[1] M.Hassan, S.Saha, Md.E.Haque, S.Islam, A.Mahmud, N.Mendis, “A comprehensive review of battery state of charge estimation techniques,” Sustainable Energy Technologies and Assessments,vol.54,2022
[2] R.P.Narasipuram,S.A.Mopidevi, “ Technological overview & design considerations for developing electric vehicle charging stations,” Journal of Energy Storage,vol.43, 2021.
[3] W.A.Choi, “Study on State of Charge and State of Health Estimation in Consideration of Lithium-Ion Battery Aging,” Sustainability, vol.12, no.24, 2020.
[4] S.Iniyan, S.Jebaraj, L.Suganthi, AA.Samuel, “Energy models for renewable energy utilization and to replace fossil fuels,” Methodology, vol.67, pp.28-37, 2020.
[5] Q.Yu,C.Wan ,J.Li ,L.E ,X.Zhang 1,Y.Huang , and T.Liu, “Open circuit voltage model fusion method for state of charge estimation of Lithium-Ion Batteries,” Energies.vol.14,no.7,2021.
[6] W.Choi, “A study on state of charge and state of health estimation in consideration of Lithium-Ion battery aging,” Sustainability,vol.12,no.24,2020
[7] B.Zine,H.Bia,A.Benmouna,M.Becherif,and M.Iqbal, “Experimentally validated coulomb counting method for Battery State-of-Charge estimation under variable current profiles,” Energies,vol.15,no.21,2022.
[8] Y.Xiong,Y.Zhu,H.Xing,S.Lin,J.Xiao,C.Zhang, “An improved state of charge estimation of lithium-ion battery based on a dual input model,” Energy Sources, Part A,vol.45,no.1, 2023.
[9] X. Bian , L.Liu, J.Yan, Z.Zou, R. Zhao, “An open circuit voltage-based model for state-ofhealth estimation of lithium-ion batteries Model development and validation,” Journal of Power Sources,vol.448,2020
[10] A.K.Birjandi, M.F. Alavi, M.Salem, M.E.H.Assad, N. Prabaharan, “Modeling carbon dioxide emission of countries in southeast of Asia by applying artificial neural network,” International Journal of Low-Carbon Technologies,17, 321–326, 2022.
[11] G.Javid, D.O.Abdeslam, M. Basset, “Adaptive online state of charge estimation of EVs Lithium-Ion batteries with deep recurrent neural networks,” Energies, vol.14, 2021.
[12] M.H.A.Jamlouie, “Accuracy improvement of SOC estimation in lithium-ion batteries by ANFIS vs. ANN modeling of nonlinear cell characteristics,” Journal of Energy Storage, vol.6, pp.95–104, 2021.
[13] R.Xiong, J.Cao, Q.Yu, H.He, “Critical review on the battery state of charge estimation methods for electric vehicles,” IEEE Access, vol.6, pp.1832–1843, 2018.
[14] Y.Zeng,Y.Li, andT.Yang , “State of charge estimation for Lithium-Ion Battery based on unscented Kalman Filter and long short-term memory neural network,” Batteries,vol.9,no.7 2023.
[15] Y.Tian , R.Lai, X.Li, J.Tian , “State-of-charge estimation for lithium-ion batteries based on attentional sequence-to-sequence architecture,” Journal of Energy Storage, vol. 62, 2023.
[16] O.H.Junior, G.R.Sylvestrin, H.F.Scherer, “Experimental validation of state of charge estimation by extended Kalman filter and modified coulomb counting,”
IEEE Latin America Transactions, vol.20, no.11, 2022.
[17] P.Shrivastava,T.K.Soon,M.Y. I.Idris,S.Mekhilef,S.B.R.S.Adnan, “Combined state of charge and state of energy estimation of Lithium-Ion battery using dual forgetting factor-based adaptive extended Kalman filter for electric vehicle applications”, IEEE Transactions on Vehicular Technology,vol.20,no.2,2021.
[18] Y.Wei,L.Ling, “State-of-Charge Estimation for Lithium-Ion Batteries Based on Temperature-Based Fractional-Order Model and Dual Fractional-Order Kalman Filter,” IEEE Access,vol.10,2022.
[19] M.Hossain, M. E. Haque,S. Saha, M.T Arif, AMT. Oo , “State of Charge Estimation of Liion Batteries Based on Adaptive Extended Kalman Filter,” IEEE Power & Energy Society General Meeting ,2020.
[20] E.L.G.Lutzemberger, L.Pugi, “State-of-charge estimation based on model-adaptive Kalman filters,” Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering,2021.
[21] Z.Huang, M. Best, J.Knowles; A.Fly, “Adaptive piecewise equivalent circuit model with SOC/SOH estimation based on extended Kalman filter,” IEEE Transactions on Energy Conversion,vol.38,no.2,2023.
