Integrated Multi-Source Energy Harvesting Architecture for Autonomous UAV Operations

Integrated Multi-Source Energy Harvesting Architecture for Autonomous UAV Operations

Levin Rohith S

Department of Electronics and Communication Engineering, Thiagarajar College of Engineering Madurai, India

Dr. G. Ananthi

Department of Electronics and Communication Engineering, Thiagarajar College of Engineering Madurai, India

Dr. P. G. S. Velmurugan

Department of Electronics and Communication Engineering Thiagarajar College of Engineering Madurai, India

R. Tharaniya

Department of Electronics and Communication Engineering, Thiagarajar College of Engineering Madurai, India

Abstract – In this paper, an efficient hybrid energy harvesting interface is proposed to synergistically scavenge power from Radio Frequency (RF) and piezoelectric sources, and power up the low power sensors in Unmanned Aerial Vehicle (UAV). The Piezoelectric Harvester (PEH) uses piezoelectric sensors to detect the vibration from the motors of the drone and its output is rectified by a bridge rectifier circuit and stored in a capacitor. The RF harvester stores energy from the nearby base station using an antenna and it is rectified using Joe Tates energy harvesting circuit. The total synergistically extracted power from both harvesters is more than the power obtained from each independently and the total energy is stored using a low power rechargeable battery. The system supplies 1-3.4 V output for powering up wireless sensors in the drone. This hybrid energy harvesting system is formulated as a convex optimization problem solvable using cvx tool in MATLAB to find the optimal parameters for power maximization.

Keywords – Piezoelectric sensor, RF energy harvesting, convex optimization

1. INTRODUCTION

   The Unmanned Aerial Vehicle (UAV), initially used in various fields like medicine, agriculture, disaster response, and military, now serves surveillance and reconnaissance too. UAV base stations are useful in public safety communications, big data analysis in Internet of Things [1]. UAVs can provide seamless wireless connectivity, especially at lower altitudes, improving link quality for ground users. Advances in electronic circuits have made low-power Wireless Sensor Networks (WSNs) common, although limited battery size affects sensor node lifetimes. A

   feasible solution to extend the lifetime of a wireless sensor node is by harvesting energy from ambient energy sources [2]. UAVs are constrained by their small size and weight, limiting their fuel capacity. They rely on rechargeable batteries, but it’s impractical to increase battery size significantly due to the impact on the UAV’s weight and size. The idea of RF and vibration energy harvesting using smart materials has been proposed as a way to improve UAVs without adding weight [3]. Piezoelectric vibration harvesters are used to gather vibration energy. They involve designing beams and developing energy transfer in storage circuits through sensor creation [4]. The piezoelectric fiber- based transducers are used in UAVs in peizo electric harvesting [5-6]. RF energy harvesting is another research area harvests energy from RF signals in the nearby environment [7-8]. Simultaneous Wireless Information and Power Transfer (SWIPT) is applied in UAV base stations to maximize the data rate using parameters [9-10]. Wireless sensors on UAVs are powered by battery units attached to the motor or autonomous sources. Extending sensor lifetimes can involve additional energy resources. Energy extraction methods include photovoltaic, vibrational, thermal, and RF. Replacing and maintaining batteries for sensors can be costly and challenging due to their locations or the increasing number of nodes. A feasible solution to increase the lifetime of a wireless sensor node is scavenging energy from ambient energy sources to recharge the battery or to work without batteries [11]. The torque of brushless motors in UAVs varies based on different applications. These motors are powered by batteries, which also supply energy to the associated sensors. However, the operational capabilities of both the drone and its sensors are constrained by the battery’s power supply.

   The major contribution of this paper,

   1. A hybrid energy harvesting model is suggested to gather energy from ambient sources like RF from nearby base stations and motor rotation vibrations. This energy is utilized to power wireless sensor networks on

      The received signal at the UAV due to transmit antennas of the base station in a Simultaneous Wireless Information and Power Transfer (SWIPT) scenario can be expressed as:

      UAVs.

   2. Analysis and design of practical circuits for the proposed hybrid energy harvesting for

      clarification.

   3. A convex optimization problem is formulated to find optimal parameters for both RF and

   The amount of RF energy harvested at the UAV can be expressed as:

   vibration energy harvesting in the hybrid system.

