Open Access
Issue
E3S Web of Conf.
Volume 547, 2024
International Conference on Sustainable Green Energy Technologies (ICSGET 2024)
Article Number 03006
Number of page(s) 4
Section Energy
DOI https://doi.org/10.1051/e3sconf/202454703006
Published online 09 July 2024
  1. Concetta and Caggiano, Mariateresa and Olabi, Abdul-Ghani and Dassisti, Michele, “Battery monitoring and prognostics optimization techniques: challenges and opportunities”, journal Energy, volume 255, pages 124538, year 2022, Elsevier. [CrossRef] [Google Scholar]
  2. Zhu Yunzheng, Qu Shaofei, Allen C. Huang, Xu Jianhong, “Smart Vehicle Control Unit – an integrated BMS and VCU System” IFAC-Papers On Line, Volume 51, Issue 31, 2018, Pages 676- 679, ISSN 2405-8963. [CrossRef] [Google Scholar]
  3. Q. Yu, R. Xiong, C. Lin, W. Shen and J. Deng, “Lithium-Ion Battery Parameters and State of- Charge Joint Estimation Based on H-Infinity and Unscented Kalman Filters”, IEEE Transactions on Vehicular Technology (Volume: 66, Issue: 10, October 2017) [Google Scholar]
  4. Rivera-Barrera,J.P. Mun˜oz-Galeano, N. Sarmiento-Maldonado, H.O, “SOC estimation for lithium-ion batteries: Review and future challenges”, Electronics-2017. [Google Scholar]
  5. Piller, S.; Perrin,, “M.; Jossen, A. Methods for state-of-charge determination and their applications”, J. Power Sources 2001, 96, 113–120 [CrossRef] [Google Scholar]
  6. Xia, C.Y.; Zhang, S.; Sun, H.T,”A strategy of estimating state of charge based on Kalman filter.” [Google Scholar]
  7. Chin. J. Power Sources 2007, 31, 414. [Google Scholar]
  8. Xing, J.; Wu, P, “State of charge estimation of lithium-ion battery based on improved adaptive unscented Kalman filter” Sustainability 2021, 13, 5046. https://doi.org/10.3390/su13095046. [CrossRef] [Google Scholar]
  9. Piller, S.; Perrin, M.; Jossen, A. “Methods for state-of-charge determination and their applications.” J. Power Sources 2001, 96, 113–120. [CrossRef] [Google Scholar]
  10. Karthick, K.; Ravivarman, S.; Priyanka, R. Optimizing Electric Vehicle Battery Life: A Machine Learning Approach for Sustainable Transportation. World Electr. Veh. J. 2024, 15, 60. https://doi.org/10.3390/wevj15020060 [CrossRef] [Google Scholar]
  11. Li, Y.; Chattopadhyay, P.; Xiong, S.; Ray, A.; Rahn, C.D, “Dynamic data-driven and model-based recursive analysis for estimation of battery state-of-charge”. Appl. Energy 2016, 184, 266–275. [Google Scholar]
  12. Semeraro, Concetta and Caggiano, Mariateresa and Olabi, Abdul-Ghani and Dassisti, Michele,” Battery monitoring and prognostics optimization techniques: challenges and opportunities”, journal Energy, volume 55, Elsevier. reactor, Ph.D. thesis, University of Ljubljana, Faculty of Mathematics and Physics (2020) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.