Novel temperature-effective modeling and state of charge estimation based on sigma-point Kalman filter for lithium titanate oxide battery

• Autorzy: Muratoğlu Yusuf , Alkaya Alkan

Identyfikatory

DOI

10.24425/bpasts.2024.150809

• Języki publikacji: EN

Abstrakty

EN

Battery modeling and state of charge (SoC) estimation are critical functions in the effective battery management system (BMS) operation. Temperature directly affects the performance and changes the model accuracy of a battery. Most studies have focused on estimating the internal temperature of the battery from the surface temperature of the battery with the help of sensors. However, due to the high number of cells in battery packs, the increase in sensor costs and the number of parameters have been ignored. Therefore, this article presents a new framework for the temperature effect using the electrical circuit model. The terminal voltage of the battery includes the effect under different operating conditions. This effect was associated with internal resistance in the battery model. The developed temperature-effective battery model was tested at different temperatures and operating currents. The model was validated with a maximum average root mean square error of 0.05% from the test results. The SoC of the LTO battery was estimated with the sigma-point Kalman (SPK) filter incorporating the developed model. The maximum average root mean square error in the estimation results is 0.11%. It is suitable for practical applications due to its low cost, simplicity, and reliability.

Słowa kluczowe

EN

temperature-effective modeling; state of charge estimation; sigma point Kalman filter; battery management system; energy storage system

PL

modelowanie efektywnej temperatury; szacowanie stanu naładowania; filtr Kalmana punktu sigma; system zarządzania baterią; system magazynowania energii

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2024
• Tom: Vol. 72, nr 5
• Strony: art. no. e150809
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Muratoğlu Yusuf

• Department of Electronics and Automation, Vocational High School, Toros University, Mezitli 33340, Mersin, Turkey

• https://orcid.org/0000-0002-0923-0959

autor

Alkaya Alkan

• Department of Electrical and Electronics Engineering, Faculty of Engineering, Mersin University, Ciftlikkoy 33100, Mersin, Turkey

