Elman Neural Network Optimized by Swarm Intelligence for SOC Estimation of Lithium-Ion Battery
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3324-8_29
Reference15 articles.
1. Sundaresan S, Devabattini BC, Kumar P, Pattipati KR, Balasingam B (2022) Tabular open circuit voltage modelling of li-ion batteries for robust SOC estimation. Energies 15(23):9142. https://doi.org/10.3390/en15239142
2. Mohammadi F (2022) Lithium-ion battery State-of-Charge estimation based on an improved Coulomb-Counting algorithm and uncertainty evaluation. J Energy Storage 48:104061. https://doi.org/10.1016/j.est.2022.104061
3. Feng Y, Xue C, Han QL, Han FL, Du JC (2020) Robust estimation for state-of-charge and state-of-health of lithium-ion batteries using integral-type terminal sliding-mode observers. IEEE Trans Ind Electron 67(5):4013–4023. https://doi.org/10.1109/TIE.2019.2916389
4. Feng L, Ding J, Han YY (2020) Improved sliding mode based EKF for the SOC estimation of lithium-ion batteries. Ionics 26(6):2875–2882. https://doi.org/10.1007/s11581-019-03368-9
5. Guo L, Li JQ, Fu ZJ (2019) Lithium-ion battery SOC estimation and hardware-in-the-loop simulation based on EKF. Energy Procedia 158:2599–2604. https://doi.org/10.1016/j.egypro.2019.02.009
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