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Measuring battery discharge characteristics for accurate UAV endurance estimation

Part of: Sustainable Aerospace

Published online by Cambridge University Press:  18 February 2020

L. Mariga ,
I. Silva Tiburcio ,
C.A. Martins Open the ORCID record for C.A. Martins [Opens in a new window] ,
A.N. Almeida Prado  and
C. Nascimento Jr.
L. Mariga
Affiliation:
Instituto Tecnológico de Aeronáutica, ITASão José dos Campos, SPBrasil
I. Silva Tiburcio
Affiliation:
Instituto Tecnológico de Aeronáutica, ITASão José dos Campos, SPBrasil
C.A. Martins*
Affiliation:
Instituto Tecnológico de Aeronáutica, ITASão José dos Campos, SPBrasil
A.N. Almeida Prado
Affiliation:
Instituto Tecnológico de Aeronáutica, ITASão José dos Campos, SPBrasil
C. Nascimento Jr.
Affiliation:
Instituto Tecnológico de Aeronáutica, ITASão José dos Campos, SPBrasil
*
[email protected]
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Abstract

The increasing use of unmanned aerial vehicles in areas such as rescue, mapping, and transportation have made it necessary to study more accurate techniques for calculating flight time estimates. Such calculations require knowing the battery discharge profile. Simplified flight time calculation methods provide data with uncertainties as they are based solely on manufacturer datasheet information. This study presents a setup to measure the battery discharge curve using a LabVIEW interface with a low-cost acquisition system. The acquired data passes through a nonlinear optimisation algorithm to find the battery coefficients, which enables the more precise estimation of its range and endurance. The great advantage of this model is that it makes it possible to predict how the battery will discharge at different rates using just one experimental curve. The methodology was applied to three different batteries and the model was validated with different discharge rates in a controlled environment, which resulted in endurance lower than 3.0% for most conditions and voltage estimation error lower than 3.0% in operational voltage. The work also presented a methodology for estimating cruise time based on the current used during each flight stage.

Keywords

Battery dischargeUAVEndurance estimation
Type
Research Article
Information
The Aeronautical Journal , Volume 124 , Issue 1277 , July 2020 , pp. 1099 - 1113
Copyright
© The Author(s) 2020. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

REFERENCES

Traub, L.W.Validation of endurance estimates for battery powered UAVs, The Aeronautical Journal, 2013, 117, (1197), pp 1155–1166.CrossRefGoogle Scholar
Cheng, F., Wang, H. and Cui, P.Rotorcraft flight endurance estimation based on a new battery discharge model, Chinese Journal of Aeronautics, 2017, 30, (4), pp 1561–1569.CrossRefGoogle Scholar
Traub, L.Calculation of constant power lithium battery discharge curves, Batteries, 2016, 2, (2), p 17.CrossRefGoogle Scholar
Thirugnanam, K., Saini, H. and Kumar, P. Mathematical modeling of Li-ion battery for charge/discharge rate and capacity fading characteristics using genetic algorithm approach, 2012 IEEE Transactions on Energy Conversion Expo, ITEC 2012. 2012;(January 2015).CrossRefGoogle Scholar
Liu, J., Gao, D. and Cao, J. Study on the effects of temperature on LiFePO4battery life, Vehicle Power and Propulsion (VPPC), IEEE Conference VPPC 2012. 2012, pp 1436–1440.Google Scholar
Pop, V., Bergveld, H.J., Regtien, P.P.L., Op het Veld, J.H.G., Danilov, D. and Notten, P.H.L.Battery aging and its influence on the electromotive force, Journal of The Electrochemical Society, 2007, 154, (8), p A744.CrossRefGoogle Scholar
Brondani, M.F., Sausen, A.T.Z.R., Sausen, P.S. and Binelo, M.O.Tema, Tendências em Matemática Apl e Comput, 2017, 18, (1), pp 127–137. doi:105540/tema2017018010127 [Internet]. Available from: https://www.mathworks.com/help/physmod/sps/powersys/ref/battery.html;jsessionid=f8e67a8c5812e58af9b6e1cb17ca..CrossRefGoogle Scholar
Omar, N., Monem, M.A., Firouz, Y., Salminen, J., Smekens, J., Hegazy, O., et al. Lithium iron phosphate based battery - Assessment of the aging parameters and development of cycle life model, Applied Energy, 2014, 113, 1575–1585. Available from: http://dx.doi.org/10.1016/j.apenergy.2013.09.003.CrossRefGoogle Scholar
Saw, L.H., Somasundaram, K., Ye, Y. and Tay, A.A.O.Electro-thermal analysis of Lithium Iron Phosphate battery for electric vehicles, Journal of Power Sources, 2014, 249, 231–238.CrossRefGoogle Scholar
Tremblay, O. and Dessaint, L.A.Experimental validation of a battery dynamic model for EV applications, World Electric Vehicle Journal, 2009, 3, (2), 289–298.CrossRefGoogle Scholar
Li, S., Member, S. and Ke, B. Study of battery modeling using mathematical and circuit oriented approaches, 2011 IEEE Power Engineering Society General Meeting, 2011, pp 1–8.CrossRefGoogle Scholar
Raszmann, E., Baker, K., Shi, Y. and Christensen, D. Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control Preprint, 2017, (February).CrossRefGoogle Scholar
Carlberg, K. Lecture 1: Optimization in Matlab. Stanford Univ [Internet]. 2009; Available from: https://www.sandia.gov/~ktcarlb/opt_class/OPT_Matlab.pdf.Google Scholar
Keil, P.Aging of lithium-ion batteries in electric vehicles: Impact of regenerative braking. Vol.7, Aging of Lithium-Ion Batteries in Electric Vehicles, Doctoral Thesis. Technischen Universität München, 2017.CrossRefGoogle Scholar