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Elektrikli Araçlar için Lityum İyon Bataryalarda Sağlık Ömrünün Belirlenmesi

Year 2023, Volume: 2023 Issue: 19, 54 - 63, 03.01.2024

Mustafa Eker Emrah Eser İlker Günay

Abstract

Modern elektrikli araçlarda yaygın olarak kullanılan Lityum İyon Bataryalar (LİB), yüksek enerji yoğunluğu, hafif ve pratik hızlı şarj özelliklere sahiptir. Elektrikli araçlar alanında LİB’ler yalnızca enerji depolamaya yönelik bileşen değil, aynı zamanda sürüş performansının ve genel araç verimliliğinin önemli belirleyicilerindendir. Bataryanın sağlığı, sürüş menzili, şarj süresi ve elektrikli araçların genel ömrü gibi önemli hususları doğrudan etkiler. Optimum batarya sağlığını korumak, sürdürülebilir araç performansı sağlarken aynı zamanda daha az batarya değişimi yoluyla çevresel etkiyi azaltmaktır. Bu da enerji depolama sistemlerinin daha geniş sürdürülebilirliğine katkıda bulunmaktadır. Bu makalede LİB’lerin Batarya Sağlık Durumunu (SOH) tahmin etmenin avantajlarının altı çizilmektedir. Bu nedenle SOH belirlenmesi için farklı tahminleme yöntemleri sunulmuştur. Bu yöntemler NASA tarafından paylaşılan batarya gruplarına ait şarj, deşarj ve batarya iç dirençlerine ait veri setlerine uygulanmıştır. Elde sonuçlar her bir batarya türü için ayrı ayrı verilmiştir. Daha sonra elde edilen veriler karşılaştırmalı olarak sunulmuştur.

