Solar Energy Storage Using Fpga

International Journal of Advances in Electrical and Electronics Engineering 120

ISSN: 2319-1112

ISSN: 2319-1112 /V1N2: 120-127 IJAEEE

Design and implementation of solar robot controlling and storage of energy based on FPGA

Gayarthri.R, 1 , Dr.P.V.Rao 2 , K.Viswanath 3 1Senior Lecturer, ECE Department, Atria Institute of Technology

2Professor, ECE Department, T.John Institute of Technology 3Asst.Professor, ECE Department, R.L.Jalappa Institute of Technology

Bangalore. Karnataka, India Email: [email protected],[email protected], [email protected]

Abstract- To explore integrated solar energy harvesting as a power source for low power systems such as wireless sensor nodes, an array of energy scavenging photodiodes(Light dependent resistances) based on a passive-pixel architecture for imagers and have been fabricated together with storage capacitors implemented using on-chip interconnect CMOS logic process. Integrated vertical plate capacitors enable dense energy storage without limiting optical efficiency. Recent advances in very low power signal processing architectures for sensors has created the opportunity to use CMOS photodiodes, similar to those used in digital cameras, for solar energy harvesting. Moreover, the increase in interconnect capacitance as CMOS processes scale provides an opportunity to store the harvested energy without requiring battery materials to be integrated on-chip. Measurements show 225 W/mm2 output power generated by a light intensity of 20k LUX. And controlling the robot on light intensity either forward or backward direction. If light intensity is on LDR1 than LDR2 then the robot will move in forward direction else the robot will move in reverse direction

Keywords Energy Harvesting, Low Power, Photodiode, FPGA, Solar panel, DC Motor, VHDL.

I. Introduction

Solar energy harvesting has been proposed to extend the lifetime of wireless sensor networks beyond limitations imposed by batteries .To reduce system cost and volume it is desirable to integrate energy harvesting and storage with data processing circuits. Recent advances in very low power signal processing architectures for sensors has created the opportunity to use CMOS photodiodes, similar to those used in digital cameras, for solar energy harvesting. Moreover, the increase in interconnect capacitance as CMOS processes scale provides an opportunity to store the harvested energy without requiring battery materials to be integrated.

Figure 1. Block diagram of a low power wireless sensor node system powered from energy scavengers and a battery. A mux switches between the unregulated energy sources.

Robo LDR

Driver Circuit SPARTAN 3E FPGA

8-Bit ADC

Solar panel

DC Motor

Battery

121 Design and implementation of solar robot controlling and storage of energy based on FPGA

ISSN: 2319-1112 / V1N2: 120-127 IJAEEE

This paper describes a test chip incorporating an array of photodiodes and storage capacitors developed to explore the maximum energy per area that can be gathered from a solar source and stored in a standard CMOS process. The layout and design of an energy scavenging photodiode must balance several competing factors. The charge generated in the depletion region of the photodiode is stored in on-chip capacitors; therefore the physical layout of the diodes Proposed Algorithm should facilitate both the solar energy harvesting and capacitive energy storage.

II. EXISTED SYSTEM

Solar energy is the most readily available source of energy. It is free. It is also the most important of the non-conventional sources of energy because it is non-polluting. We are focusing on the Existing which uses embedded / microcontroller hardware technologies to control the system to capture the maximum energy from sun light. In the existing system, Solar photo voltaic (SPV), Can be used to generate electricity from the sun. Silicon solar cells play an important role in generation of electricity [1-2]. While the output of solar cells depends on the intensity of sunlight and the angle of incidence, and The solar panels must remain in front of sun. But due to rotation of earth those panels cant maintain their position always in front of sun. This problem results in decrease of their efficiency in storing the power.

Delivering power to wireless sensor network nodes is a significant System design challenge. Solar energy harvesting has been proposed to extend the lifetime of these networks beyond the limitations which have been previously imposed by batteries. Prior works have successfully demonstrated powering wireless systems through discrete photovoltaic cells together with separate energy storage devices using board level designs. The system consists of sensors that can observe the environment, an analog-to-digital converter (ADC) that can quantize the analog signal from the sensors, a digital signal processing (DSP) core that can analyze and encode the quantized data and a transceiver (RF) so that the node can transmit and receive information.

