Hybrid optimized deep learning approach for prediction of battery state of charge, state of health and state of temperature
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Electrical and Electronic Engineering
Link
https://link.springer.com/content/pdf/10.1007/s00202-023-02105-w.pdf
Reference22 articles.
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2. Bockrath S, Rosskopf A, Koffel S, Waldhor S, Srivastava K, Lorentz VRH (2019) State of charge estimation using recurrent neural networks with long short-term memory for lithium-ion batteries. In: IECON proceedings (industrial electronics conference), 2019-October, pp 2507–2511. https://doi.org/10.1109/IECON.2019.8926815
3. Harifi S, Khalilian M, Mohammadzadeh J, Ebrahimnejad S (2019) Emperor Penguins Colony: a new metaheuristic algorithm for optimization. Evol Intell 12(2):211–226. https://doi.org/10.1007/S12065-019-00212-X/TABLES/10
4. Hariprasad A, Priyanka I, Sandeep R, Ravi V, Shekar O, Students UG (2020) Battery management system in electric vehicles. Int J Eng Res Technol. https://doi.org/10.17577/IJERTV9IS050458
5. He W, King M, Luo X, Dooner M, Li D, Wang J (2021) Technologies and economics of electric energy storages in power systems: review and perspective. Adv Appl Energy 4:100060. https://doi.org/10.1016/J.ADAPEN.2021.100060
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