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Lityum Bataryalarda Şarj Durumu (SoC) ve Sağlık Durumu (SoH) Kestirimi

Year 2023, Volume: 5 Issue: 1, 33 - 51, 30.06.2023

Gökhan Sevim Necmi Altın

https://doi.org/10.55213/kmujens.1250621

Abstract

Bataryalar kimyasal yapılarına göre çeşitlilik gösterse de genel kullanım amaçları enerjiyi depo etmektir. Kullanıcı konforu ve bataryayı daha verimli kullanabilmek için batarya durumlarını tahmin etmek önemlidir. Bu çalışmada lityum bazlı bir bataryanın şarj durumu (SoC) ve sağlık durumu (SoH) Kalman filtresi yardımı ile kestirilmeye çalışılmıştır. Geliştirilen yöntem ile MATLAB programında oluşturulan bir batarya modelinin SoC ve SoH değerleri kestirilmiştir. MATLAB/Simulink’de var olan bataryanın SoC değeri önerilen yöntem ile kestirilmiş ve Simulink modelinin verdiği değer ile karşılaştırılmıştır. Yapılan benzetim çalışmalarında önerilen yöntem ile elde edilen değer ile Simulink modelinin verdiği değer arasındaki hata değerinin maksimum ±0.03 olduğu tespit edilmiştir. Benzer şekilde SoH kestirimi ile elde edilen değerin maksimum sapma miktarının ±0.03 olması beklenmektedir.

Keywords

Şarj durumu Batarya ömrü Kestirim Kalman filtre

Supporting Institution

Gazi Üniversitesi BAP Koordinasyon Birimi

Project Number

FYL-2022-7597

Thanks

Bu çalışma Gazi Ünizersitesi Bilimsel Araşatırma Projeleri ve Koordinasyon Birimi tarafından FYL-2022-7597 nolu proje kapsamında desteklenmiştir.

References

  • Andre D., Appel C., Soczka-Guth T., Sauer DU., Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries, Journal of power sources, 224, 20-27, (2013).
  • Andrea D., Battery management systems for large lithium-ion battery packs, Artech house, (2010).
  • Chang WY.,The state of charge estimating methods for battery: A review, International Scholarly Research Notices, 2013, (2013).
  • Çayıroğlu İ., Kalman filtresi ve programlama, Fen ve Teknoloji Bilgi Paylaşımı, 1, (2012).
  • Fu B., Wang W., Li Y., Peng Q., An improved neural network model for battery smarter state-of-charge estimation of energy-transportation system, Green Energy and Intelligent Transportation, 100067, (2023).
  • Fu Y., Fu H., A Self-calibration SOC Estimation Method for Lithium-ion Battery, IEEE Access, (2023).
  • Hansen, T., Wang CJ., Support vector based battery state of charge estimator, Journal of Power Sources, 141(2), 351-358, (2005).
  • He H., Xiong R., Fan, J., Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach. energies, 4(4), 582-598, (2011).
  • Jafari H., Rahimpour MR., Pb acid batteries. Rechargeable Batteries: History, Progress and Applications, 17-39, (2020).
  • Linda O., William EJ., Huff M., Manic M., Gupta V., Nance J., Govar J., Intelligent neural network implementation for SOCI development of Li/CFx batteries, In 2009 2nd International Symposium on Resilient Control Systems (pp. 57-62), IEEE, (2009).
  • Ng KS., Moo CS., Chen YP., Hsieh YC., State-of-charge estimation for lead-acid batteries based on dynamic open-circuit voltage, In 2008 IEEE 2nd International Power and Energy Conference (pp. 972-976), IEEE, (2008).
  • Ng KS., Moo CS., Chen YP., Hsieh YC., Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries, Applied energy, 86(9), 1506-1511, (2009).
  • Özcan ÖF., Karadağ T., Altuğ M., Özgüven Ö., Elektrikli Araçlarda Kullanılan Pil Kimyasallarının Özellikleri ve Üstün Yönlerinin Kıyaslanması Üzerine Bir Derleme Çalışması, Gazi University Journal of Science Part A: Engineering and Innovation, 8(2), 276-298, (2021).
  • Sato S., Kawamura A., A new estimation method of state of charge using terminal voltage and internal resistance for lead acid battery, In Proceedings of the Power Conversion Conference-Osaka 2002 (Cat. No. 02TH8579) (Vol. 2, pp. 565-570), IEEE, (2002).
  • Shrivastava P., Soon TK., Idris MYIB., Mekhilef S., Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries, Renewable and Sustainable Energy Reviews, 113, 109233, (2019).
  • Singh P., Vinjamuri R., Wang X., Reisner D., Design and implementation of a fuzzy logic-based state-of-charge meter for Li-ion batteries used in portable defibrillators. Journal of power sources, 162(2), 829-836, (2006).
  • Tezde Eİ., Okumuş Hİ., Batarya modelleri ve şarj durumu (SoC) belirleme, EMO Bilimsel Dergi, 8(1), 21-25, (2018).
  • Topan PA., Ramadan MN., Fathoni G., Cahyadi AI., Wahyunggoro O., State of Charge (SOC) and State of Health (SOH) estimation on lithium polymer battery via Kalman filter, In 2016 2nd International Conference on Science and Technology-Computer (ICST) (pp. 93-96), IEEE, (2016).
  • Wang N., Xia X., Zeng X., State of charge and state of health estimation strategies for lithium-ion batteries, International Journal of Low-Carbon Technologies, ctad032, (2023).
  • Watrin N., Blunier B., Miraoui A., Review of adaptive systems for lithium batteries state-of-charge and state-of-health estimation, In 2012 IEEE Transportation Electrification Conference and Expo (ITEC) (pp. 1-6), IEEE, (2012).
  • Xing L., Luo W., Liu X., Xiang B., SOC Estimation for Lithium Batteries Based on Fractional Order Model and Robust Unscented Kalman Filter, (2023).

