for the electricity consumed during operation, the robot is still limited by the battery capacity.

An improved method is the use of photovoltaic elements to increase robot autonomy. Such a system will have greater autonomy because of the energy captured from the light energy. Currently, such systems are used in the autonomous mobile robots. Most of these systems have a fixed catch area and are provided with various elements for the concentration of the luminous flux.

For robots that move on tracks and in less known areas, the light source direction is unpredictable. This paper presents a solution for the power supply of the mobile robots with a mobile photovoltaic cell

with a higher efficiency of light energy conversion. The proposed system has two basic elements: a multidirectional sensor for light intensity measuring and a photovoltaic cell panel. The system meets the following: it determines the direction of the light source with the multidirectional sensor; it decides the



27

photovoltaic panel orientation to the light source depending on several factors (e.g. light source direction stability, efficiency of movement given the necessary energy for the panel positioning), and the optimization process of the energy conversion and the energy monitoring.

The hardware structure used for the control circuit, obtained using a PSoC (Programmable System on Chip) Cypress CY8C3866AXI-040 microcontroller type and the structure of the control and monitoring programs are also explained.

After the tests and experiments were performed, it was determined that the solar power system for autonomous mobile robots presented in this paper increases the autonomy of the robot. Photovoltaics cells A photovoltaic cell converts light energy into electricity through the photovoltaic effect. There are

different materials and processes that can, in theory, meet photovoltaic energy conversion. However, in practice, the semiconductor junctions are usually used. The efficiency of the solar cells varies from 6% for amorphous silicon solar cells to 42.8% for multi-junction solar cells. The conversion efficiency of the trade solar cells with Si multicrystalline is around 14-19% [1]. Figure 1 shows the simplest model for the junction of photovoltaic cells. According to Figure 1 we can write the current equation (Kirchhoff theorem I) like this:

shdphS IIII (1)

where: Is - current through the Rload load and Rs series resistance, Iph - pn junction light-generated current, Id - current through pn junction and Ish - current through the leak resistance from the photovoltaic cell model. From equation (1) we obtain the I-V (current-voltage) characteristic equation of the photovoltaic cell. Therefore we explain the Iph, Id and Ish currents [2]:

03021 1 TTPFFPFPI jSSph (2)

where: F0 = 1000W/m2, T0 = 298.15K, P1 [Am2/W], P2 [m2/W] and P3 [1/K] data are normally a manufacturer constant and Tj is the junction temperature.

1exp 0

j

SSS

Sfsatd T

IRVkNa

eII (3)

where:

j

gjsat kT

ETPI exp3

4 (4)

where e0 = 602177331019[C] is electron charge, αf = 1...5 is the ideal diode factor (usually 1), Ns - number of cells in series, k = 1.38065810-23[J/K] Boltzmann's constant, Rs [Ω] is the series resistance of the model, Eg [eV] is the bandwidth and P4 [A/K3] is a correction factor. P4, Rs and Rsh can be obtained from the solar panel data sheet.

The diode reverse saturation current is given by:

sh

SSSsh R

IRVI (5)

The characteristics of photovoltaic cells are represented by the load current depending on the load voltage. This

Figure 1: Photovoltaic cell model.



28

family of curves is called IV curves. An IV curve describes the amount of current that will produce a solar cell at a certain voltage. Thus, the operating voltage and cell current are determined by the load characteristics. IV curves are shown in Figure 2 (dependence of load current according the load voltage for different values of the light intensity [kW/m2]). The intensity of the light received by a solar cell is called "number of suns"; one sun corresponds to an illumination value under the AM1.5 standard of 1 kW/m2. A PV (PhotoVoltaic) module designed to function under one sun is called the flat mode, while modules that use sunlight intensity for many suns are called concentrators.

Several parameters characterizing photovoltaic cells can be defined on the IV curves: short-circuit current (ISC), open circuit voltage (VOC), fill factor (FF) and efficiency. At a load resistance RS, the photovoltaic cell will operate at the point of the IV curve where V = IRS. This point can be found by plotting the line which leaves from the origin and has a slope of 1/RS on the curve IV - the point where the line intersects the IV curve shows the operating voltage and the operating current. Since the IV curve described by the equation (1) is nonlinear, the maximum power is one point on the curve. This point is called maximum power point (MPP). The photovoltaic cells should always work

at the maximum power point so that they can deliver the maximum energy. For this reason, photovoltaic cells supply circuits are provided with mechanisms for maximum power point tracking (MPPT). Maximum Power Point Tracker Figure 3 shows a simplified block diagram of a power circuit provided with photovoltaic cell.

