By
M. Gayathri,
Mrs. P. Arthi,
A Thenmozhi,
- UG Scholars, TPGIT, Bagayam, Vellore District, Tamilnadu, India
- Assistant Professor, TPGIT, Bagayam, Vellore District, Tamilnadu, India
- UG Scholars, TPGIT, Bagayam, Vellore District, Tamilnadu, India
Abstract
The most widely used energy storage components in products like electric cars, portable electronics, and energy storage systems are lithium batteries. On the other hand, if lithium batteries are not regularly checked, they may perform worse, have a shorter lifespan, or even explode or cause serious harm. Our proposal is to develop a state of health monitoring system for lithium batteries and an algorithm for estimating the state of charge based on the state of health results, to avert such mishaps. Additionally, since speed control affects the rotational speed of motors and other machinery, it is required in electric cars. This directly affects how the machine operates and is essential to the calibre and result of the work. Li-ion batteries have a lot of energy in them, and thermal runaway accelerates quicker the more power is in the battery itself. If the battery is fully charged and something happened inside it, then thermal runaway would happen quickly. To overcome this, fire protection of electric vehicles is necessary.
Keywords: Lithium batteries, rotational speed, health monitoring, accelerates, energy storage
How to cite this article: M. Gayathri, Mrs. P. Arthi, A Thenmozhi. Analyzing And Predicting the Battery Health of Battery Energy in EV’s. Journal of Nuclear Engineering & Technology. 2024; ():-.
How to cite this URL: M. Gayathri, Mrs. P. Arthi, A Thenmozhi. Analyzing And Predicting the Battery Health of Battery Energy in EV’s. Journal of Nuclear Engineering & Technology. 2024; ():-. Available from: https://journals.stmjournals.com/jonet/article=2024/view=167337
References
- Liao et Q, (2017) ‘Performance assessment and classification of retired lithium-ion battery from electric vehicles for energy storage’, International Journal. Hydrogen Energy.Vol.42, No.30, pp.18817–18823 Available at: https://www.sciencedirect.com/science/article/abs/pii/S0360319917323108?via%3Dihub
- Lyu Z, Gao and L. Chen, (2021) ‘Li-ion battery state of health estimation and remaining useful life prediction through a model-data-fusion method’, IEEE Trans. Power Electron., Vol.36, No.6, pp.6228–6240. Available at: https://ieeexplore.ieee.org/document/9238444
- N and D. Strickland, (2016) ‘Analysis and comparative study of different converter modes in modular second-life hybrid battery energy storage systems’, IEEE J. Emerg. Sel. Topics Power Electron., Vol.4, No.2, pp.547–563. Available at: https://research.aston.ac.uk/en/publications/analysis-and-comparative-study-of-different-converter-modes-in-mo
- Severson K, M. P. Attia, N. Jin, N. Perkins, B. Jiang and Z. Yang, (2019) ‘Data-driven prediction of battery cycle life before capacity degradation’, Nature Energy., Vol.4, No.5, pp.383–391. Available at: https://www.nature.com/articles/s41560-019-0356-8
- Vermeer W, G. R. Chandra Mouli and P. Bauer, (2022) ‘A comprehensive review on the characteristics and modeling of lithium-ion battery aging’, IEEE Trans. Transp. Electrific., Vol.8, No.2, pp.2205–2232. Available at: https://research.tudelft.nl/en/publications/a-comprehensive-review-on-the-characteristics-and-modeling-of-lit
- Wang et al. Q, (2021) ‘A novel consistency evaluation method for series connected battery systems based on real-world operation data’, IEEE Trans. Transp. Electrific., Vol.7, No.2, pp.437–451. Available at: https://ieeexplore.ieee.org/document/9171880
- Yang Y, J. Qiu, C. Zhang, J. Zhao and G. Wang, (2022) ‘Flexible integrated network planning considering echelon utilization of second life of used electric vehicle batteries’, IEEE Trans. Transp. Electrific., Vol.8, No.1, pp.263–276. Available at: https://ieeexplore.ieee.org/document/9383273
- Maleki S, Ray B, Hagh MT. Hybrid framework for predicting and forecasting State of Health of Lithium-ion batteries in Electric Vehicles. Sustainable Energy, Grids and Networks. 2022 Jun 1; 30:100603.
- Urquizo J, Singh P. A review of health estimation methods for Lithium-ion batteries in Electric Vehicles and their relevance for Battery Energy Storage Systems. Journal of Energy Storage. 2023 Dec 20; 73:109194.
- Xiong R, Zhang Y, Wang J, He H, Peng S, Pecht M. Lithium-ion battery health prognosis based on a real battery management system used in electric vehicles. IEEE Transactions on Vehicular Technology. 2018 Aug 9;68(5):4110-21.
- Akbar K, Zou Y, Awais Q, Baig MJ, Jamil M. A machine learning-based robust state of health (SOH) prediction model for electric vehicle batteries. Electronics. 2022 Apr 12;11(8):1216.
- Barré A, Suard F, Gérard M, Montaru M, Riu D. Statistical analysis for understanding and predicting battery degradations in real-life electric vehicle use. Journal of Power Sources. 2014 Jan 1; 245:846-56.
- Li Y, Liu K, Foley AM, Zülke A, Berecibar M, Nanini-Maury E, Van Mierlo J, Hoster HE. Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review. Renewable and sustainable energy reviews. 2019 Oct 1; 113:109254.
- Lipu MH, Hannan MA, Hussain A, Hoque MM, Ker PJ, Saad MH, Ayob A. A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations. Journal of cleaner production. 2018 Dec 20; 205:115-33.
- Dong G, Han W, Wang Y. Dynamic Bayesian network-based lithium-ion battery health prognosis for electric vehicles. IEEE Transactions on Industrial Electronics. 2020 Nov 4;68(11):10949-58.
Ahead of Print Subscription Review Article
Journal of Nuclear Engineering & Technology
ISSN: 2277-6184
| Volume | |
| Received | July 1, 2024 |
| Accepted | July 26, 2024 |
| Published | August 16, 2024 |