Predicting Battery Life for Electric UAVs

Saha, Bhaskar; Koshimoto, Edwin; Quach, Cuong C.; Vazquez, Sixto L.; Wright, Stephanie; Hogge, Edward F.; Strom, Thomas H.; Hill, Boyd L.; Goebel, Kai

doi:10.2514/6.2011-1517

Cited by 17 publications

(18 citation statements)

References 3 publications

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“…In fact, it can be seen in Figure 5 that the optimum velocity for this particular quadcopter is around 6 m/s. This plunge in energy consumption is due to a phenomenon called translational lift, which is a characteristic of the rotorcraft [10]. For consistency, an additional six figures that summarize the data from the 23 flights are provided in the Appendix of this paper.…”

Section: Data Collection and Resultsmentioning

confidence: 97%

“…Also, by using higher quality batteries such as the Turnigy Nanotech product line, manufacturing tolerances are more consistent and the higher C rating allows for the battery to be more resistant to voltage sagging [10]. In the future, these batteries will be tested for charge and discharge characteristics at the U.S. Army Research Laboratory's Communications-Electronics Research, Development and Engineering Center (CERDEC).…”

Section: Figure 1 3d Robotics and Dji Test Vehiclesmentioning

confidence: 98%

“…Additionally, six identical Turnigy Nanotech 4,000 mAh 4-cell 25-50 C lithium polymer batteries (LiPo) [10], shown in Figure 2, were procured to reduce variance in battery energy output due to different brands. Also, by using higher quality batteries such as the Turnigy Nanotech product line, manufacturing tolerances are more consistent and the higher C rating allows for the battery to be more resistant to voltage sagging [10].…”

Section: Figure 1 3d Robotics and Dji Test Vehiclesmentioning

confidence: 99%

“…Therefore, a BMS can improve the flight time of a UAS, since it can provide a better estimate of the available capacity and allows the vehicle to fly longer (and perhaps more efficiently), depending on the load requirements. Examples of common techniques used for this purpose include application of the extended Kalman filter and Bayesian inference frameworks [10,11].…”

Section: A Brief Introductionmentioning

confidence: 99%

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LiPo battery energy studies for improved flight performance of unmanned aerial systems

et al. 2016

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Energy storage is one of the most important determinants of how long and far a small electric powered unmanned aerial system (UAS) can fly. For years, most hobby and experimentalists used heavy fuels to power small drone-like systems. Electric motors and battery storage prior to the turn of the century were either too heavy or too inefficient for flight times of any usable duration. However, with the availability of brushless electric motors and lithium-based batteries everything has changed. Systems like the Dragon Eye, Pointer, and Raven are in service performing reconnaissance, intelligence, surveillance, and target acquisition (RISTA) for more than an hour at a time. More recently, multi-rotor vehicles have expanded small UAS capabilities to include activities with hovering and persistent surveillance. Moreover, these systems coupled with the surge of small, low-cost electronics can perform autonomous and semi-autonomous missions not possible just ten years ago. This paper addresses flight time limitation issues by proposing an experimental method with procedures for system identification that may lead to modeling of energy storage in electric UAS'. Consequently, this will allow for energy storage to be used more effectively in planning autonomous missions. To achieve this, a set of baseline experiments were designed to measure the energy consumption of a mid-size UAS multi-rotor. Several different flight maneuvers were considered to include different lateral velocities, climbing, and hovering. Therefore, the goal of this paper is to create baseline flight data for each maneuver to be characterized with a certain rate of energy usage. Experimental results demonstrate the feasibility and robustness of the proposed approach. Future work will include the development of mission planning algorithms that provide realistic estimates of possible mission flight times and distances given specific mission parameters.Keywords: Battery energy management, flight performance, modeling, unmanned aerial system A BRIEF INTRODUCTIONLithium-based batteries present advantageous characteristics, such as high charge and discharge rates, longevity, high energy density, and affordable cost accompanied with a lightweight structure [1,2,3,4]. Nonetheless, only small improvements in lithium-based batteries have occurred over the past ten years with the biggest improvement being the reduction of price. Even with the most advanced lithium-based batteries, electric multi-rotor UAS mission plans are often dictated by the flight time (and consequently path) of the vehicle [5]. In most scenarios, this is generally limited to approximately thirty minutes before battery protection measures activate and the craft must land. However, lithium-based batteries are still affected by charge and discharge rates, life cycles, temperature, and their capacity rating [1]. These issues need to be considered when operating battery powered unmanned air systems. Currently, lithium-based battery characteristics offer the best power supply for small UAS' where r...

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Section: Data Collection and Resultsmentioning

confidence: 97%

Section: Figure 1 3d Robotics and Dji Test Vehiclesmentioning

confidence: 98%

Section: Figure 1 3d Robotics and Dji Test Vehiclesmentioning

confidence: 99%

Section: A Brief Introductionmentioning

confidence: 99%

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LiPo battery energy studies for improved flight performance of unmanned aerial systems

et al. 2016

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“…Although prognostics is used in many engineering systems and products including batteries [70,71,72,73,74,75,76,77], to assess the remaining useful 220 life and enhance the durability and reliability, prognostics is rarely discussed with respect to fuel cells. Jouin et al (2013aJouin et al ( , 2013b) discussed the benefits of applying the prognostics techniques to monitor the SOH and estimate the RUL of PEMFC, aiming at improving their durability and reliability, and hence extend their life spans [78,79].…”

Section: Applications Of Prognostics To Pemfcmentioning

confidence: 99%

A review on prognostics and health monitoring of proton exchange membrane fuel cell

Sutharssan

Montalvão

Chen

et al. 2017

Renewable and Sustainable Energy Reviews

195

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Fuel cell technology can be traced back to 1839 when British scientist Sir William Grove discovered that it was possible to generate electricity by the reaction between hydrogen and oxygen gases. However, fuel cell still cannot compete with internal combustion engines although they have many advantages including zero carbon emissions. Fossil fuels are cheaper and present very high volumetric energy densities compared with the hydrogen gas. Furthermore, hydrogen storage as a liquid is still a huge challenge. Another important disadvantage is the lifespan of the fuel cell because of their durability, reliability and maintainability.Prognostics is an emerging technology in sustainability of engineering systems through failure prevention, reliability assessment and remaining useful lifetime estimation. Prognostics and health monitoring can play a critical role in enhancing the durability, reliability and maintainability of the fuel cell system. This paper presents a review on the current state-of-the-art in prognostics and health monitoring of Proton Exchange Membrane Fuel Cell (PEMFC), aiming at identifying research and development opportunities in these fields. This paper also highlights the importance of incorporating prognostics and failure modes, mechanisms and effects analysis (FMMEA) in PEMFC to give them sustainable * Corresponding author Email address: (tsutharssan@iee.org) (Thamo Sutharssan)Preprint submitted to Renewable & Sustainable Energy Reviews November 14, 2016 competitive advantage when compared with other non-clean energy solutions.

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A Bayesian Approach to Battery Prognostics and Health Management

Zhou²,

Han³

2016

Advances in Battery Manufacturing, Service, and Management Systems

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Predicting Battery Life for Electric UAVs

Cited by 17 publications

References 3 publications

LiPo battery energy studies for improved flight performance of unmanned aerial systems

LiPo battery energy studies for improved flight performance of unmanned aerial systems

A review on prognostics and health monitoring of proton exchange membrane fuel cell

A Bayesian Approach to Battery Prognostics and Health Management

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