Keywords

Elektrikli araçlar Lityum iyon bataryalar

References

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  • [3] J. Salminen, T. Kallio, N. Omar, P. Van den Bossche, J. Van Mierlo, and H. Gualous, “Transport Energy – Lithium Ion Batteries,” Futur. Energy Improv. Sustain. Clean Options our Planet, pp. 291–309, Jan. 2014, doi: 10.1016/B978-0-08-099424-6.00014-4.
  • [4] Y. Nishi, “Lithium Ion Secondary Batteries; Past 10 Years and The Future,” J. Power Sources, vol. 100, no. 1–2, pp. 101–106, Nov. 2001, doi: 10.1016/S0378-7753(01)00887-4.
  • [5] W. Waag, C. Fleischer, and D. U. Sauer, “Adaptive on-line prediction of the available power of lithium-ion batteries,” J. Power Sources, vol. 242, pp. 548–559, Nov. 2013, doi: 10.1016/J.JPOWSOUR.2013.05.111.
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  • [10] Y. Shang, G. Lu, Y. Kang, Z. Zhou, B. Duan, and C. Zhang, “A multi-fault diagnosis method based on modified Sample Entropy for lithium-ion battery strings,” J. Power Sources, vol. 446, p. 227275, Jan. 2020, doi: 10.1016/J.JPOWSOUR.2019.227275.
  • [11] J. Garche and A. Jossen, “Monitoring and safety tests of batteries: From state of charge (SOC) and health (SOH) to misuse, abuse and crash,” AIP Conf. Proc., vol. 1765, no. 1, p. 20005, Aug. 2016, doi: 10.1063/1.4961897/734036.
  • [12] X. Li, Z. Wang, L. Zhang, C. Zou, and D. D. Dorrell, “State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis,” J. Power Sources, vol. 410–411, pp. 106–114, Jan. 2019, doi: 10.1016/J.JPOWSOUR.2018.10.069.
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  • [14] M. Li, J. Lu, Z. Chen, and K. Amine, “30 Years of Lithium-Ion Batteries,” Adv. Mater., vol. 30, no. 33, p. 1800561, Aug. 2018, doi: 10.1002/ADMA.201800561.
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  • [17] P. Shen, M. Ouyang, L. Lu, J. Li, and X. Feng, “The co-estimation of state of charge, state of health, and state of function for lithium-ion batteries in electric vehicles,” IEEE Trans. Veh. Technol., vol. 67, no. 1, pp. 92–103, Jan. 2018, doi: 10.1109/TVT.2017.2751613.
  • [18] M. Cacciato, G. Nobile, G. Scarcella, and G. Scelba, “Real-time model-based estimation of SOC and SOH for energy storage systems,” 2015 IEEE 6th Int. Symp. Power Electron. Distrib. Gener. Syst. PEDG 2015, Aug. 2015, doi: 10.1109/PEDG.2015.7223028.
  • [19] M. A. Figueroa-Santos, J. B. Siegel, and A. G. Stefanopoulou, “Leveraging Cell Expansion Sensing in State of Charge Estimation: Practical Considerations,” Energies 2020, Vol. 13, Page 2653, vol. 13, no. 10, p. 2653, May 2020, doi: 10.3390/EN13102653.
  • [20] T. Wu, T. Zhao, and S. Xu, “Prediction of Remaining Useful Life of the Lithium-Ion Battery Based on Improved Particle Filtering,” Front. Energy Res., vol. 10, p. 863285, Jun. 2022, doi: 10.3389/FENRG.2022.863285/BIBTEX.
  • [21] T. Fan and W. Zhao, “Ensemble of model-based and data-driven prognostic approaches for reliability prediction,” 2017 Progn. Syst. Heal. Manag. Conf. PHM-Harbin 2017 - Proc., Oct. 2017, doi: 10.1109/PHM.2017.8079114.
  • [22] “Lithium-Ion Batteries,” 2019, doi: 10.1007/978-3-030-16800-1.
  • [23] X. Feng, M. Ouyang, X. Liu, L. Lu, Y. Xia, and X. He, “Thermal runaway mechanism of lithium ion battery for electric vehicles: A review,” Energy Storage Mater., vol. 10, pp. 246–267, Jan. 2018, doi: 10.1016/J.ENSM.2017.05.013.
  • [24] B. Dunn, H. Kamath, and J. M. Tarascon, “Electrical energy storage for the grid: A battery of choices,” Science (80-. )., vol. 334, no. 6058, pp. 928–935, Nov. 2011, doi: 10.1126/SCIENCE.1212741/SUPPL_FILE/DUNN-SOM.PDF.
  • [25] L. Yao et al., “A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods,” World Electr. Veh. J. 2021, Vol. 12, Page 113, vol. 12, no. 3, p. 113, Aug. 2021, doi: 10.3390/WEVJ12030113.
  • [26] G. Olivier, Bousquet; Ulrike Von, Luxburg; Ratsch, Advanced Lectures on Machine Learning, vol. 3176. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.
  • [27] A. Mammone, M. Turchi, and N. Cristianini, “Support vector machines,” Wiley Interdiscip. Rev. Comput. Stat., vol. 1, no. 3, pp. 283–289, Nov. 2009, doi: 10.1002/WICS.49.
  • [28] D. A. Pisner and D. M. Schnyer, “Support vector machine,” Mach. Learn. Methods Appl. to Brain Disord., pp. 101–121, Jan. 2020, doi: 10.1016/B978-0-12-815739-8.00006-7.
  • [29] M. Shahbazi, N. A. Smith, M. Marzband, and H. U. R. Habib, “A Reliability-Optimized Maximum Power Point Tracking Algorithm Utilizing Neural Networks for Long-Term Lifetime Prediction for Photovoltaic Power Converters,” Energies 2023, Vol. 16, Page 6071, vol. 16, no. 16, p. 6071, Aug. 2023, doi: 10.3390/EN16166071.
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Determination of Battery Health Life in Lithium Ion Batteries for Electric Vehicles

Year 2023, Volume: 2023 Issue: 19, 54 - 63, 03.01.2024

Mustafa Eker Emrah Eser İlker Günay

Abstract

Lithium-ion batteries, widely used in modern electric vehicles, feature high energy density, light weight and practical fast charging. In the field of electric vehicles, lithium-ion batteries are not only components for energy storage, but also important determinants of driving performance and overall vehicle efficiency. Battery health directly affects important aspects such as driving range, charging time and the overall lifespan of electric vehicles. Maintaining optimal battery health ensures sustainable vehicle performance while also reducing environmental impact through fewer battery replacements. This contributes to the larger sustainability of energy storage systems. This paper highlights the advantages of estimating the state of health (SOH) of lithium ion batteries. Therefore, different estimation methods for SOH determination are presented. These methods are applied to data sets of charge, discharge and internal resistance of battery packs shared by NASA. The results are presented separately for each battery type. The data obtained are then presented comparatively.

Keywords

Electric vehicles Lithium ion batteries SOH.

