Battery Models and Estimation Techniques for Energy Storage Systems in Residential Buildings
DOI:
https://doi.org/10.71426/jmt.v1.i1.pp47-58Keywords:
Battery Models, State of Charge (SOC), Estimation Techniques, Equivalent circuit.Abstract
This paper presents a comprehensive analysis of battery models and estimation techniques, focusing on their applications in residential buildings. The paper begins by exploring various battery models, including electrochemical, electrical equivalent circuit models. Different estimation techniques through various tests are discussed to provide insights into accurate battery parameter estimation. The applications of battery models in residential buildings are then discussed, highlighting energy storage sizing, energy management and optimization, and backup power supply. Additionally, it emphasizes the significance of software tools for battery modelling and simulation. Overall, this paper serves as a valuable resource for researchers, engineers, and decision-makers involved in the design and operation of residential energy systems, enabling them to make informed decisions regarding battery selection, sizing, and control strategies.
References
[1] Sun K, Shu Q. Overview of the types of battery models. In: Proceedings of the 30th Chinese Control Conference. IEEE; 2011. Available from: https://doi.org/10.1109/CCC.2011.6001591
[2] Thakkar RR, Srinivasa Rao Y, Sawant RR. Performance analysis of electrical equivalent circuit models of lithium-ion battery. In: Proceedings of the 2020 IEEE Pune Section International Conference (PuneCon). IEEE; 2020. Available from: https://doi.org/10.1109/PuneCon50868.2020.9362386
[3] Bhat C, Channegowda J, Naraharisetti K. Electrolyte based equivalent circuit model of lithium-ion batteries for intermittent load applications. In: Proceedings of the 2022 IEEE International Conference on Power Electronics, Smart Grid, and Renewable Energy (PESGRE). IEEE; 2022. Available from: https://doi.org/10.1109/PESGRE52268.2022.9715891
[4] Zhang X, Zhang W, Lei G. A review of Li-ion battery equivalent circuit models. Transactions on Electrical and Electronic Materials. 2016;17(6):311–316. Available from: https://doi.org/10.4313/TEEM.2016.17.6.311
[5] Tamilselvi S, et al. A review on battery modelling techniques. Sustainability. 2021;13(18):10042. Available from: https://doi.org/10.3390/su131810042
[6] Nejad S, Gladwin DT, Stone DA. A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states. Journal of Power Sources. 2016;316:183–196. Available from: https: //doi.org/10.1016/j.jpowsour.2016.03.042
[7] Liu C, Hu M, Jin G, Xu Y, Zhai J. State of power estimation of lithium-ion battery based on fractional-order equivalent circuit model. Journal of Energy Storage. 2021;41:102954. Available from: https://doi.org/10.1016/j.est.2021.102954
[8] Li K, Tseng KJ, Moraleja L. Study of the influencing factors on the discharging performance of lithium-ion batteries and its index of state-of-energy. In: Proceedings of the IECON 2016–42nd Annual Conference of the IEEE Industrial Electronics Society. IEEE; 2016. Available from: https://doi.org/10.1109/IECON.2016.7793567
[9] Wang Y, Tian J, Sun Z, Wang L, Xu R, Li M, Chen Z. A comprehensive review of battery modelling and state estimation approaches for advanced battery management systems. Renewable and Sustainable Energy Reviews. 2020;131:110015. Available from: https://doi.org/10.1016/j.rser.2020.110015
[10] Song Y, Park M, Seo M, Kim SW. Improved SOC estimation of lithium-ion batteries with novel SOC–OCV curve estimation method using equivalent circuit model. In: Proceedings of the 2019 4th International Conference on Smart and Sustainable Technologies (SpliTech). IEEE; 2019. Available from: https://doi.org/10.23919/SpliTech.2019.8783149