   The rest of this paper is organized as follows: In

   section 2, system model of proposed hybrid energy harvesting in UAV is explained and analytical expression for RF and PEH is discussed. Convex optimization problem is formulated for RF and PEH in section 3. In section 4, the hardware

   The power corresponding to the harvested RF energy can be calculated by considering the energy efficiency factor and the harvested energy is given by

   implementation of the proposed hybrid energy harvesting system model is provided. Results and discussion are addressed in section 5.

2. SYSTEM MODEL

   The block diagram of the proposed hybrid energy harvesting system model in UAV is shown in Figure 2.1. The sensors associated with UAV are operated using separate battery backup. Hybrid energy harvester backups the power required for the sensors in UAV. It consists of four motors M1, M2, M3 and M4 connected to the drone whose vibration yields the variable force required for harvesting energy from vibration with the help of four piezoelectric plates PZ1, PZ2, PZ3 and PZ4. The RF energy harvester harvests ambient RF power from nearby base station. The battery and stabilizer units are embedded to stabilize and store the harvested energy. The sensors like tilt sensos, accelerometers and current sensors utilize the harvested energy.

   Substituting equation (2), which defines the received signal (2) into equation (3), which defines

   , we get:

   2.2 PIEZO ELECTRIC ENERGY HARVESTER (PEH) IN UAV

   The well-known mechanical equation governing the vibrations in a piezoelectric material [4] can be expressed as:

   + + + = (5) where and is the first derivative and second derivative of the displacement. If represents the force applied at the piezoelectric

   element in Newtons, it can be expressed as:

   The voltage generated due to a particular force F applied at the piezoelectric element, considering the resistive load, can be expressed [12] in terms of the angular frequency as follows:

   The harvested power resulting from vibration can be expressed as:

   Fig.2.1 Hybrid Energy Harvesting System Model in UAV

   2.1 RF ENERGY HARVESTING IN UAV

3. OPTIMIZATION PROBLEM FOR ENERGY ARVESTING

   It is hard to estimate the energy harvesting analytically using RF and PEH. The optimization problem for maximizing the harvested power in hybrid energy harvesting is formulated as

4. HARDWARE IMPLEMENTATION OF HYBRID ENERGY HARVESTING

   The theoretical aspects of energy harvester can be validated by the hardware implementation. For RF power harvester, the hardware verification is carried

   out with Joe Tates Ambient Power Module (APM)

   circuit and it is shown in Figure 2.2. The hardware circuit includes four 1N34 germanium diodes, two

   The joint optimization problem described in equation (9) is a quasi-convex problem, and finding the optimal values for the resistance and base- station transmitting power is known to be NP- hard.Hence, the joint optimization problem is reformulated as individual problem to find the optimal solution for and . The optimal value of the quasi-convex problem can be determined by reformulating as feasibility problem given by,

   0.2 mF ceramic capacitors and two 100 mF 50 volts electrolytic capacitors. This APM converts ambient radio frequencies to usable electrical power. The APM is a simple electronic circuit which, when connected to antenna and ground, delivers low voltage in range of several milliwatts. The actual circuit consists of two oppositely polarized Voltage doublet. The DC output of every doublet is connected serial with the opposite to maximize voltage. The antenna module used for APM circuit is RF24L01+transceiver module and it is shown in Figure.2.3. It is operating at 2.4 GHz worldwide ISM frequency band and uses Gaussian Frequency

   Shift Keying (GFSK) modulation for data transmission. The module comes with a SMA

   Subject to

   connector and a duck antenna. It consists of a special RFX2401C chip which integrates the Power

   Equation (10) can be reformulated as a convex constraint.

   find R, Subject to

   Optimization problem is defined as

   find PBS, Subject to

   Amplifier (PA), Low Noise Amplifier (LNA), and transmit receive switching circuitry.

   Fig.2.2. Circuit diagram for RF energy harvesting

   The third optimization problem in PEH (Piezoelectric Energy Harvester) is defined as follows:

   find , Subject to

   The optimization problem in equation (12) and

   (13) are convex.

   system

   The module may achieve a significantly big transmission in a range of approximately 1 km with the aid of a range extender chip and a duck antenna. It takes extremely weak signals from the antenna and amplify it. The antenna module operates at a frequency range of 2.4 GHz ISM band with maximum operating current of 13.5 milli Ampere.

   the output voltage. A boost converter is a DC-to-DC power converter that is used to step-up voltage from its input to output. It is a category of switched-mode power supply (SMPS) containing at minimum of two semiconductors (a diode and a transistor) and at least one energy storage element such as a capacitor or an inductor or the combination of both.