In Figure 3, the energy taken from the photovoltaic cell is used by the DC-DC (DC - direct current)

converter for charging the battery. The DC-DC converter is controlled via the control circuit in two directions. The first direction is the maximum power point tracking (MPP) through the MMPT circuit so as to obtain maximum energy from the photovoltaic cell. In the second line, the surge protection is intended for batteries and the surge can come from the DC-DC converter or from the inverter for energy recovery from the load. For the charging of the battery, the applied VB voltage needs to be constant. This is in contradiction with the fact that the voltage provided by photovoltaic cells should be amended so as to reach the MPP point. In the circuit used in this paper, the problem is solved by the maximum power point tracking circuit and the TPS62290 Texas Instruments buck (step-down) DC-DC converter [3]. The input voltage at the DC-DC converter is changed by modifying an active load formed by a field effect transistor as shown in Figure 4.

Figure 2: IV and power family of curves.

Photo-voltaic Cells

DC-DC

Converter

MPPT

Circuit

Control

Circuit

Over-

Voltage

Inverter

/

Load

Battery

IS

VS

VB VL

Figure 3: Block diagram of power system.



29

The load resistance for the photovoltaic cell consists of the R3 resistance and of the Q1 field effect transistor. The Q1 field effect transistor operates in the resistance regime controlled by the output voltage provided by the U2 inverting amplifier. The circuit operation can be explained using Figure 5. It is assumed that the circuit operating point is

point A (VA, IA). This is the corresponding maximum power provided by photovoltaic cells at a beam intensity of 0.5kW/m2. For an increase of the intensity of light to 1.5kW/m2, the operating point moves to point A' (VA’, IA’). At this point the power provided by the photovoltaic cell is not full. Moving from point A to point A' causes the increase of the Vs tension that leads to a decrease of the gate voltage of the Q1 transistor. The transistor output resistance increases, which increases the RS load resistance seen by the photovoltaic cell. As a result, the slope of the load decreases, and the operating point moves from point A' to point B. Point B represents the maximum power corresponding to an intensity curve of 1.5kW/m2. The electricity provided by

the photovoltaic cell is then converted by DC-DC converter for charging the battery. The photovoltaic current expression is given by equation (2), but certain considerations must be taken

into account. At low levels of light intensity, the effect of leak resistance (Rsh in Figure 1) becomes increasingly important. For this reason it is best to choose a photovoltaic cell with the leakage resistance as great as possible. Photovoltaic current variation is not linear as shown in equation (2). Also, the dependence of the Q1 channel resistance field effect transistor of the control voltage is not linear. However, for small signal variations it can work with some linearization to simplify processing.

The light tracking system The measure of light intensity

Figure 4: The power circuit diagram detail.

Figure 5: The circuit operation for the maximum power point tracking.



30

The brightness tracking system is shown in Figure 6. When the robot moves, this system directs the photovoltaic panel to the stable light source with the greatest intensity.

To achieve this, the guidance system is equipped with a sensor system for analyzing the light source and a horizontal and vertical positioning of the photovoltaic panel system.

The sensor system for the analysis of the light source is shown in Figure 7. This sensor system consists of nine photosensitive sensor cells. Each cell contains one photosensitive element providing a signal in proportion with the light source

intensity. The nine sensors are placed on a sphere and the position of a light sensor provides the information about light source direction. Analog signals from the nine sensors that measure the light source intensity are applied to an analog-digital via a multiplexer (Figure 10). The correspondents of the measured numerical values of the intensities of each sensor are stored in RAM (Random Access Memory).

The command and control system knows the relative position of the photovoltaic panel in relation to the sensor system based on the number of steps of the stepping motors drives for the photovoltaic panel positioning. Based on the information position of the maximum intensity light source,

guidance command is developed for the photovoltaic panel. For the perfect alignment of the photovoltaic panel with the light source, two sensors are still used on the vertical and horizontal alignments (Figure 6). The alignment sensors act so that the photovoltaic panel is brought into perpendicular position to the source light output from. The principle diagram of the alignment sensor is shown in Figure 8. This circuit forms a resistive divider for which the current through the divider is proportional to the light

intensity and the potential at point A indicates the light source position. Voltage of the A point, which applies to a comparison circuit, is given by (6).