The layout and design of an integrated energy scavenging photodiode must balance several competing factors . The charge generated in the depletion region of the photodiode is meant to be stored in on-chip capacitors; therefore the physical layout of the diodes should facilitate both the solar energy harvesting and capacitive energy storage. The light that reaches the photodiodes depletion region must first pass through the passivation layers and avoid the metal storage capacitance, which is constructed on top of the diode to minimize area. A figure of merit (FOM) is needed in order to quantitatively assess the performance of the photodiodes [3]. The FOM used here is also known as the fill factor, which is defined as the maximum output power obtainable divided by the product of the open circuit voltage and the short circuit current, for a given light intensity schematic for a photodiode under illumination delivering power to a load resistance on the left, along with an curve shown on the right. Which are in general functions of the incident light intensity. Graphically, the figure of merit can be seen as the ratio of the two rectangles and a FOM equal to one would be ideal. To explore the photodiode design tradeoffs experimentally, three different geometries were fabricated and tested. the top view layout for the three photodiodes along with a layer key. The first design, photodiode D1, and is similar to a passive pixel structure used for a CMOS imager the p-substrate and n-well form the diode.

The integration of the photovoltaic cells along with analog and digital signal processing circuits is of interest in this work. The scaling of a photovoltaic cell from large, standalone arrays to the integrated circuit level will impact the cells output. Power, efficiency, optimal load resistance, and optical properties of the photodiode. Energy harvesting systems with high power requirements may require a significant area allocation for integrated photodiodes. Integrated photodiodes can also double as power supply bypass capacitors; the additional cost associated with the photodiodes footprint could potentially limit its usefulness to larger feature size technologies. True integration of photodiodes and active circuitry on the same die may require covering the active circuitry with a metal cap to block the incident light from potentially degrading signal integrity. A substrate trench, forming a barrier, can also be employed to help limit the lateral photocurrent traveling from the photodiodes to the active circuitry. The construction of the storage capacitance and routing for the photodiodes must not degrade the optical efficiency (OE), which is defined as the fraction of incident light onto the chips surface which reaches the photodiode. In general, the optical efficiency is influenced by three loss factors: reflection loss, absorption loss, and critical angle loss. Once photons reach the photodiode, the quantum efficiency (QE) determines how many photons will generate electron-hole pairs. The product of OE and QE should be maximized by the geometry of the photodiode and storage capacitance to maximize both the energy harvesting ability and storage capacity.

IJAEEE ,Volume1,Number 2 Gayarthri. R et al.

ISSN: 2319-1112 / V1N2: 120-127 IJAEEE

Problems with existing methods: Microcontroller based systems can be existing but they dont have Automatic Sun Tracking System by getting the maximum power in the day time.

III. Proposed Algorithm

To explore integrated solar energy harvesting as a power source for low power systems, an array of energy scavenging photodiodes based on a passive-pixel architecture for CMOS imagers has been fabricated together with storage capacitors implemented using on-chip interconnect in a 0.35- m bulk process. Integrated vertical plate capacitors enable dense energy storage without limiting optical efficiency. Light energy is converted to electrical energy through a photodiode and mechanical vibrations are converted to electrical energy by an electromechanical transducer .an analog-to-digital converter (ADC) that can quantize the analog signal from the sensors, a CPLD/FPGA core that can analyze and encode the quantized data and a transceiver (RF) so that the node can transmit and receive information. The systems energy gathering ability will depend on environmental conditions, which can change over time.

The scavenged energy needs to be regulated before being used by these functional blocks Delivering power to wireless sensor network nodes is a significant System design challenge. Solar energy harvesting has been proposed to extend the lifetime of these networks beyond the limitations which have been previously imposed by batteries. Prior works have successfully demonstrated powering wireless systems through discrete photovoltaic cells together with separate energy storage devices using board level designs.

Now, We can formally state our problem to explore integrated solar energy harvesting as a power source for low power systems, to design and implement the solar energy harvesting system on FPGA to take sunlight as input for LEDs and convert those signals to digital and to target the FPGA to analyze the intensity of the light and rotate the panel accordingly and store energy into battery. The system consists of sensors that can observe the environment, an analog-to-digital converter (ADC) that can quantize the analog signal from the sensors, Light energy is converted to electrical energy through a photodiode and mechanical vibrations are converted to electrical energy by an electromechanical transducer. A multiplexer (mux) is used to switch between energy sources. The systems energy gathering ability will depend on environmental conditions, which can change over time. Hence, the scavenged energy needs to be regulated before being used by these functional blocks.

