An ANFIS approach to modeling a small satellite power source of NASA

Mohammad (Behdad) Jamshidi; Neda Alibeigi; Ali Lalbakhsh; Saeed Roshani

doi:10.1109/ICNSC.2019.8743333

An ANFIS approach to modeling a small satellite power source of NASA

Mohammad (Behdad) Jamshidi, Neda Alibeigi, Ali Lalbakhsh, Saeed Roshani

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

12 Citations (Scopus)

Abstract

Before launching satellites into space, a wide variety of practical and comprehensive tests must be done on their different subsystems. Because, the cost spent for designing and manufacturing satellites is much higher than these investigations. One of the prominent sectors of these devices is their power supplies. In this paper, a neuro-fuzzy based black-box technique for modeling a li-ion battery used in a small satellite of the National Aeronautics and Space Administration (NASA) is presented. The dataset was extracted from a range of particular tests on 18650 lithium-ion cells by scientists of NASA. The proposed approach includes an Adaptive Neuro-Fuzzy Inference System (ANFIS) model with a Fuzzy Inference System (FIS) generated by a subtractive clustering algorithm to estimate and predict the capacity of the cell for next cycles. The results indicated the proposed method can be considered an efficient and reliable technique for estimating parameters of batteries.

Original language	English
Title of host publication	Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019
Editors	Haibin Zhu, Jiacun Wang, MengChu Zhou
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	459-464
Number of pages	6
ISBN (Electronic)	9781728100838
DOIs	https://doi.org/10.1109/ICNSC.2019.8743333
Publication status	Published - 2019
Externally published	Yes
Event	16th IEEE International Conference on Networking, Sensing and Control, ICNSC 2019 - Banff, Canada Duration: 9 May 2019 → 11 May 2019

Conference

Conference	16th IEEE International Conference on Networking, Sensing and Control, ICNSC 2019
Country/Territory	Canada
City	Banff
Period	9/05/19 → 11/05/19

Keywords

ANFIS
Artificial intelligence
Battery
Black-box model
MATLAB
NASA
Satellite
System identification

Access to Document

10.1109/ICNSC.2019.8743333

Cite this

Jamshidi, M., Alibeigi, N., Lalbakhsh, A., & Roshani, S. (2019). An ANFIS approach to modeling a small satellite power source of NASA. In H. Zhu, J. Wang, & M. Zhou (Eds.), Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019 (pp. 459-464). [8743333] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICNSC.2019.8743333

Jamshidi, Mohammad (Behdad) ; Alibeigi, Neda ; Lalbakhsh, Ali et al. / An ANFIS approach to modeling a small satellite power source of NASA. Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019. editor / Haibin Zhu ; Jiacun Wang ; MengChu Zhou. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2019. pp. 459-464

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abstract = "Before launching satellites into space, a wide variety of practical and comprehensive tests must be done on their different subsystems. Because, the cost spent for designing and manufacturing satellites is much higher than these investigations. One of the prominent sectors of these devices is their power supplies. In this paper, a neuro-fuzzy based black-box technique for modeling a li-ion battery used in a small satellite of the National Aeronautics and Space Administration (NASA) is presented. The dataset was extracted from a range of particular tests on 18650 lithium-ion cells by scientists of NASA. The proposed approach includes an Adaptive Neuro-Fuzzy Inference System (ANFIS) model with a Fuzzy Inference System (FIS) generated by a subtractive clustering algorithm to estimate and predict the capacity of the cell for next cycles. The results indicated the proposed method can be considered an efficient and reliable technique for estimating parameters of batteries.",

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author = "Jamshidi, {Mohammad (Behdad)} and Neda Alibeigi and Ali Lalbakhsh and Saeed Roshani",

year = "2019",

doi = "10.1109/ICNSC.2019.8743333",

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booktitle = "Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019",

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Jamshidi, M, Alibeigi, N, Lalbakhsh, A & Roshani, S 2019, An ANFIS approach to modeling a small satellite power source of NASA. in H Zhu, J Wang & M Zhou (eds), Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019., 8743333, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 459-464, 16th IEEE International Conference on Networking, Sensing and Control, ICNSC 2019, Banff, Canada, 9/05/19. https://doi.org/10.1109/ICNSC.2019.8743333

An ANFIS approach to modeling a small satellite power source of NASA. / Jamshidi, Mohammad (Behdad); Alibeigi, Neda; Lalbakhsh, Ali et al.

Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019. ed. / Haibin Zhu; Jiacun Wang; MengChu Zhou. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 459-464 8743333.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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AU - Lalbakhsh, Ali

AU - Roshani, Saeed

PY - 2019

Y1 - 2019

N2 - Before launching satellites into space, a wide variety of practical and comprehensive tests must be done on their different subsystems. Because, the cost spent for designing and manufacturing satellites is much higher than these investigations. One of the prominent sectors of these devices is their power supplies. In this paper, a neuro-fuzzy based black-box technique for modeling a li-ion battery used in a small satellite of the National Aeronautics and Space Administration (NASA) is presented. The dataset was extracted from a range of particular tests on 18650 lithium-ion cells by scientists of NASA. The proposed approach includes an Adaptive Neuro-Fuzzy Inference System (ANFIS) model with a Fuzzy Inference System (FIS) generated by a subtractive clustering algorithm to estimate and predict the capacity of the cell for next cycles. The results indicated the proposed method can be considered an efficient and reliable technique for estimating parameters of batteries.

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KW - Battery

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ER -

Jamshidi M, Alibeigi N, Lalbakhsh A, Roshani S. An ANFIS approach to modeling a small satellite power source of NASA. In Zhu H, Wang J, Zhou M, editors, Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). 2019. p. 459-464. 8743333 https://doi.org/10.1109/ICNSC.2019.8743333

An ANFIS approach to modeling a small satellite power source of NASA

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