   Fig. 2.3 Antenna Module for APM circuit

   The RF module yields a maximum voltage of 400 mV for a base station transmitting 100 mW at 2.4 GHz.The hardware set up for PEH circuit is shown in Figure2.4. A potential difference is produced when a piezoelectric crystal undergoes mechanical compression, which modifies the dipole moment. Due to the poling voltage, tension perpendicular to the direction of polarization or compression along that direction results in voltage of equivalent polarity. A voltage of the opposite polarity to the poling voltage is produced by tension along the direction of polarization or compression perpendicular to the direction of polarization. This converts the mechanical energy of compression or tension into electrical energy.

   Four piezoelectric plates harvest vibration energy from the drone’s motors, converting it into electrical energy. This energy is then directed to a bridge rectifier circuit for AC to DC conversion. The piezoelectric plate is a device which converts voltage to vibration and vice-versa. The plates used in this project has a resonance frequency of 4.6+/-

   0.5 kHz, resonance impedance of 200 , capacitance of 20 nF +/- 30% at 1 kHz.

   Fig. 2.4. Circuit diagram for PEH.

   4.1 HYBRID ENERGY HARVESTING

   The proposed hybrid energy harvesting system circuit for the UAV is the series connection of the RF and piezo electric energy harvesting circuits. The circuit diagram and block diagram of proposed hybrid energy harvesting model is shown in Figure

   2.5 and Figure 2.6 respectively. The total voltage produced as the output of the hybrid system is the summation of the voltage produced by PEH system and the RF energy harvesting system. The generated voltage is given to a voltage booster circuit to boost

   Fig. 2.5. Circuit diagram for Hybrid energy harvesting system

   Lithium-ion batteries, commonly found in portable electronics and electric vehicles, are gaining popularity in military and aerospace applications. In the hybrid circuit, the voltage (1.5 volts) is elevated using a boost converter to reach up to 5 volts. This increased voltage is used to store the harvested power in a rechargeable battery. This power stored using the rechargeable battery is utilized for sensor applications such as LM94021 temperature sensor, BME280 Humidity and power sensor, VBMA400 ultra power accelerometers etc. This power stored using the rechargeable battery is utilized for sensor applications such as LM94021 temperature sensor (Operating voltage 1.5 V, Range 50 to 150ºC), BME280 Humidity and pressure sensor (Operating voltage 1.71 to 3.6V), VBMA400 ultra low power accelerometer (Operating voltage 1.2 to 3.6V).

5. RESULTS AND DISCUSSIONS

The RF module yields a maximum of 400 mV for a base station transmitting power of 100 Mw at 2.4 GHz. In piezoelectric module, each sensor yields a maximum of 0.6 volts. Therefore, the four piezoelectric sensor yields a maximum voltage of

2.8 volts. The piezoelectric module yields 0.36 mW of maximum power. The hybrid energy harvesting module yields a maximum voltage of 2.8 volts.

Fig.2.6. Block Diagram of UAV with Hybrid Energy harvester.

The power is boosted using voltage booster circuit. The power at the booster output is stored using (3.7 volts 500 mA) rechargeable battery. The piezoelectric device offers high output voltage and capacitance, but it’s costly and its coupling coefficient depends on material properties. On the other hand, the RF device boasts high output current, durability, and longevity. However, it suffers from low output voltage and efficiency. For MATLAB simulation, the simulation parameters assumed are listed in Table 4.1. The harvested power due to RF energy is shown in Figure 4.1. In this figure, the total power in the base station is varied and the harvested power is observed for two different configurations:

= 2, & = 3 and single receive antenna.

Table.4.1 Simulation Parameters

It is observed that the harvested power due to RF

energy is varying with respect to transmit power and channel conditions. It is observed that by increasing the transmit power from 2 to 4 Volts, RF harvested power is increased. But, in the case of 4 to 5 volts, RF harvested power decreases. It is due to the fact that the path loss attenuation and distance increases the available power and rate of charge decreases.