IV. Energy Scavenging Photodiodes

Figure 1 shows the block diagram of a typical wireless sensor node powered by light, mechanical vibration, and a battery. Light energy and vibrations are converted to electrical energy by photodiodes and electromechanical transducers, respectively. The systems energy gathering ability will depend on environmental conditions which change over time and the scavenged energy needs to be regulated before being used. The sensor node consists of an analog-to-digital converter (ADC) that samples sensor data, a DSP core, and an RF transceiver. For low duty cycles, average power for this system can be under 5 W. Light reaching the photodiode depletion region must first pass through the passivation layers and around the storage capacitance, which is constructed on top of the diode to reduce area. Light Dependent Resistors are very useful especially in light/dark sensor circuits. Normally the resistance of an LDR is very high, sometimes as high as 1000 000 ohms, but when they are illuminated with light resistance drops dramatically [7].


ISSN: 2319-1112 / V1N2: 120-127 IJAEEE

Figure 2: Top view of photodiodes. (a) D1, (b) D2, (c) D3, (d) Layer key.

The total PN junction capacitance is the sum of the capacitances from the diffusion-well diode, Dpdif-nw, and the substrate-well diode, Dpsub-nw. This capacitance can be modeled by a particular capacitance per unit area, Cj and a particular side wall capacitance per unit length, Cjw. These capacitances are in general nonlinear (voltage-dependent). The width of the depletion region will shrink with an increase in applied forward bias voltage across the junction. This decrease in depletion width will lead to an increase in the depletion capacitance, which can be modeled using a square root dependence on applied voltage. The pn junction capacitances can be written as

Where is the built in potential of the junction, Ai is the area and Pi is the perimeter of the ith diode. Table 1 summarizes the simulated capacitive characterization of the three diodes shown in Figure 2. The Cd given here is calculated with a junction voltage of 0.55 V, which is close to the open circuit voltage of the photodiodes under normal indoor lighting conditions. Solar cells or photovoltaic cells are in fact large area semiconductor diodes that convert sunlight into electrical current to produce usable power. The power output of a solar array is given in watts. In order to calculate the energy needs of the application, a measurement in watthours per day is often used. The output power of the solar cell depends on multiple factors, such as sunlight intensity and direction of the cells. In order to reach the maximum power, the solar tracker has been added to this system to avoid time limit of the fixed systems. White Light Emitting Diode (WLED) is a device that emits white light when an electrical current passes through it in forward direction.

The interest of WLED for lighting applications has been growing over the past few years and will replace the incandescent systems in the next few years due to their very long life, low voltage operation needs and high efficiency. This WLED based solar cells system for lighting application aims to provide solar energy for operating WLED lights for maximum hours of operation [8-9]. To optimize the low power consumption of this system, the Pulse Width Modulation (PWM) has been used. This method is widely used in wireless optical communication system. The method of driving a WLED is to switch it on and off between the maximum and zero current and use a MOSFET in switching regime with a low power consumption required. The light illumination is determined by duty cycle changing of the driving pulse signal at the gate of MOSFET. If the frequency pulse is high enough, the human eyes will not follow the changes of the light and will response only to the average level of light.

Table -1 Experiment Result (25C, Area = 338m2).

D1

D2

D3

TL1

SFUB Cm(pF) 0.254 0.254 0.216 1.004 0.61 Cd0(pF) 0.070 0.178 0.285 - - Cd(pF) 0.113 0.286 0.460 - -


ISSN: 2319-1112 / V1N2: 120-127 IJAEEE

A. Solar Cell Characteristics

The simplest solar cell equivalent model consists of diode and current source connected in parallel. Current source current is directly proportional to the solar radiation. Diode represents PN junction of a solar cell. Equation of ideal solar cell.

Which represents the ideal solar cell model where Iph is photo current in ampere, Is is reverse saturation current in ampere (approximately 10-8/square meter), V is diode voltage in volt, and m is diode ideality factor (m = 1 for ideal diode). Thermal voltage can be calculated with the following equation: where VT is thermal voltage; its value about 25mV at 25C k is Boltzmanns constant. The value of Boltzmann's constant is approximately 1.3807 x 10-23 joules per kelvin (J K-1). T is temperature in kelvin and q is charge of electron which value is 1.6 x 10-19 coulombs. The working point of the solar cell depends on load and solar insulation.

Figure: 3 White LED Characteristics Structure of WLED consisting of a GaInN blue LED chip and a phosphor encapsulating the die. (b) Wavelength converting phosphorescence and blue luminescence.

HARDWARE SETUP


ISSN: 2319-1112 / V1N2: 120-127 IJAEEE

FLOW CHART OF PROPOSED METHOD

The sensitivity of a photo detector is the relationship between the light falling on the device and the resulting output signal. In the case of a photocell, one is dealing with the relationship between the incident light and the corresponding resistance of the cell of the photocell versus wavelength of light. Light energy is converted to electrical energy through a photodiode and mechanical vibrations are converted to electrical energy by an electromechanical transducer.

