Research Article
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Machine Learning Approaches for Enhancing the SoH Estimation of LTO Batteries

Year 2025, Volume: 9 Issue: 1, 48 - 59

İsmail Can Dikmen , Nisanur Yildiran , Teoman Karadag

https://doi.org/10.30939/ijastech..1522403

Abstract

Lithium titanate oxide (LTO) batteries' practical application in modern technologies depends on accurately predicting their state of health (SoH). Using advanced machine learning (ML) techniques, our study examined how to estimate LTO batteries' SoH. For this purpose, we aged rechargeable LTO batteries for 3500 cycles with a battery analyzer and performed differential voltage analysis (DVA). To estimate SoH as a regression problem, we used three machine learn-ing methods: Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Gaussi-an Process Regressions (GPR). As a novel approach to SoH estimation, our research uses a feedforward neural network to solve the categorization problem. In analyzing and comparing the performance of all methods, we found that this categorization-based neural network ap-proach improved computational efficiency by 60.89% while achieving SoH estimation accura-cy of 93.18%. By advancing the field of battery health monitoring, these findings contribute to more reliable and efficient battery management algorithms. In addition to improving battery management systems' accuracy and computational efficiency, the categorization approach demonstrated here could also be used to extend the life and reliability of LTO batteries, includ-ing those used in electric vehicles and renewable energy storage systems. The results of this study illustrate the importance of applying innovative machine learning applications to en-hance battery SoH estimations, providing important implications for future research and prac-tice.

Keywords

LTO batteries Machine learning State of Health estimation Differential voltage analysis Battery management algorithms

Supporting Institution

Inonu University

Project Number

FDK-2021-2645, FOA-2018-1358

Thanks

Prof. Dr. Serdar ALTIN

References

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Year 2025, Volume: 9 Issue: 1, 48 - 59

İsmail Can Dikmen , Nisanur Yildiran , Teoman Karadag

https://doi.org/10.30939/ijastech..1522403

Abstract

Project Number

FDK-2021-2645, FOA-2018-1358

References

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  • [20] Basia A, Simeu-Abazi Z, Gascard E, Zwolinski P. Review on State of Health estimation methodologies for lithium-ionbatteries in the context of circular economy. CIRP J Manuf Sci Technol. 2021;32:517–28. https://doi.org/10.1016/j.cirpj.2021.02.004.
  • [21] Feng H, Shi G. SOH and RUL prediction of Li-ion batteries based on improved Gaussianprocess regression. J POWER Electron. 2021;21(12):1845–54. https://doi.org/10.1007/s43236-021-00318-5.
  • [22] Yang D, Zhang X, Pan R, Wang Y, Chen Z. A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve. J Power Sources. 2018;384:387–95. https://doi.org/10.1016/J.JPOWSOUR.2018.03.015.
  • [23] Yildiran N, Dikmen IC, Karadag T. State of Health Estimation of Lithium Titanate Oxide Batteries Through Data-Driven Techniques and Machine Learning. 8th Int Artif Intell Data Process Symp IDAP. 2024. https://doi.org/10.1109/IDAP64064.2024.10711165.
  • [24] Yang B, Qian Y, Li Q, Chen Q, Wu J, Luo E, et al. Critical summary and perspectives on state-of-health of lithium-ion battery. Renew Sustain Energy Rev. 2024;1;190:114077. https://doi.org/10.1016/J.RSER.2023.114077.
  • [25] Xiong W, Mo Y, Yan C. Online State-of-Health Estimation for Second-Use Lithium-Ion BatteriesBased on Weighted Least Squares Support Vector Machine. IEEE ACCESS. 2021;9:1870–81. https://doi.org/10.1109/ACCESS.2020.3026552.
  • [26] Zou B, Xiong M, Wang H, Ding W, Jiang P, Hua W, et al. A Deep Learning Approach for State-of-Health Estimation of Lithium-IonBatteries Based on a Multi-Feature and Attention Mechanism Collaboration. BATTERIES-BASEL. 2023; 9(6). https://doi.org/10.3390/batteries9060329.
  • [27] Zhou X, Hsieh SJ, Peng B, Hsieh D. Cycle life estimation of lithium-ion polymer batteries using artificialneural network and support vector machine with time-resolvedthermography. Microelectron Reliab. 2017;79:48–58. https://doi.org/10.1016/j.microrel.2017.10.013.
  • [28] Niraula A, Singh JG. Deep Learning-Based Approach for State-of-Health Estimation of Lithium-Ion Battery in the Electric Vehicles. 2023 Int Conf Power, Instrumentation, Energy Control PIECON 2023. https://doi.org/10.1109/PIECON56912.2023.10085757.
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  • [32] Müller V, Scurtu RG, Memm M, Danzer MA, Wohlfahrt-Mehrens M. Study of the influence of mechanical pressure on the performance and aging of Lithium-ion battery cells. J Power Sources. 2019;15;440:227148. https://doi.org/10.1016/J.JPOWSOUR.2019.227148.
  • [33] Soltani M, Vilsen SB, Stroe AI, Knap V, Stroe DI. Degradation behaviour analysis and end-of-life prediction of lithium titanate oxide batteries. J Energy Storage. 2023;68. https://doi.org/10.1016/j.est.2023.107745.
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There are 63 citations in total.

