Performance comparison of lithium polymer battery SOC estimation using GWO-BiLSTM and cutting-edge deep learning methods
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Electrical and Electronic Engineering
Link
https://link.springer.com/content/pdf/10.1007/s00202-023-01934-z.pdf
Reference49 articles.
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2. Christensen PA et al (2021) Risk management over the life cycle of lithium-ion batteries in electric vehicles. Renew Sustain Energy Rev 148:111240. https://doi.org/10.1016/J.RSER.2021.111240
3. See KW et al (2022) Critical review and functional safety of a battery management system for large-scale lithium-ion battery pack technologies. Int J Coal Sci Technol 9(1):1–17. https://doi.org/10.1007/S40789-022-00494-0/FIGURES/6
4. Li Y et al (2022) A hybrid machine learning framework for joint SOC and SOH estimation of lithium-ion batteries assisted with fiber sensor measurements. Appl Energy 325:119787. https://doi.org/10.1016/J.APENERGY.2022.119787
5. Huang Z, Fang Y, Xu J (2021) SOC estimation of Li-ION battery based on improved EKF algorithm. Int J Autom Technol 22(2):335–340. https://doi.org/10.1007/S12239-021-0032-4
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2. Cavity-constrained LIBS combined with the gray wolf optimization algorithm for optimizing bidirectional long short-term memory (GWO-BiLSTM) networks for classification prediction of different brands of cigarettes;Journal of Analytical Atomic Spectrometry;2024
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