An ADC has an analog reference voltage or current against which the analog input is compared. So, basically, the ADC is a divider. The Input/Output transfer function is given by the formula indicated here. If you have seen this formula before, you probably did not see the G term (gain factor).Here is an example of a 3-bit A/D converter. Because it has 3 bits, there are 23 = 8 possible output codes. The difference between each output code is VREF / 23. Assuming that the output response has no errors, every time you increase the voltage at the input by 1 Volt, the output code will increase by one bit. The solar energy is absorbed by the solar cell arrays and converted it to electrical energy. The solar cell array is made to track the sunlight throughout the day. SOLAR cells or photovoltaic cells are in fact large area semiconductor diodes that convert sunlight into electrical current to produce usable power. They have long been used in situations where electrical power from power station is unavailable, such as in remote area power systems. The power output of a solar array is given in watts. In order to calculate the energy needs of the application, a measurement in watt-hours per day is often used. The output power of the solar cell depends on multiple factors, such as sunlight intensity and direction of the cells. In order to reach the maximum power, the solar tracker has been added to this system to avoid time limit of the fixed systems.

Start

Light is sensed by LDR

Analog signal from LDR is Converter

Code is processed in FPGA

FPGA code is used to control

Decision Taken by

Forward rotation

Stable

Reverse rotation

Solar cell array Battery

Stop


ISSN: 2319-1112 / V1N2: 120-127 IJAEEE

III. Experiment and Result

IV.CONCLUSION In more advanced technologies the capacitance per unit area will increase as the minimum distance between plates

shrinks. However, for the case where vertical parallel plate storage capacitors are constructed above the photodiodes this decrease in distance will negatively impact the OE due to a smaller aperture size. Table 1 summarizes the results with an incident light intensity of 20k LUX, similar to being outside on a sunny day. Based on these results, design D3 with area 150 m x 150 m can deliver 5 W to power the system described above. D1 needs 184 m x 184 m and D2 needs 164 m x 164 m to deliver 5 W. Without illumination, the system energy must be supplied by the integrated storage capacitors. And controlling the robot on light intensity either forward or backward direction. If light intensity is on LDR1 than LDR2 then the robot will move in forward direction else the robot will move in reverse direction. For a 25 mm2 total diode area consisting of 3 diodes in series with the metal storage capacitances for each diode connected in parallel, D1, D2, and D3 can supply enough energy for the DSP in [2] to produce 687, 745, and 903 output samples respectively. Future work will involve testing the photodiodes response with incident light from a green laser (532 nm wavelength) to determine OE and QE as well as fabricating the diodes in poly silicon on top of the die to exploit more area for energy scavenging.

V. REFERENCE

[1] A. Kansal and M. Srivastava, An environmental energy harvesting framework for sensor networks, Intl. Symp.Low Power Elec. and Design (ISLPED) 03, pp. 481-6, Aug. 2003. [2] R. Amirtharajah and A. Chandrakasan, A micropower programmable DSP using approximate signal processing based on distributed arithmetic, IEEE JSSC, Vol. 39, No. 2, p. 337-47, Feb. 2004. [3] M. Scott, B. Boser, and K. Pister, An ultralow-energy ADC for smart dust, IEEE JSSC, Vol. 38, No. 7, p. 1123- 9, July 2003. [4] B. Otis, Y. Chee, and J. Rabey, A 400 W-Rx, 1.6mW-Tx super-regenerative transceiver for wireless sensor networks, ISSCC 05, pp. 396-7, 606, Feb. 2005. [5] P. Catrysse, P. and B. Wandell, Optical efficiency of image sensor pixels, J. Opt. Soc. Am., Vol. 19, No. 8,August 2002. [6] I. Fujimori, C. Wang, and C. Sodini, A 256 256 CMOS differential passive pixel imager with FPN reduction techniques, IEEE JSSC, Vol. 35, pp. 2031-7, December 2000. [7] Advanced Design Systems software package 2005, Agilent Inc. [8] R. Aparicio and A. Hajimiri, Capacity Limits and Matching Properties of Integrated Capacitors, IEEE JSSC, vol. 37, no. 3, pp. 384-93, March 2002. [9] J. Soo Lee, R. I. Hornsey, and D. Renshaw, Analysis of CMOS Photodiodes-Part I: Quantum Efficiency, IEEE Trans. on Electron Devices, Vol. 50, No. 5, May 2003. [10] J. Soo Lee, R. I. Hornsey, and D. Renshaw, Analysis of CMOS Photodiodes-Part II: Lateral Photoresponse, IEEE.

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Solar Energy Storage Using Fpga