Details

Primary Language English
Subjects Hybrid and Electric Vehicles and Powertrains
Journal Section Articles
Authors

İsmail Can Dikmen 0000-0002-7747-7777

Nisanur Yildiran 0000-0001-6689-7322

Teoman Karadag 0000-0002-7682-7771

Project Number FDK-2021-2645, FOA-2018-1358
Publication Date
Submission Date July 25, 2024
Acceptance Date October 22, 2024
Published in Issue Year 2025 Volume: 9 Issue: 1

Cite

APA Dikmen, İ. C., Yildiran, N., & Karadag, T. (n.d.). Machine Learning Approaches for Enhancing the SoH Estimation of LTO Batteries. International Journal of Automotive Science And Technology, 9(1), 48-59. https://doi.org/10.30939/ijastech..1522403
AMA Dikmen İC, Yildiran N, Karadag T. Machine Learning Approaches for Enhancing the SoH Estimation of LTO Batteries. IJASTECH. 9(1):48-59. doi:10.30939/ijastech.1522403
Chicago Dikmen, İsmail Can, Nisanur Yildiran, and Teoman Karadag. “Machine Learning Approaches for Enhancing the SoH Estimation of LTO Batteries”. International Journal of Automotive Science And Technology 9, no. 1 n.d.: 48-59. https://doi.org/10.30939/ijastech. 1522403.
EndNote Dikmen İC, Yildiran N, Karadag T Machine Learning Approaches for Enhancing the SoH Estimation of LTO Batteries. International Journal of Automotive Science And Technology 9 1 48–59.
IEEE İ. C. Dikmen, N. Yildiran, and T. Karadag, “Machine Learning Approaches for Enhancing the SoH Estimation of LTO Batteries”, IJASTECH, vol. 9, no. 1, pp. 48–59, doi: 10.30939/ijastech..1522403.
ISNAD Dikmen, İsmail Can et al. “Machine Learning Approaches for Enhancing the SoH Estimation of LTO Batteries”. International Journal of Automotive Science And Technology 9/1 (n.d.), 48-59. https://doi.org/10.30939/ijastech. 1522403.
JAMA Dikmen İC, Yildiran N, Karadag T. Machine Learning Approaches for Enhancing the SoH Estimation of LTO Batteries. IJASTECH.;9:48–59.
MLA Dikmen, İsmail Can et al. “Machine Learning Approaches for Enhancing the SoH Estimation of LTO Batteries”. International Journal of Automotive Science And Technology, vol. 9, no. 1, pp. 48-59, doi:10.30939/ijastech. 1522403.
Vancouver Dikmen İC, Yildiran N, Karadag T. Machine Learning Approaches for Enhancing the SoH Estimation of LTO Batteries. IJASTECH. 9(1):48-59.


International Journal of Automotive Science and Technology (IJASTECH) is published by Society of Automotive Engineers Turkey

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