212 RRVDDRU A (6) When the two photoresists are illuminated equally: R1 = R2 and VA = VDD/2. When the R1 photoresist is brightly illuminated, R1 < R2 and VA > VDD/2. The lighting mechanism of the two photoresists is explained in Figure 9. The panel is considered aligned when the two photoresists are equally illuminated. This is shown by a comparator circuit that detects the occurrence of a voltage equal to VDD/2 at point A.

Figure 6: The brightness tracking system

Light Sensors System

Vertical Drive

Mechanism

Horizontal Drive

Mechanism

Photovoltaic Panel

Horizontal Alignment

Sensor

Vertical Alignment

Sensor

Light Sensors

Figure 7: The light sensors system.

Figure 8: The alignment

sensor schematics.

Figure 9: The alignment principle.



31

The positioning system of the photovoltaic panel The photovoltaic panel positioning system allows its vertical movement with a maximum angle of 320o

and a horizontal angle of up to 180o. The operation is performed by stepper motors with mechanical transmission with gear and flexible coupling. The elastic coupling mechanism protects from accidental strikes or attacks. The mechanical gear transmission ensures proper positioning resolution of the photovoltaic panel. Using stepper motors determines the relative position of the photovoltaic panel by counting the number of the steps. These engines also allow the determining energy requirements for a movement. Energy is determined on the basis of the energy (Wstep) consumed to make a step and the necessary number of steps (n_step) needed to be made, according to relation (7).

C

stepstepmove f

n_stepPn_stepWW

(7)

where: Pstep is the power necessary to carry out a step and fC is the frequency of stepper motor command. The photovoltaic panel positioning control system must know the relative position of the panel in

relation to the photosensitive sensor system every time. Because it was necessary to introduce a flexible coupling protection, it is clear that the relative position can change in an uncontrolled manner. The discrepancy between the actual position and the known position of the system is determined by the location of the light source direction as described by the optical sensor system. If the location cannot be done, then the relative position of the photovoltaic panel to the optical sensors system is reapplied. For correcting the position, the positioning system is equipped with mechanical limit switch ends of the vertical and horizontal race. The correction of the photovoltaic panel position is made by moving the point of origin established by these mechanically limited. Circuit of photovoltaic panel control system

Figure 10 shows the structure scheduled for the Cypress PSoC CY8C3866AXI-040 system used to control the orientation of the photovoltaic cell. An analog signal multiplexer (Amux_1) for the provided signal from the optical sensor system is used. On the analog inputs of the multiplexer (via pin Pin_1 - Pin_9) the signals proportional to light intensity, from nine optical sensors are applied. The multiplexer output is applied to the ADC (Analog to Digital Converter) ADC_DelSig_1.

The obtained numerical data represents the light source direction showed by the sensor number from which data is taken and the light intensity by the value is recorded. These numeric values are stored in an array in

memory. The numerical values are quantized and are accompanied by the time information so that we can determine the stability of the source in time. A STMicroelectronics L6228 control circuit type is used for the control of the two bipolar stepper motors for moving the photovoltaic panel in the two planes, both vertically and horizontally. The central unit commands these engines in a classical scheme by means of a Clock_1 clock circuit providing a frequency of 400 Hz (the digital output Pin_10). In addition, the central unit generates control

signals for these engines: sense, full step or half step operation, on/off etc. (Figure 11).

The positioning process begins based on the steering information provided by the light sensors system. This information is indicative and is supplemented by information from the sensors on the horizontal and vertical alignment. The positioning process ends when the comparators associated with the alignment sensor lights also indicate equal illumination of the two photosensitive sensor elements (Figures 8 and 9). The alignment sensors use the Comp_1 and Comp_2 comparators connected to the internal reference voltage (Vref). The signals from the alignment sensors are applied to the analog input Pin_10 and Pin_11 respectively.

In order to pursue the maximum power point supplied from the photovoltaic cell (U2 circuit in Figure

Figure 10: The programmed PSoC structure.

Figure 11: Control signals for L6228

command circuit.



32

4) an amplifier is also necessary. Command and control software of the photovoltaic panel control system

The command and control system for the positioning of the photovoltaic panel uses a fuzzy controller [4]. Because the Cypress CY8C3866AXI-040 circuit is provided with an 8051 CPU type with 64K of program memory, 8k of RAM, and a 25MHz clock frequency, the control system is achieved with a Mamdani fuzzy controller. The block diagram of the command and control program is shown in Figure 12.

The data input source is represented by the values provided by the nine of optical sensors of the light sensor system and by the count of the number of steps for the two stepper motors for the positioning of the photovoltaic panel. Based on data recorded in RAM PSoC, the direction of the light source with maximum intensity can be determined by detecting the maximum light intensity sensor that measures the light intensity and the size.

