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 proceedingConference proceeding contributionResearchpeer-review

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.

LanguageEnglish
Title of host publicationProceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019
EditorsHaibin Zhu, Jiacun Wang, MengChu Zhou
Place of PublicationBanff, Canada
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages459-464
Number of pages6
ISBN (Electronic)9781728100838
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event16th IEEE International Conference on Networking, Sensing and Control, ICNSC 2019 - Banff, Canada
Duration: 9 May 201911 May 2019

Conference

Conference16th IEEE International Conference on Networking, Sensing and Control, ICNSC 2019
CountryCanada
CityBanff
Period9/05/1911/05/19

Fingerprint

Adaptive Neuro-fuzzy Inference System
Fuzzy inference
aeronautics
inference
NASA
Satellites
Battery
electric batteries
spacecraft launching
Modeling
Fuzzy Inference System
Neuro-fuzzy
Cell
Launching
Black Box
cells
Clustering algorithms
power supplies
Clustering Algorithm
boxes

Keywords

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

Cite this

Jamshidi, M. B., 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] Banff, Canada: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICNSC.2019.8743333
Jamshidi, Mohammad (Behdad) ; Alibeigi, Neda ; Lalbakhsh, Ali ; Roshani, Saeed. / 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. Banff, Canada : Institute of Electrical and Electronics Engineers (IEEE), 2019. pp. 459-464
@inproceedings{e96e59fdb4bf462d9169c92b6a4c955d,
title = "An ANFIS approach to modeling a small satellite power source of NASA",
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.",
keywords = "ANFIS, Artificial intelligence, Battery, Black-box model, MATLAB, NASA, Satellite, System identification",
author = "Jamshidi, {Mohammad (Behdad)} and Neda Alibeigi and Ali Lalbakhsh and Saeed Roshani",
year = "2019",
doi = "10.1109/ICNSC.2019.8743333",
language = "English",
pages = "459--464",
editor = "Haibin Zhu and Jiacun Wang and MengChu Zhou",
booktitle = "Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
address = "United States",

}

Jamshidi, MB, 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), Banff, Canada, 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; Roshani, Saeed.

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

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionResearchpeer-review

TY - GEN

T1 - An ANFIS approach to modeling a small satellite power source of NASA

AU - Jamshidi, Mohammad (Behdad)

AU - Alibeigi, Neda

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.

AB - 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.

KW - ANFIS

KW - Artificial intelligence

KW - Battery

KW - Black-box model

KW - MATLAB

KW - NASA

KW - Satellite

KW - System identification

UR - http://www.scopus.com/inward/record.url?scp=85068772207&partnerID=8YFLogxK

U2 - 10.1109/ICNSC.2019.8743333

DO - 10.1109/ICNSC.2019.8743333

M3 - Conference proceeding contribution

SP - 459

EP - 464

BT - Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019

A2 - Zhu, Haibin

A2 - Wang, Jiacun

A2 - Zhou, MengChu

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Banff, Canada

ER -

Jamshidi MB, 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. Banff, Canada: Institute of Electrical and Electronics Engineers (IEEE). 2019. p. 459-464. 8743333 https://doi.org/10.1109/ICNSC.2019.8743333