[22] X.Shu,Z.Chen,J.Shen,F.Guo,Y.Zhang,Y.Liu, “State of Charge Estimation for LithiumIon Battery Based on Hybrid Compensation Modeling and Adaptive H-Infinity Filter,”IEEE Transactions on Transportation Electrification,vol.9,no.1,2023.
[23] K.Li, F.Zhou, X.Chen, W.Yang, J.Shen, Z.Song, “State-of-charge estimation combination algorithm for lithium-ion batteries with Frobenius-norm-based QR decomposition modified adaptive cubature Kalman filter and H-infinity filter based on electro-thermal model,” Energy,vol.263, Part C, 2023.
[24] H.Ben Sassi, F. Errahimi, N.Es-Sbai, “State of charge estimation by multi-innovation unscented Kalman filter for vehicular applications,” Journal of Energy Storage,vol.32, 2020.
[25] X.Lin,Y.Tang, J.Ren, Y.Wei , “ State of charge estimation with the adaptive unscented Kalman filter based on an accurate equivalent circuit model,” Journal of Energy Storage,vol.41,2021.
[26] L.Chen, X.Wu , AM.Lopes , L.Yin , P.Li, “ Adaptive State-of-Charge estimation of lithiumion batteries based on square-root unscented Kalman filte,” Energy,vol.6,no.8, 2022.
[27] N.Shi, Z.Chen, M.Niu, Z.He, Y.Wang, J. Cui, “State-of-charge estimation for the lithiumion battery based on adaptive extended Kalman filter using improved parameter identification,” Journal of Energy Storage,vol.45,2022.
[28] S.Hosseininasab,N.Momtaheni,S.Pischinger,M.Günther,L.Bauer, “State-of-charge estimation of Lithium-ion batteries using an adaptive dual unscented Kalman filter based on a reduced-order model,” Journal of Energy Storage,vol.73,Part D, 2023.
[29] F.Xia,Z.Wang,C.Zhang, “State of charge estimation of Lithium-ion batteries with particle filter algorithm,” The 46th Annual Conference of the IEEE Industrial Electronics Society,2020.
[30] V.Sangwan, R.Kumar, A.K. Rathore, “State-of-Charge estimation of Li-ion battery at different temperatures using particle filter”, The Journal of Engineering ,vol.2019, no.18,pp.5320–5324, 2019.
[31] Yuanhong Xu, Mingcong Deng , “Particle filter design for robust nonlinear control system of uncertain heat exchange process with sensor noise and communication time delay,” Applied Sciences ,vol.12,no.5,2022.
[32] R.Havangi, “Simultaneous Estimation of model parameters and state-of-charge of Lithium-Ion Batteries using Recursive least squares and Modified Particle Filter”, ComputationalIntelligence in Electrical Engineering, vol. 12, no.1, 2021.
[33] B.Xia, B.Xia, S.Guo ,W.Wang,Y. Lai,H.Wang,M.Wang W.Zheng, “A State of Charge Estimation Method Based on Adaptive Extended Kalman-Particle Filtering for Lithium-ion Batteries,” Energies ,vol.11,no.10,2018.
[34] Y.Wang, Z.Chen, “A framework for state-of-charge and remaining discharge time prediction using unscented particle filter,” Applied Energy,vol.260, 2020.
[35] F.Liu,J.Ma, W.Su, “Unscented Particle Filter for SOC Estimation Algorithm Based on a Dynamic Parameter Identification” Mathematical Problems in Engineering,vol.2019,2019.
[36] X.Wei, C.Jun, G.Yu, M.Jiachen, C.Jiaqing , “Unscented particle filter based state of energy estimation for LiFePO4 batteries using an online updated model,” International Journal of Automotive Technology,vol.23, pp.503–510,2022.
[37] K.Zhang,J.Ma,X.Zhao,D. Zhang,Y.He,“State of charge estimation for Lithium battery Based on adaptively weighting cubature particle filter,” IEEE Access,vol.7,2019.
[38] C.Kuptametee, N.Aunsri, “Review of resampling techniques in particle filtering framework,” Measurement, vol.193, 2022.
[39] X.Zhang, L.Zhao,W.Zhong,F.Gu, “Performance Analysis of Resampling Algorithms of Parallel Distributed Particle Filters,” IEEE Access, vol.9, 2021.
[40] H.A.Khidir, İ.Etikan, D.H.Kadir, N.H.Mahmood, R.Sabetvand, “Bayesian machine learning analysis with Markov Chain Monte Carlo techniques for assessing characteristics and risk factors of Covid-19 in Erbil City-Iraq 2020–2021,” Alexandria Engineering Journal,vol.78, pp.162-174, 2023.
[41] https://data.mendeley.com/datasets/wykht8y7tg/1