When the transmit antenna at the base station is increased, more power is harvested in UAV. The harvested power due to vibration is shown in Figure.4.2. In this figure, the load resistance of the circuit is varied and the harvested power is observed for three different force values: 5N, 8N and 10N. It is observed that maximum power is harvested when heavy force is excited on the piezo electric sensors. Using equation (11), the optimal value of resistance in vibration energy harvesting is determined using the optimization problem formulated and it is shown in Table.4.2 for different values of total resistance. For varying force level, the optimal resistance will be the same for the given specifications. Using equation (13), the optimal angular frequency values are obtained for different force level and it is shown in Table.4.3. Similarly, the optimal value of base station power in RF energy harvesting is determined using the optimization problem formulated in equation (12) and it is shown in Table.4.3 for different values of base station. A 2D plot for hybrid energy harvester is shown in Figure 4.3. It shows the total harvested power variation with RF and vibration

Table.4.2. Optimal Resistance in PEH

Figure.4.2. Harvested Power in PEH vs Load Resistance

Figure.4.1. Harvested Power in RF vs Total Power in base-station

Table.4.3 Optimal Frequency in PEH

Table.4.4 Optimal base station in RF Energy

Harvesting

V. CONCLUSIONS

A hybrid energy system is implemented into hardware prototype. The harvester consists of a nRF24L01+ transceiver module, SMA connector, a duck- antenna and PZT piezo-electric sensor plates. The proposed system module has a capability to produce output voltage of 2.8V. This hybrid energy harvester could find numerous applications to power the sensors which are placed in UAV. The hybrid energy harvesting model is formulated as convex optimization and the optimal parameters for maximizing the output power are determined as the solution for the convex optimization problem using MATLAB.

REFERENCES

1. L. Zhang et al (2009)., A survey on 5G millimeter wave communications for UAV-assisted wireless networks, IEEE Access, vol. 7, pp. 117460117504.
2. Esma Turgut, M. Cenk Gursoy and Ismail Guvenc (2020),Energy Harvesting in Unmanned Aerial Vehicle Networks With 3D Antenna Radiation Patterns IEEE Transactions on Green Communications and Networking, VOL. 4, NO. 4.
3. Cuong Van Nguyen, Toan Van Quyen, Anh My Le, Linh Hoang Truong, Minh Tuan Nguyen (2020), Advanced Hybrid Energy Harvesting Systems for Unmanned Aerial Vehicles (UAVs), Advances in Science, Technology and Engineering Systems Journal, Vol.5, Issue 1, Page No 34- 39.
4. Anton, S. R. and Sodano, H. A., A review of power harvesting using piezoelectric materials (2007), Smart Materials and Structures, 16(3), R1-R21.
5. Bent, A. A. and Hagood, N. W (1997), Piezoelectric fiber composites with inter digitated electrodes, Journal of Intelligent Material Systems and Structures, 8(11), 903-919.
6. Rossetti, G. A. Jr., Pizzochero, A. and Bent, A. A, (2000) Recent advances in active fiber composites technology, IEEE International Symposium on Applications of Ferroelectrics, 2, 753-756.
7. C. Li, Y. Li, K. Song, L. Yang (2016), Energy efficient design for multiuser downlink energy and uplink information transfer in 5G. Sci. China Inf. Sci. 59(2), 18.
8. K. Song, B. Ji, C. Li, L. Yang (2019), Outage analysis for simultaneous wireless information and power transfer in dual-hop relaying networks. Wireless Networks. Springer, 25(2), 837844.
9. M. Hua, C. Li, Y. Huang, L. Yang, (2017), 9th International Conference on Wireless Communications and Signal Processing (WCSP). Throughput maximization for UAV- enabled wireless power transfer in relaying system, pp. 1 5.
10. S. Yin, Y. Zhao, L. Li, (2018), IEEE International Conference on Communications (ICC). UAV-assisted cooperative communications with time-sharing SWIPT, pp. 16.
11. SalarChamanian, Member, BerkayCiftci,Hasan Ulusan, Ali Muhtaroglu, and HalukKulah, (2019), Power-Efficient Hybrid Energy Harvesting System for Harnessing Ambient Vibrations, IEEE Transactions on Circuits and SystemsI, Vol. 66.
12. Daniel Guyomar, Adrien Badel, Elie Lefeuvre, and Claude Richard (2005), Toward Energy Harvesting Using Active Materials and Conversion Improvement by Nonlinear Processing, IEEE transactions on ultrasonics, ferroelectrics, and frequency control, vol. 52, pp. 584 595.