• https://orcid.org/0000-0002-8235-6726

Bibliografia

• [1] J. Zhu et al., “An improved electro-thermal battery model complemented by current dependent parameters for vehicular low temperature application,” Appl. Energy, vol. 248, pp. 149–161, Aug. 2019, doi: 10.1016/j.apenergy.2019.04.066.
• [2] J. Jayaprabakar, J.A. Kumar, J. Parthipan, A. Karthikeyan, M. Anish, and N. Joy, “Review on hybrid electro chemical energy storage techniques for electrical vehicles: Technical insights on design, performance, energy management, operating issues & challenges,” J. Energy Storage, vol. 72, p. 108689, Nov. 2023, doi: 10.1016/j.est.2023.108689.
• [3] A.F. Challoob, N.A. Bin Rahmat, V.K.A.L Ramachandaramurthy, and A.J. Humaidi, “Energy and battery management systems for electrical vehicles: A comprehensive review & recommendations,” Energy Explor. Exploit., Jan. 2023, doi: 10.1177/01445987231211943.
• [4] G. Yüksek and A. Alkaya, “An Adaptive Energy Management Approach for Battery-Supercapacitor Hybrid Energy Storage System,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 72, pp. e150203, doi: 10.24425/bpasts.2024.150203.
• [5] G. Yüksek and A. Alkaya, “A novel state of health estimation approach based on polynomial model for lithium-ion batteries,” Int. J. Electrochem. Sci., vol. 18, no. 5, p. 100111, 2023, doi: 10.1016/j.ijoes.2023.100111.
• [6] Z.N. Ezhyeh, M. Khodaei, and F. Torabi, “Review on doping strategy in Li4Ti5O12 as an anode material for Lithium-ion batteries,” Ceram. Int., vol. 49, no. 5. pp. 7105–7141, Mar. 2023, doi: 10.1016/j.ceramint.2022.04.340.
• [7] R. Ranjith Kumar, C. Bharatiraja, K. Udhayakumar, S. Devakirubakaran, K.S. Sekar, and L. Mihet-Popa, “Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications,” IEEE Access, vol. 11, pp. 105761–105809, 2023, doi: 10.1109/ACCESS.2023.3318121.
• [8] K. Saqli, H. Bouchareb, N.K. M’sirdi, and M. Oudghiri Bentaie, “Lithium-ion battery electro-thermal modelling and internal states co-estimation for electric vehicles,” J. Energy Storage, vol. 63, p. 107072, Jul. 2023, doi: 10.1016/j.est.2023.107072.
• [9] J. Meng, G. Luo, M. Ricco, M. Swierczynski, D.I. Stroe, and R. Teodorescu, “Overview of Lithium-Ion battery modeling methods for state-of-charge estimation in electrical vehicles,” Appl. Sci., vol. 8, no. 5, p. 659, Apr. 2018, doi: 10.3390/app8050659.
• [10] A.A. Hussein, “An Empirical Capacity Estimation Model for Lithium-ion Battery Cells Using Surface Temperature and Terminal Voltage Measurements,” 2023 IEEE Applied Power Electronics Conference and Exposition (APEC), Orlando, USA, 2023, pp. 110–113, doi: 10.1109/APEC43580.2023.10131419.
• [11] L. Xu, J. Cooper, A. Allam, and S. Onori, “Comparative Analysis of Numerical Methods for Lithium-Ion Battery Electrochemical Modeling,” J. Electrochem. Soc., vol. 170, no. 12, p. 120525, Dec. 2023, doi: 10.1149/1945-7111/ad1293.
• [12] G. Tucker, R. Drummond, and S.R. Duncan, “Optimal Fast Charging of Lithium Ion Batteries: Between Model-Based and Data-Driven Methods,” J. Electrochem. Soc., vol. 170, no. 12, p. 120508, Dec. 2023, doi: 10.1149/1945-7111/ad0ccd.
• [13] S. Nejad, D.T. Gladwin, and D.A. Stone, “A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states,” J. Power Sources, vol. 316, pp. 183–196, Jun. 2016, doi: 10.1016/j.jpowsour.2016.03.042.
• [14] S. Mao et al., “An Electrical–Thermal Coupling Model with Artificial Intelligence for State of Charge and Residual Available Energy Co-Estimation of LiFePO4 Battery System under Various Temperatures,” Batteries, vol. 8, no. 10, p. 140, Oct. 2022, doi: 10.3390/batteries8100140.
• [15] G. Vennam, A. Sahoo, and S. Ahmed, “A Novel Coupled Electro-thermal-aging Model for Simultaneous SOC, SOH, and Parameter Estimation of Lithium-ion Batteries,” 2022 American Control Conference (ACC), Atlanta, USA, 2022, pp. 5259–5264, doi: 10.23919/ACC53348.2022.9867320.
• [16] H. Bouchareb, K. Saqli, N.K. M’Sirdi and M. Oudghiri, “Observer Design for SOC Estimation of Li-ion Batteries Based on Electro-Thermal Coupled Model,” 2021 9th International Renewable and Sustainable Energy Conference (IRSEC), Morocco, 2021, pp. 1–6, doi: 10.1109/IRSEC53969.2021.9741140.
• [17] A.K. De Souza, G. Plett, and M.S. Trimboli, “Lithium-Ion Battery Charging Control Using a Coupled Electro-Thermal Model and Model Predictive Control,” IEEE Applied Power Electronics Conference and Exposition (APEC), New Orleans, USA, 2020, pp. 3534–3539, doi: 10.1109/APEC39645.2020.9124431.
• [18] S. Liu, H. Sun, H. Yu, J. Miao, C. Zheng, and X. Zhang, “A framework for battery temperature estimation based on fractional electro-thermal coupling model,” J. Energy Storage, vol. 63, p. 107042, Jul. 2023, doi: 10.1016/j.est.2023.107042.
• [19] C.S. Chin, Z. Gao, and C.Z. Zhang, “Comprehensive electro-thermal model of 26650 lithium battery for discharge cycle under parametric and temperature variations,” J. Energy Storage, vol. 28, p. 101222, Apr. 2020, doi: 10.1016/j.est.2020.101222.
• [20] K. Li, F. Zhou, X. Chen, W. Yang, J. Shen, and 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, p. 125763, Jan. 2023, doi: 10.1016/J.ENERGY.2022.125763.
• [21] A.M.S.M.H.S. Attanayaka, J.P. Karunadasa, and K.T.M.U. Hemapala, “Comprehensive electro-thermal battery-model for Li-ion batteries in microgrid applications,” Energy Storage, vol. 3, no. 3, p. e230, Jun. 2021, doi: 10.1002/EST2.230.
• [22] P. Qin, Y. Che, H. Li, Y. Cai, and M. Jiang, “Joint SOC–SOP estimation method for lithium-ion batteries based on electro-thermal model and multi-parameter constraints,” J. Power Electron., vol. 22, no. 3, pp. 490–502, Mar. 2022, doi: 10.1007/s43236-021-00376-9.
• [23] H. Pang et al., “A novel extended Kalman filter-based battery internal and surface temperature estimation based on an improved electro-thermal model,” J. Energy Storage, vol. 41, p. 102854, Sept. 2021, doi: 10.1016/j.est.2021.102854.
• [24] M.A. Perez Estevez, S. Calligaro, O. Bottesi, C. Caligiuri, and M. Renzi, “An electro-thermal model and its electrical parameters estimation procedure in a lithium-ion battery cell,” Energy, vol. 234, p. 121296, Nov. 2021, doi: 10.1016/j.energy.2021.121296.
• [25] H. Pang, L. Guo, L. Wu, J. Jin, F. Zhang, and K. Liu, “A novel extended Kalman filter-based battery internal and surface temperature estimation based on an improved electro-thermal model,” J. Energy Storage, vol. 41, p. 102854, Sep. 2021, doi: 10.1016/J.EST.2021.102854.
• [26] A.J. Haug, “Bayesian estimation for target tracking: Part II, the Gaussian sigma-point Kalman filters,” WIREs Comp Stat, vol. 4, no. 5, pp. 489–497, Sep. 2012, doi: 10.1002/wics.1215.
• [27] Y. Muratoğlu and A. Alkaya, “Nonlinear estimator-based state of charge estimation for lithium titanate oxide battery in energy storage systems,” Energy Storage, vol. 6, no. 1, p. e494, Feb. 2024, doi: 10.1002/EST2.494