Year 2023, Volume: 5 Issue: 1, 33 - 51, 30.06.2023

Gökhan Sevim Necmi Altın

https://doi.org/10.55213/kmujens.1250621

Abstract

Keywords

Şarj durumu Batarya ömrü Kestirim Kalman filtre

Project Number

FYL-2022-7597

References

  • Andre D., Appel C., Soczka-Guth T., Sauer DU., Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries, Journal of power sources, 224, 20-27, (2013).
  • Andrea D., Battery management systems for large lithium-ion battery packs, Artech house, (2010).
  • Chang WY.,The state of charge estimating methods for battery: A review, International Scholarly Research Notices, 2013, (2013).
  • Çayıroğlu İ., Kalman filtresi ve programlama, Fen ve Teknoloji Bilgi Paylaşımı, 1, (2012).
  • Fu B., Wang W., Li Y., Peng Q., An improved neural network model for battery smarter state-of-charge estimation of energy-transportation system, Green Energy and Intelligent Transportation, 100067, (2023).
  • Fu Y., Fu H., A Self-calibration SOC Estimation Method for Lithium-ion Battery, IEEE Access, (2023).
  • Hansen, T., Wang CJ., Support vector based battery state of charge estimator, Journal of Power Sources, 141(2), 351-358, (2005).
  • He H., Xiong R., Fan, J., Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach. energies, 4(4), 582-598, (2011).
  • Jafari H., Rahimpour MR., Pb acid batteries. Rechargeable Batteries: History, Progress and Applications, 17-39, (2020).
  • Linda O., William EJ., Huff M., Manic M., Gupta V., Nance J., Govar J., Intelligent neural network implementation for SOCI development of Li/CFx batteries, In 2009 2nd International Symposium on Resilient Control Systems (pp. 57-62), IEEE, (2009).
  • Ng KS., Moo CS., Chen YP., Hsieh YC., State-of-charge estimation for lead-acid batteries based on dynamic open-circuit voltage, In 2008 IEEE 2nd International Power and Energy Conference (pp. 972-976), IEEE, (2008).
  • Ng KS., Moo CS., Chen YP., Hsieh YC., Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries, Applied energy, 86(9), 1506-1511, (2009).
  • Özcan ÖF., Karadağ T., Altuğ M., Özgüven Ö., Elektrikli Araçlarda Kullanılan Pil Kimyasallarının Özellikleri ve Üstün Yönlerinin Kıyaslanması Üzerine Bir Derleme Çalışması, Gazi University Journal of Science Part A: Engineering and Innovation, 8(2), 276-298, (2021).
  • Sato S., Kawamura A., A new estimation method of state of charge using terminal voltage and internal resistance for lead acid battery, In Proceedings of the Power Conversion Conference-Osaka 2002 (Cat. No. 02TH8579) (Vol. 2, pp. 565-570), IEEE, (2002).
  • Shrivastava P., Soon TK., Idris MYIB., Mekhilef S., Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries, Renewable and Sustainable Energy Reviews, 113, 109233, (2019).
  • Singh P., Vinjamuri R., Wang X., Reisner D., Design and implementation of a fuzzy logic-based state-of-charge meter for Li-ion batteries used in portable defibrillators. Journal of power sources, 162(2), 829-836, (2006).
  • Tezde Eİ., Okumuş Hİ., Batarya modelleri ve şarj durumu (SoC) belirleme, EMO Bilimsel Dergi, 8(1), 21-25, (2018).
  • Topan PA., Ramadan MN., Fathoni G., Cahyadi AI., Wahyunggoro O., State of Charge (SOC) and State of Health (SOH) estimation on lithium polymer battery via Kalman filter, In 2016 2nd International Conference on Science and Technology-Computer (ICST) (pp. 93-96), IEEE, (2016).
  • Wang N., Xia X., Zeng X., State of charge and state of health estimation strategies for lithium-ion batteries, International Journal of Low-Carbon Technologies, ctad032, (2023).
  • Watrin N., Blunier B., Miraoui A., Review of adaptive systems for lithium batteries state-of-charge and state-of-health estimation, In 2012 IEEE Transportation Electrification Conference and Expo (ITEC) (pp. 1-6), IEEE, (2012).
  • Xing L., Luo W., Liu X., Xiang B., SOC Estimation for Lithium Batteries Based on Fractional Order Model and Robust Unscented Kalman Filter, (2023).
There are 21 citations in total.