References

  • [1] A. Mahmoudzadeh Andwari, A. Pesiridis, S. Rajoo, R. Martinez-Botas, and V. Esfahanian, “A Review of Battery Electric Vehicle Technology and Readiness Levels,” Renew. Sustain. Energy Rev., vol. 78, pp. 414–430, Oct. 2017, doi: 10.1016/J.RSER.2017.03.138.
  • [2] N. E. Galushkin, N. N. Yazvinskaya, and D. N. Galushkin, “Mechanism of Thermal Runaway in Lithium-Ion Cells,” J. Electrochem. Soc., vol. 165, no. 7, pp. A1303–A1308, 2018, doi: 10.1149/2.0611807JES.
  • [3] J. Salminen, T. Kallio, N. Omar, P. Van den Bossche, J. Van Mierlo, and H. Gualous, “Transport Energy – Lithium Ion Batteries,” Futur. Energy Improv. Sustain. Clean Options our Planet, pp. 291–309, Jan. 2014, doi: 10.1016/B978-0-08-099424-6.00014-4.
  • [4] Y. Nishi, “Lithium Ion Secondary Batteries; Past 10 Years and The Future,” J. Power Sources, vol. 100, no. 1–2, pp. 101–106, Nov. 2001, doi: 10.1016/S0378-7753(01)00887-4.
  • [5] W. Waag, C. Fleischer, and D. U. Sauer, “Adaptive on-line prediction of the available power of lithium-ion batteries,” J. Power Sources, vol. 242, pp. 548–559, Nov. 2013, doi: 10.1016/J.JPOWSOUR.2013.05.111.
  • [6] S. Jafari, Z. Shahbazi, and Y. C. Byun, “Lithium-Ion Battery Health Prediction on Hybrid Vehicles Using Machine Learning Approach,” Energies 2022, Vol. 15, Page 4753, vol. 15, no. 13, p. 4753, Jun. 2022, doi: 10.3390/EN15134753.
  • [7] M. S. H. Lipu et al., “A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations,” J. Clean. Prod., vol. 205, pp. 115–133, Dec. 2018, doi: 10.1016/J.JCLEPRO.2018.09.065.
  • [8] X. Li, Z. Wang, and L. Zhang, “Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles,” Energy, vol. 174, pp. 33–44, May 2019, doi: 10.1016/J.ENERGY.2019.02.147.
  • [9] H. Tian, P. Qin, K. Li, and Z. Zhao, “A review of the state of health for lithium-ion batteries: Research status and suggestions,” J. Clean. Prod., vol. 261, p. 120813, Jul. 2020, doi: 10.1016/J.JCLEPRO.2020.120813.
  • [10] Y. Shang, G. Lu, Y. Kang, Z. Zhou, B. Duan, and C. Zhang, “A multi-fault diagnosis method based on modified Sample Entropy for lithium-ion battery strings,” J. Power Sources, vol. 446, p. 227275, Jan. 2020, doi: 10.1016/J.JPOWSOUR.2019.227275.
  • [11] J. Garche and A. Jossen, “Monitoring and safety tests of batteries: From state of charge (SOC) and health (SOH) to misuse, abuse and crash,” AIP Conf. Proc., vol. 1765, no. 1, p. 20005, Aug. 2016, doi: 10.1063/1.4961897/734036.
  • [12] X. Li, Z. Wang, L. Zhang, C. Zou, and D. D. Dorrell, “State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis,” J. Power Sources, vol. 410–411, pp. 106–114, Jan. 2019, doi: 10.1016/J.JPOWSOUR.2018.10.069.
  • [13] Beta Writer, Lithium-Ion Batteries. Cham, Switzerland: Springer Nature Switzerland AG, 2019.
  • [14] M. Li, J. Lu, Z. Chen, and K. Amine, “30 Years of Lithium-Ion Batteries,” Adv. Mater., vol. 30, no. 33, p. 1800561, Aug. 2018, doi: 10.1002/ADMA.201800561.
  • [15] M. Yoshio, R. J. Brodd, and A. Kozawa, “Lithium-ion batteries: Science and technologies,” Lithium-Ion Batter. Sci. Technol., pp. 1–452, 2009, doi: 10.1007/978-0-387-34445-4/COVER.
  • [16] N. Williard, W. He, C. Hendricks, and M. Pecht, “Lessons Learned from the 787 Dreamliner Issue on Lithium-Ion Battery Reliability,” Energies 2013, Vol. 6, Pages 4682-4695, vol. 6, no. 9, pp. 4682–4695, Sep. 2013, doi: 10.3390/EN6094682.
  • [17] P. Shen, M. Ouyang, L. Lu, J. Li, and X. Feng, “The co-estimation of state of charge, state of health, and state of function for lithium-ion batteries in electric vehicles,” IEEE Trans. Veh. Technol., vol. 67, no. 1, pp. 92–103, Jan. 2018, doi: 10.1109/TVT.2017.2751613.
