Energy Efficiency Optimization in UAVs: a review

In recent years, development of Unmanned Aerial Vehicles (UAV) has become a significant growing segment of the global aviation industry. The present paper provides an overview of the research conducted on the field of UAV energy efficiency optimization. Introduction Efficient energy utilization on an UAV is essential to its functioning, often needed to achieve the operational goals of range, endurance and other specific mission requirements. The considerable amount of data produced by the UAVs requires high data rate connectivity such as that offered by free space optical (FSO) communication. When using FSO links some important issues need be considered in power consumption for pointing, acquisition and tracking (PAT) subsystem of the FSO. Due to the limitations of the space available and the mass budget on the UAV, it is often a delicate balance between the onboard energy available (i.e. fuel) and the achievement of the operational goals [1]. Three methods of achieving energy efficiency onboard the UAV are encountered in the relevant literature, namely: o optimization of mission waypoints o use of a Hybrid-Electric Propulsion System onboard the UAV. o use of effective power management systems. Mission Waypoint Optimization One of the challenges in the control of UAVs is to make them autonomous or semi-autonomous in order to relieve the operator from the constant monitoring. One such application is the area coverage, where the task is to find the minimal route that connects the defined set of waypoints. Both deterministic and non-deterministic methods have been applied for the solution of the trajectory optimization problem: Ant Colony Optimization [2], Mixed Integer Linear Programming [3] [4], Evolutionary Algorithms [5][6], Genetic Algorithms [7]-[9], Stochastic Sampling [10], Particle Swarm [11] as well as Neural Networks [12]. Exploitation of wind energy in order to optimize the flight trajectory is also encountered in the relevant literature. Several studies suggest that the performance of UAVs may be considerably improved by utilizing natural resources, especially wind energy, during flights. The key challenge of exploiting wind energy in practical UAV operations lies in the availability of reliable and timely wind field information in the operational region. In [13], a strategy that combines wind measurement and optimal trajectory planning onboard UAVs is proposed and explored. During a cycle of the flight, a UAV can take measurements of wind velocity components over the flight region, use these measurements to estimate the local wind field through a model-based approach, and then compute a flight trajectory for the next flight cycle with the objective of optimizing fuel. As the UAV follows the planned trajectory, it continues to measure the wind components and repeats the process of updating the wind model with new estimations and planning optimal trajectories for the next flight cycle. A methodology to generate optimal trajectories that utilize the vertical component of wind to enable flights that would otherwise be impossible given the performance constraints of the UAV, is also presented in [14]. Reference [15] adopts a network modeling approach to formulate the problem of finding minimum energy flight paths. The relevant airspace is divided into small regions using a grid of nodes, inter-connected by arcs. A function, representing energy cost, is defined on every arc in terms of the solution of a constrained nonlinear program for the optimal local airspeed to fly in a given wind field. Then, shortest-path models are implemented on the network to find the optimal paths from an origin to a destination. A Gaussian distribution is used in [16] in order to determine uncertainty in the time-varying wind fields. Next, a Markov Decision Process is used to plan a path based upon the uncertainty of Gaussian distribution. This technique provides not only an effective energy-path planning method which can effectively exploits the wind field, but also a robust flight path. Apart from vertical wind components, other atmospheric effects as thermal gusts and wind gradients represent significant sources of energy that an aircraft can potentially tap to increase endurance and range. In [17], the feasibility of improving UAV mission effectiveness by extracting energy from the atmosphere is explored. Specifically an aerial surveillance mission in the vicinity of a geographic ridge is considered. Cross winds blowing over the ridge produce regions of lift on the windward side that can be exploited to increase mission duration. In [18], solar and piezoelectric energy harvesting techniques are selected and integrated into UAVs. The analysis showed that the UAV with energy harvesting generated less entropy. Moreover, it was demonstrated that the addition of the solar and piezoelectric devices would supply usable power for charging batteries and sensors and that it would be advantageous to implement them into a small UAV. In [19], a receding horizon controller which computes a sequence of pitch rate commands with the goal of maximizing energy gain over a fixed horizon is derived. An energy based reward function is used to maximize energy gain with only local knowledge of atmospheric wind conditions. The results show that the controller is effective in maximizing energy gained from the surrounding air, resulting in altitude or velocity gain. Hybrid-Electric Propulsion System Efficient energy utilisation on an UAV is essential to its functioning, often required to achieve the operational goals of range, endurance and other specific mission requirements. Due to the limitations of the space available and the mass budget on the UAV, there is often a need to compromise between the onboard energy available (i.e. fuel) and achieving the operational goals. One technology with potential in this area is the use of Hybrid-Electric Propulsion System (HEPS) [20]. Hybrid technology combines the advantages of two or more power sources to create a more efficient propulsion system for a vehicle. While many variants of hybrid systems are available today, most derive from three basic categories: series, parallel and power-split. While most systems utilize an internal combustion engine as the primary power source, others use fuel cells or turbine engines. Each system has unique advantages and disadvantages adaptable to the specific needs of a vehicle [21]. Series Configuration. In a series hybrid configuration (Fig. 1(a)), the primary propulsion source is an electric motor (EM). Typically, an internal combustion engine (ICE) drives a generator, which then provides power to the motor and an energy storage system. As the combustion engine is not mechanically linked to the driveshaft, it is able to operate at its optimum torque and speed range independent of power demand. Excess energy from the generator may be stored in a battery, capacitor or flywheel for high demand operation [21]. Large vehicles, like buses and locomotives, are the most common use for this type of configuration [22]. Parallel Configuration. Parallel hybrid-electric propulsion systems (Fig. 1(b)) are beneficial for small UAV. The benefits include increased time on station and range as compared to electricpowered UAV and reduced acoustic and thermal signatures not available with gasoline-powered unmanned aerial vehicles [23]. Moreover, the parallel HEPS configuration enables the powering of the UAV using the ICE alone, the EM alone, or a combination of both power plants depending on the operating conditions. This results in the advantage of redundancy, which is important in both civilian and military applications [24].