پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 31 |
| تعداد شمارهها | 834 |
| تعداد مقالات | 8,015 |
| تعداد مشاهده مقاله | 14,854,818 |
| تعداد دریافت فایل اصل مقاله | 9,587,849 |
Improvement of The Battery State of Charge Estimation Using Recursive Least Square Based Adaptive Extended Kalman Filter | ||
| International Journal of Industrial Electronics Control and Optimization | ||
| مقاله 6، دوره 7، شماره 2، مرداد 2024، صفحه 141-151 اصل مقاله (1.88 M) | ||
| نوع مقاله: Research Articles | ||
| شناسه دیجیتال (DOI): 10.22111/ieco.2024.47863.1537 | ||
| نویسندگان | ||
| Ramezan Havangi* ؛ Fatemeh Karimi | ||
| University of Birjand | ||
| چکیده | ||
| Battery Management System (BMS) including measurements errors that causes decrease in the quality of calculated State of the Charge (SOC). It will limit the accurate estimation of the SOC that is a critical challenge in some of the engineering fields such as medical science, robotics, navigation and industrial applications. These facts implies on the significance of SOC estimation from battery measurements that is the matter of the literature through the recent years. Due to the dependency of the EKF to the system model, the change in the battery parameters and noise information cause losing performance in the SOC estimation over the time. In this paper, we assume that the battery parameters including internal resistance and capacitor and also the noise information are varying over the time. To solve that, two separate on-line identification algorithms for parameters and noise information are introduced. In more details, a Recursive Least Square (RLS) algorithm is used to identify the resistance and capacitor values. Moreover, the process and measurement noise covariance are estimated based on iterative noise information identification algorithm. Then all of the updated values are used in the EKF algorithm. This paper aims to address the issue of uncertainty in SOC estimation by proposing two algorithms. The first algorithm focuses on identifying deterministic uncertainty, which refers to uncertainty in model parameters. To address the challenge of uncertain model parameters, RLS is introduced. | ||
| کلیدواژهها | ||
| Battery charge؛ State of the Charge (SOC)؛ Kalman Filter؛ Estimation | ||
| مراجع | ||
|
[1] P.Shrivastava, P.Naidu , S.Sharma , B.K.Panigrahi , A.Garg, “Review on technological advancement of lithium-ion battery states estimation methods for electric vehicle applications”, Journal of Energy Storage, vol.64, 2023.
[2] G.Mohebalizadeh, H.Alipour,L.Mohammadian, M.Sabahi, “An Improved Sliding Mode Controller for DC/DC Boost Converters Used in EV Battery Chargers with Robustness against the Input Voltage Variations”, International Journal of Industrial Electronics, Control and Optimization,vol.4, no.2, pp.257-266 , 2021. [3] C.Ge, Y.Zheng , Y.Yu, “State of charge estimation of lithium-ion battery based on improved forgetting factor recursive least squares-extended Kalman filter joint algorithm”, Journal of Energy Storage, vol.55,2022. [4] K.H.Wu , M.Seyedmahmoudian, A. tojcevski, “Lithium-ion battery state of charge estimation using improved coulomb counting method with adaptive error correction” Journal of Automobile Engineering,2023. [5] P.Shrivastava, T.Soon , M.Y.I.B.Idris, S.Mekhilef , S. Bahari, R.S.Adnan, “Comprehensive co-estimation of lithium-ion battery state of charge, state of energy, state of power, maximum available capacity, and maximum available energy” Journal of Energy Storage,vol.6, Part B, 10 December 2022, 106049 [6] T. Bat-Orgil, B. Dugarjav, and T. Shimizu, “Battery Module Equalizer based on State of Charge Observation derived from Overall Voltage Variation,” IEEJ J. Ind. Appl., vol. 9, no. 5, pp. 584–596, 2020. [7] M. Lu, X. Zhang, J. Ji, X. Xu, and Y. Zhang, “Research progress on power battery cooling technology for electric vehicles,” J. Energy Storage, vol. 27, pp. 101155, 2020. [8] X. Xiong, S.