## 1 Introduction  
Numerous applications can potentially benefit from the use of drones. This has attracted a lot of research related to drones. Drone based delivery services continue to fascinate us. Such a drone based delivery service is already in use for the delivery of medical supplies [1]. There has been some work related to completing autonomous drone delivery missions when there is communication failure [2]. Researchers have also worked with drone delivery scheduling [3], battery charging issues, battery assignment [4], routing issues [5] and also a surveillance network [6] for delivery drones. However, as pointed out in [5], the problem of routing delivery drones is not trivial, because several issues, like flight distance, power usage, and payload weight, need to be considered. Because of their high energy density [7], Li-ion batteries are a natural choice for use onboard drones. However, drone flight times still continue to be short, because of the high current required for flying. A high current discharge from a battery can increase its temperature [8]. An increased battery temperature can lead to a decrease in battery life. Further, if the temperature of a Li-ion battery reaches around 60 \({}^{\circ}\)C, then there is a risk of thermal runaway, which may lead to an explosion and fire [8]. Motivated by the need to combat such issues, researchers have developed models and techniques for predicting battery behavior [9; 10; 11; 12; 13; 14; 15; 16; 17]. Methods to determine parameters of a thermal model of a Li-ionbattery were reviewed in [9]. The authors in [10] combined impedance-based and electrochemical techniques to examine the thermal effects on charging a battery. Fast thermal runaway detection was addressed in [11]. Battery models that focus on thermal behavior were described in [12; 13; 14]. Literature also exists on special techniques for Li-ion battery model parameter estimation, which can reduce the number of experiments required for estimating battery parameters [15]. Observers have also been used to estimate internal states of batteries [16]. Detecting battery terminal voltage collapse without the need of an accurate model was investigated in [17]. Instead of using models to predict battery behavior, and thus avoiding the possibility of thermal runaway due to a rise in battery temperature on account of high current loading, an alternative approach may consist of reducing the load current that needs to be supplied by a battery. This has been investigated for unmanned aerial vehicles (UAVs), and drones, by incorporating a second onboard energy source, e.g., a fuel cell. Flight times can be improved by employing fuel cells onboard drones [18]. Literature related to using fuel cells onboard UAVs exists [19; 20]. However, the usage of fuel cells onboard large fixed wing UAVs may prove easier than on small quadrotor-based drones, because a larger vehicle has more space available to accommodate the fuel tanks required along with the fuel cell. Further, as mentioned in [18], a fuel cell system to be used on a drone needs to be sophisticated, i.e., lightweight and compact. Using such a sophisticated fuel cell system may increase flight time, and such a specialized product is usually prohibitively expensive. Thus, this paper proposes the use of a low capacity, low cost, metal hydride fuel stick-powered fuel cell onboard a drone, in addition to a Li-ion battery pack. The low capacity metal hydride fuel stick-powered fuel cell stack was chosen because (i) it is much less expensive compared to a sophisticated aerospace grade fuel cell stack; (ii) being low capacity, it is compact and light in weight; (iii) the metal hydride fuel stick cools as it supplies the fuel required for the fuel cell stack to operate; and (iv) the low capacity fuel cell stack can act as a backup power source to keep the microcomputer and communication circuitry onboard a drone alive for a longer time, even when the main batteries which supply the motor are completely drained.