Details

Primary Language Turkish
Subjects Electrical Engineering
Journal Section Research Articles
Authors

Gökhan Sevim 0000-0002-9585-3756

Necmi Altın 0000-0003-3294-9782

Project Number FYL-2022-7597
Publication Date June 30, 2023
Submission Date February 15, 2023
Published in Issue Year 2023 Volume: 5 Issue: 1

Cite

APA Sevim, G., & Altın, N. (2023). Lityum Bataryalarda Şarj Durumu (SoC) ve Sağlık Durumu (SoH) Kestirimi. Karamanoğlu Mehmetbey Üniversitesi Mühendislik Ve Doğa Bilimleri Dergisi, 5(1), 33-51. https://doi.org/10.55213/kmujens.1250621
AMA Sevim G, Altın N. Lityum Bataryalarda Şarj Durumu (SoC) ve Sağlık Durumu (SoH) Kestirimi. KMUJENS. June 2023;5(1):33-51. doi:10.55213/kmujens.1250621
Chicago Sevim, Gökhan, and Necmi Altın. “Lityum Bataryalarda Şarj Durumu (SoC) Ve Sağlık Durumu (SoH) Kestirimi”. Karamanoğlu Mehmetbey Üniversitesi Mühendislik Ve Doğa Bilimleri Dergisi 5, no. 1 (June 2023): 33-51. https://doi.org/10.55213/kmujens.1250621.
EndNote Sevim G, Altın N (June 1, 2023) Lityum Bataryalarda Şarj Durumu (SoC) ve Sağlık Durumu (SoH) Kestirimi. Karamanoğlu Mehmetbey Üniversitesi Mühendislik ve Doğa Bilimleri Dergisi 5 1 33–51.
IEEE G. Sevim and N. Altın, “Lityum Bataryalarda Şarj Durumu (SoC) ve Sağlık Durumu (SoH) Kestirimi”, KMUJENS, vol. 5, no. 1, pp. 33–51, 2023, doi: 10.55213/kmujens.1250621.
ISNAD Sevim, Gökhan - Altın, Necmi. “Lityum Bataryalarda Şarj Durumu (SoC) Ve Sağlık Durumu (SoH) Kestirimi”. Karamanoğlu Mehmetbey Üniversitesi Mühendislik ve Doğa Bilimleri Dergisi 5/1 (June 2023), 33-51. https://doi.org/10.55213/kmujens.1250621.
JAMA Sevim G, Altın N. Lityum Bataryalarda Şarj Durumu (SoC) ve Sağlık Durumu (SoH) Kestirimi. KMUJENS. 2023;5:33–51.
MLA Sevim, Gökhan and Necmi Altın. “Lityum Bataryalarda Şarj Durumu (SoC) Ve Sağlık Durumu (SoH) Kestirimi”. Karamanoğlu Mehmetbey Üniversitesi Mühendislik Ve Doğa Bilimleri Dergisi, vol. 5, no. 1, 2023, pp. 33-51, doi:10.55213/kmujens.1250621.
Vancouver Sevim G, Altın N. Lityum Bataryalarda Şarj Durumu (SoC) ve Sağlık Durumu (SoH) Kestirimi. KMUJENS. 2023;5(1):33-51.
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