-L. Wang, C. Fernandez, C.-M. Yu, C.-Y. Zou, and C. Jiang, “A novel practical state of charge estimation method: an adaptive improved ampere-hour method based on composite correction factor,” Int. J. energy Res., vol. 44, no. 14, pp. 11385–11404, 2020. [9] 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. [10] 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. [11] X. Bian , L.Liu, J.Yan, Z.Zou, R. Zhao, “An open circuit voltage-based model for state-of-health estimation of lithium-ion batteries Model development and validation,” Journal of Power Sources,vol.448,2020 [12] 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,vol.17, pp.321–326, 2022. [13] 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. [14] F. Yang, W. Li, C. Li, and Q. Miao, “State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network,” Energy, vol. 175, pp. 66–75, 2019. [15] M. Talha, F. Asghar, and S. H. Kim, “A neural network-based robust online SOC and SOH estimation for sealed lead--acid batteries in renewable systems,” Arab. J. Sci. Eng., vol. 44, no. 3, pp. 1869–1881, 2019. [16] W. Kim, P.Y. Lee, J. Kim, K.S. Kim, “A robust state of charge estimation approach based on nonlinear battery cell model for lithium-ion batteries in electric vehicles”, IEEE Trans. Veh. Technol., vol.70, no.6, pp. 5638–5647,.2021. [17] 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. [18] T. Jarou, J. Abdouni, S. Benchikh, S. Elidrissi, and others, “The parameter update of Lithium-ion battery by the RSL algorithm for the SOC estimation under extended kalman filter (EKF-RLS),” Int. J. Eng. Appl. Phys., vol. 3, no. 2, pp. 706–719, 2023. [19] Y. Fang, R. Xiong, and J. Wang, “Estimation of Lithium-ion battery state of charge for electric vehicles based on dual extended Kalman filter,” Energy Procedia, vol. 152, pp. 574– 579, 2018. [20] R. Xiong, H. He, F. Sun, X. Liu, and Z. Liu, “Model-based state of charge and peak power capability joint estimation of lithium-ion battery in plug-in hybrid electric vehicles,” J. Power Sources, vol. 229, pp. 159–169, 2013. [21] S. Zhang, H. Tao, K. Bi, W. Yan, and H. Ni, “SOC Estimation of Lithium-ion Battery Based on RLS-EKF for Unmanned Aerial Vehicle,” in Journal of Physics: Conference Series, pp. 12002.,2022. [22] M. Li, Y. Zhang, Z. Hu, Y. Zhang, and J. Zhang, “A battery SOC estimation method based on AFFRLS-EKF,” Sensors, vol. 21, no. 17, p. 5698, 2021. [23] C. Ge, Y. Zheng, and Y. Yu, “State of charge estimation of lithium-ion battery based on improved forgetting factor recursive least squares-extended Kalman filter joint algorithm,” J. Energy Storage, vol. 55, p. 105474, 2022. [24] C. Zhang, X. Li, W. Chen, G. G. Yin, J. Jiang, and others, “Robust and adaptive estimation of state of charge for lithium-ion batteries,” IEEE Trans. Ind. Electron., vol. 62, no. 8, pp. 4948–4957, 2015. [25] D. Sun et al., “State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator,” Energy, vol. 214, p. 119025, 2021. [26] T. Jarou, J. Abdouni, S. Benchikh, S. Elidrissi, and others, “The parameter update of Lithium-ion battery by the RSL algorithm for the SOC estimation under extended kalman filter (EKF-RLS),” Int. J. Eng. Appl. Phys., vol. 3, no. 2, pp. 706–719, 2023. [27] M. Li, Y. Zhang, Z. Hu, Y. Zhang, and J. Zhang, “A battery SOC estimation method based on AFFRLS-EKF,” Sensors, vol. 21, no. 17, pp. 5698, 2021. | ||
|
آمار تعداد مشاهده مقاله: 374 تعداد دریافت فایل اصل مقاله: 388 |
||