Determining the necessary positioning energy is based on the current position of the photovoltaic panel (determined by counting the number of steps by stepping motors) and the position the panel must reach (based on the direction indicated by the optical sensors system). The fuzzy controller uses three input quantities: the light source stability, determined by recording the time as the light source intensity remains within certain limits, the intensity of the light source and energy required for the positioning. Based on this amounts we can decide whether the photovoltaic panel positioning is done or not. For this reason, the size of the output fuzzy controller is the size of the relay with two states: enabled or prohibited. Thus, if the regulator decides that the movement will be, the stepping motors are controlled based on the information calculated in the photovoltaic panel route determination block.

Three input quantities are associated with linguistic variables and triangular membership functions, as shown in Figure 13. Similarly, although the size of the output is a function of validation, a triangular function (Figure 14) was also adopted. After defuzzyfication, the output value is compared with a reference size to generate the signal that validates or invalidates start stepping motors to position the photovoltaic panel.

Given that the photovoltaic panel decision to move influences the efficiency, the choice of membership functions is very important and requires a fine adjustment of the adopted domain for the membership functions. However, to some extent, the adjustment is permitted by adjusting the reference value with which to compare the size of the output of the fuzzy controller.

17 rules are established for the Mamdani inference machine. These rules must determine whether the orientation of the photovoltaic panel to the source of light is productive. The stability of the light source, the light intensity and energy required to perform the movement are taken into consideration.

Figure 12: Block diagram of the command and control program.

Figure 13: The membership input functions.



33

In principle, the light source must have a high stability, a high brightness and low energy for

photovoltaic panel orientation. The rules set also deal with the limit situations when the energy gained is comparable to the energy consumed.

Figure 15 presents the following area: the output size depending on the stability of the light source, the energy used to move the photovoltaic panel (Figure 15.a) and the output size depending on the stability of the light source and intensity of the light source (Figure 15.b).

The used central unit is an 8-bit 8051 compatible unit. For this reason, a simple from was adopted for the membership functions. This is favored by the simplicity of the output size which is a binary size. Surfaces obtained in this way lead to good results in practice.

Figure 15.a: Command versus stability and energy Figura 15.b: Command versus brightness and stability. Here are the rules used to obtain these surfaces. 1. If (stability is moderate) and (brightness is low) and (energy-consumption is small) then (command is certain) 2. If (stability is moderate) and (brightness is low) and (energy-consumption is mean) then (command is perhaps) 3. If (stability is moderate) and (brightness is low) and (energy-consumption is high) then (command is not) 4. If (stability is moderate) and (brightness is average) and (energy-consumption is small) then (command is certain) 5. If (stability is moderate) and (brightness is average) and (energy-consumption is mean) then (command is perhaps) 6. If (stability is moderate) and (brightness is average) and (energy-consumption is high) then (command is not) 7. If (stability is moderate) and (brightness is high) and (energy-consumption is small) then (command is certain) 8. If (stability is moderate) and (brightness is high) and (energy-consumption is mean) then (command is certain) 9. If (stability is moderate) and (brightness is high) and (energy-consumption is high) then (command is perhaps) 10. If (stability is high) and (brightness is low) and (energy-consumption is small) then (command is certain) 11. If (stability is high) and (brightness is low) and (energy-consumption is mean) then (command is certain) 12. If (stability is high) and (brightness is low) and (energy-consumption is high) then (command is perhaps) 13. If (stability is high) and (brightness is average) and (energy-consumption is mean) then (command is certain) 14. If (stability is high) and (brightness is average) and (energy-consumption is high) then (command is certain) 15. If (stability is high) and (brightness is high) and (energy-consumption is small) then (command is certain) 16. If (stability is high) and (brightness is high) and (energy-consumption is mean) then (command is certain) 17. If (stability is high) and (brightness is high) and (energy-consumption is high) then (command is certain)

Conclusions This paper presents a solution for empowering the mobile robots supplied with battery. The additional energy is taken from the environmental sources of light. Because a mobile robot frequently changes its position, it is

Figure 14: The membership output function.