[11] Xu J, Mi CC, Cao B, Cao J. A new method to estimate the state of charge of lithium-ion batteries based on the battery impedance model. Journal of Power Sources. 2013;233:277–284. Available from: https://doi.org/10.1016/j.jpowsour.2013.01.094
[12] Wang Y, Zhang C, Chen Z. A method for state-of-charge estimation of LiFePO4 batteries at dynamic currents and temperatures using particle filter. Journal of Power Sources. 2015;279:306–311. Available from: https://doi.org/10.1016/j.jpowsour.2015.01.005
[13] Fan G, Li X, Canova M. A reduced-order electrochemical model of Li-ion batteries for control and estimation applications. IEEE Transactions on Vehicular Technology. 2018;67(1):313–323. Available from: https://doi.org/10.1109/TVT.2017.2738780
[14] Rahimi-Eichi H, Ojha U, Baronti F, Chow MY. Battery management system: An overview of its application in the smart grid and electric vehicles. IEEE Industrial Electronics Magazine. 2013;7(2):4–16. Available from: https://doi.org/10.1109/MIE.2013.2250351
[15] Kim IS. Nonlinear state of charge estimator for hybrid electric vehicle battery. IEEE Transactions on Power Electronics. 2008;23(4):2027–2034. Available from: https://doi.org/10.1109/TPEL.2008.924629
[16] Zheng L, Zhang L, Zhu J, Wang G, Jiang J. Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model. Applied Energy. 2016;180:424–434. Available from: https://doi.org/10.1016/j.apenergy.2016.08.016
[17] Wang S, Verbrugge M, Wang JS, Liu P. Multi-parameter battery state estimator based on the adaptive and direct solution of the governing differential equations. Journal of Power Sources. 2011;196(20):8735–8741. Available from: https://doi.org/10.1016/j.jpowsour.2011.06.078
[18] Susanna S, Dewangga BR, Wahyuungoro O, Cahyadi DI. Comparison of simple battery model and Thevenin battery model for SOC estimation based on OCV method. In: Proceedings of the 2019 International Conference on Information and Communications Technology (ICOIACT). IEEE; 2019. Available from: https://doi.org/10.1109/ICOIACT46704.2019.8938495
[19] Wahyuddin MI, Priambodo PS, Sudibyo H. State of charge (SoC) analysis and modelling battery discharging parameters. In: Proceedings of the 2018 4th International Conference on Science and Technology (ICST). IEEE; 2018. Available from: https://doi.org/10.1109/ICSTC.2018.8528631
[20] Chang WY. The state of charge estimation methods for battery: A review. International Scholarly Research Notices. 2013;2013:953792. Available from: https://doi.org/10.1155/2013/953792
[21] Ramesh B, Suhashini G, Kalnoor G, Manikanta BVVN, Krishna VBM, Nageswara Rao D. Cost optimization by integrating PV system and battery energy storage system into microgrid using particle swarm optimization. International Journal of Pure and Applied Mathematics. 2017;114(8):45–55.
[22] Krishna VBM, Babu ASH, Jithendranath J, Rao CUM. An isolated wind hydro hybrid system with two back-to-back power converters and a battery energy storage system using neural network compensator. In: Proceedings of the 2014 International Conference on Circuits, Power and Computing Technologies (ICCPCT). IEEE; 2014. p. 273–279. Available from: https://doi.org/10.1109/ICCPCT.2014.7054829
[23] Pande AS, Soni BP, Bhadane KV. Classification and review of electric circuit models for electric vehicle batteries. International Journal of Electric and Hybrid Vehicles. 2023;15(2):107–126. Available from: https://doi.org/10.1504/IJEHV.2023.132029
[24] Pande AS, Soni BP, Bhadane KV. Classification and review of electric circuit models for electric vehicle batteries. International Journal of Electric and Hybrid Vehicles. 2023;15(2):107-26. Available from: https://doi.org/10.1504/IJEHV.2023.132029
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Copyright (c) 2024 V L Narasimha Rao Peddibhotla, V. Lavanya, D. Manasa, Sairam Boggavarapu, Bhanu Pratap Soni (Author)

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