  • [18] M. Cacciato, G. Nobile, G. Scarcella, and G. Scelba, “Real-time model-based estimation of SOC and SOH for energy storage systems,” 2015 IEEE 6th Int. Symp. Power Electron. Distrib. Gener. Syst. PEDG 2015, Aug. 2015, doi: 10.1109/PEDG.2015.7223028.
  • [19] M. A. Figueroa-Santos, J. B. Siegel, and A. G. Stefanopoulou, “Leveraging Cell Expansion Sensing in State of Charge Estimation: Practical Considerations,” Energies 2020, Vol. 13, Page 2653, vol. 13, no. 10, p. 2653, May 2020, doi: 10.3390/EN13102653.
  • [20] T. Wu, T. Zhao, and S. Xu, “Prediction of Remaining Useful Life of the Lithium-Ion Battery Based on Improved Particle Filtering,” Front. Energy Res., vol. 10, p. 863285, Jun. 2022, doi: 10.3389/FENRG.2022.863285/BIBTEX.
  • [21] T. Fan and W. Zhao, “Ensemble of model-based and data-driven prognostic approaches for reliability prediction,” 2017 Progn. Syst. Heal. Manag. Conf. PHM-Harbin 2017 - Proc., Oct. 2017, doi: 10.1109/PHM.2017.8079114.
  • [22] “Lithium-Ion Batteries,” 2019, doi: 10.1007/978-3-030-16800-1.
  • [23] X. Feng, M. Ouyang, X. Liu, L. Lu, Y. Xia, and X. He, “Thermal runaway mechanism of lithium ion battery for electric vehicles: A review,” Energy Storage Mater., vol. 10, pp. 246–267, Jan. 2018, doi: 10.1016/J.ENSM.2017.05.013.
  • [24] B. Dunn, H. Kamath, and J. M. Tarascon, “Electrical energy storage for the grid: A battery of choices,” Science (80-. )., vol. 334, no. 6058, pp. 928–935, Nov. 2011, doi: 10.1126/SCIENCE.1212741/SUPPL_FILE/DUNN-SOM.PDF.
  • [25] L. Yao et al., “A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods,” World Electr. Veh. J. 2021, Vol. 12, Page 113, vol. 12, no. 3, p. 113, Aug. 2021, doi: 10.3390/WEVJ12030113.
  • [26] G. Olivier, Bousquet; Ulrike Von, Luxburg; Ratsch, Advanced Lectures on Machine Learning, vol. 3176. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.
  • [27] A. Mammone, M. Turchi, and N. Cristianini, “Support vector machines,” Wiley Interdiscip. Rev. Comput. Stat., vol. 1, no. 3, pp. 283–289, Nov. 2009, doi: 10.1002/WICS.49.
  • [28] D. A. Pisner and D. M. Schnyer, “Support vector machine,” Mach. Learn. Methods Appl. to Brain Disord., pp. 101–121, Jan. 2020, doi: 10.1016/B978-0-12-815739-8.00006-7.
  • [29] M. Shahbazi, N. A. Smith, M. Marzband, and H. U. R. Habib, “A Reliability-Optimized Maximum Power Point Tracking Algorithm Utilizing Neural Networks for Long-Term Lifetime Prediction for Photovoltaic Power Converters,” Energies 2023, Vol. 16, Page 6071, vol. 16, no. 16, p. 6071, Aug. 2023, doi: 10.3390/EN16166071.
  • [30] K. Saha, B.; Goebel, “Battery Data Set, NASA AMES Prognostics Data Repository,” Moffett Field, CA., 2007. Accessed: Dec. 04, 2023. [Online]. Available: https://scirp.org/reference/referencespapers.aspx?referenceid=3297577.
There are 30 citations in total.

Details

Primary Language Turkish
Subjects Electrical Energy Storage
Journal Section Research Articles
Authors

Mustafa Eker 0000-0003-1085-0968

Emrah Eser

İlker Günay

Early Pub Date December 27, 2023
Publication Date January 3, 2024
Submission Date December 4, 2023
Acceptance Date December 22, 2023
Published in Issue Year 2023 Volume: 2023 Issue: 19

Cite

APA Eker, M., Eser, E., & Günay, İ. (2024). Elektrikli Araçlar için Lityum İyon Bataryalarda Sağlık Ömrünün Belirlenmesi. Journal of New Results in Engineering and Natural Sciences, 2023(19), 54-63.