34

necessary that the capture system monitors power light sources. This paper presents the implementation of such solutions for capturing of the light energy by the photovoltaic panel and its guidance system guided by a fuzzy controller. The amount of energy gained by the system essentially depends on how the fuzzy inference machine is designed. This machine must decide on the PV panel orientation taking into consideration the energy balance between gained and consumed energy. Using the fuzzy controller is not strictly necessary. All sizes present in the system can be calculated and the output size can be determined through mathematical calculation. In this case, certain limits should be set in order to obtain the output size. This makes the positioning system more rigid. The fuzzy inference machine offers greater flexibility, therefore this solution was preferred. The experimental results confirm the correctness of the choice. Photovoltaic panel movement is often done when the robot is stopped, but if the situation is favorable, the positioning of the panel is made when the robot is moving. After the experiments, an average energy saving of 37% (favorable environmental conditions) was found. This value was obtained by comparing the same activities of the robot with the photovoltaic system and without it. It is obvious that the outcome is influenced by the environmental conditions and the type of work carried out by the robot. However, one can say that the system developed provides additional autonomy mobile robots and is especially useful in situations where the autonomy of the robot must be as high as possible. Bibliography [1] Dorin Petreus, Cristian Farcas, Ionut Ciocan, “Modeling and simulation of photovoltaic cells”, Acta Technica Napocensis, Electronics and Telecommunications, Volume 49, Number 1, 2008 [2] Joe-Air Jiang, Tsong-Liang Huang, Ying-Tung Hsiao, and Chia-Hong Chen, "Maximum Power Tracking for Photovoltaic Power Systems", Tamkang Journal of Science and Engineering, Vol. 8, No 2, pp. 147-153, 2005 [3] Adriaan Arie Johannes Lefeber, "Tracking control of nonlinear mechanical systems", Universiteit Twente, Nederlands, ISBN 90-365-1426-6, 2000 [4] Serge Guillaume, “Designing Fuzzy Inference Systems from Data: An Interpretability-Oriented Review’, IEEE Transactions On Fuzzy Systems, Vol. 9, No. 3, June 2001 [5] James Kramer, Matthias Scheutz, "Development environments for autonomous mobile robots: A survey", Journal Autonomous Robots, Volume 22 Issue 2, Kluwer Academic Publishers Hingham, MA, USA, February 2007 [6] Amanda Whitbrook, "Programming Mobile Robots with Aria and Player", ISBN 978-1-84882-863-6, Springer-Verlag London Limited, 2010 [7] Johan H. R. Enslin, Mario S. Wolf, Daniel B. Snyman, and Wernher Swiegers, "Integrated Photovoltaic Maximum Power Point Tracking Converter", IEEE Transactions On Industrial Electronics, VOL. 44, NO. 6, pp. 769-773, December 1997 [8] R. Babuska and H. B. Verbruggen, “A new identification method for linguistic fuzzy models,” in Proc. Fourth IEEE Int. Conf. Fuzzy Syst.,Yokohama, Japan, Mar. 1995 [9] D. P. Hohm and M. E. Ropp, "Comparative Study of Maximum Power Point Tracking Algorithms", Progress In Photovoltaics: Research And Applications, Prog. Photovolt: Res. Appl., 11:47–62, pp 47-62, 2003 [10] F. Bellosa, "The Benefit of Event-Driven Energy Accounting in Power-Sensitive Systems," Proceedings of 9th ACM SIGOPS European Workshop, Kolding, Denmark, September 2000 [11] Trishan Esram, Jonathan W. Kimball, Philip T. Krein, Patrick L. Chapman, and Pallab Midya, "Dynamic Maximum Power Point Tracking of Photovoltaic Arrays Using Ripple Correlation Control", IEEE Transactions On Power Electronics, VOL. 21, NO. 5, pp. 1282-1291, SEPTEMBER 2006, [12] Eftichios Koutroulis, Kostas Kalaitzakis, and Nicholas C. Voulgaris, "Development of a Microcontroller-Based, Photovoltaic Maximum Power Point Tracking Control System", IEEE Transactions On Power Electronics, VOL. 16, NO. 1, pp. 46-54, JANUARY 2001 [13]. Trishan Esram, and Patrick L. Chapman, "Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques", IEEE Transactions On Energy Conversion, VOL. 22, NO. 2, pp. 439-449, JUNE 2007 [14] Weidong Xiao, Nathan Ozog, and William G. Dunford, "Topology Study of Photovoltaic Interface for Maximum Power Point Tracking", IEEE Transactions On Industrial Electronics, VOL. 54, NO. 3, pp. 1696-1704, JUNE 2007, [15] V. Salas, E. Olias, A. Barrado, A. Lazaro, "Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems", Solar Energy Materials & Solar Cells 90, pp. 1555–1578, 2006.

photovoltaic power system for autonomous mobile robots

Documents