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Climate change is driving the transformation of energy systems from fossil to renewable energies. In industry, power supply systems and electro-mobility, the need for electrical energy storage is rising sharply. Lithium-based batteries are one of the most widely used technologies. Operating parameters must be determined to control the storage system within the approved operating limits. Operating outside the limits, i.e., exceeding or falling below the permitted cell voltage, can lead to faster aging or destruction of the cell. Accurate cell information is required for optimal and efficient system operation. The key is high-precision measurements, sufficiently accurate battery cell and system models, and efficient control algorithms. Increasing demands on the efficiency and dynamics of better systems require a high degree of accuracy in determining the state of health and state of charge (SOC). These scientific contributions to the above topics are divided into two parts. In the first part of the paper, a holistic overview of the main SOC assessment methods is given. Physical measurement methods, battery modeling, and the methodology of using the model as a digital twin of a battery are addressed and discussed. In addition, adaptive methods and artificial intelligence methods that are important for SOC calculation are presented. Part two of the paper presents examples of the application areas and discusses their accuracy.
Czasopismo
Rocznik
Tom
Strony
311--323
Opis fizyczny
Bibliogr. 35 poz., rys., tab.
Twórcy
autor
- Magdeburg–Stendal University of Applied Sciences Germany
autor
- Fraunhofer IFF Magdeburg Germany
autor
- Magdeburg–Stendal University of Applied Sciences Germany
Bibliografia
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- [11] Partovibakhsh M., Liu G., Online estimation of model parameters and state-of-charge of LithiumIon battery using Unscented Kalman Filter, 2012 American Control Conference (ACC) (2012), DOI: 10.1109/ACC.2012.6315272.
- [12] Wang W., Wang X., Xiang C., Wei C., Zhao Y., Unscented Kalman filter-based battery SOC estimation and peak power prediction method for power distribution of hybrid electric vehicles, IEEE Access, vol. 6, pp. 35957–35965 (2018), DOI: 10.1109/ACCESS.2018.2850743.
- [13] Seo B.-H., Nguyen T.H., Lee D.-C., Lee K.-B., Kim J.-M., Condition monitoring of lithium polymer batteries based on sigma-point Kalman filter, Journal of Power Electronics, vol. 12, no. 5, pp. 778–786 (2012), DOI: 10.6113/JPE.2012.12.5.778.
- [14] Xia B., Huang R., Lao Z., Zhang R., Lai Y., Zheng W., Wang M., Online parameter identification of lithium-ion batteries using a novel multiple forgetting factor recursive least square algorithm, Energies, vol. 11, no. 11, pp. 1–19 (2018), DOI: 10.3390/en11113180.
- [15] Yu Q., Xiong R., Lin C., Online estimation of state-of-charge based on the H infinity and unscented Kalman filters for lithium ion batteries, Energy Procedia, vol. 105, pp. 2791–2796 (2017), DOI: 10.1016/j.egypro.2017.03.600.
- [16] Liu Z., Dang X., A new method for state of charge and capacity estimation of lithium-ion battery based on dual strong tracking adaptive H infinity filter, Mathematical Problems in Engineering, pp. 1–18 (2018), DOI: 10.1155/2018/5218205.
- [17] Nejad S., Gladwin D.T., Stone D.A., Enhanced state-of-charge estimation for lithium-ion iron phosphate cells with flat open-circuit voltage curves, IECON2015-Yokohama, November 9–12, Japan (2015), DOI: 10.1109/IECON.2015.7392591.
- [18] Hou Z., Xie P., Hou J., The state of charge estimation of power lithium battery based on RBF neural network optimized by particle swarm optimization, Journal of Applied Science and Engineering, vol. 20, no. 4, pp. 483–490 (2017), DOI: 10.6180/JASE.2017.20.4.10.
- [19] Sun X., Ji J., Ren B., Xie C., Yan D., Adaptive forgetting factor recursive least square algorithm for online identification of equivalent circuit model parameters of a lithium-ion battery, Energies, vol. 12, no. 12, 2242 (2019), DOI: 10.3390/en12122242.
- [20] Sheng H., Xiao J., Electric vehicle state of charge estimation: nonlinear correlation and fuzzy support vector machine, Journal of Power Sources, vol. 281, pp. 131–137 (2015), DOI: 10.1016/j.jpowsour. 2015.01.145.
- [21] Xu J., Cao B., Chen Z., Zou Z., An online state of charge estimation method with reduced prior battery testing information, International Journal of Electrical Power & Energy Systems, vol. 63, pp. 178–184 (2014), DOI: 10.1016/j.ijepes.2014.06.017.
- [22] Liu F., Ma J., Su W., Unscented particle filter for SOC estimation algorithm based on a dynamic parameter identification, Mathematical Problems in Engineering, no. 6, pp. 1–14 (2019), DOI: 10.1155/2019/ 7452079.
- [23] Lai X., Gao W., Zheng Y., Ouyang M., Li J., Han X., Zhou L., A comparative study of global optimization methods for parameter identification of different equivalent circuit models for Li-Ion batteries, Electrochimica Acta, vol. 295, pp. 1057–1066 (2019), DOI: 10.1016/j.electacta.2018.11.134.
- [24] Wang Z., Gladwin D.T., Smith M.J., Haass S., Practical state estimation using Kalman filter methods for large-scale battery systems, Applied Energy, vol. 294, 117022, ISSN 0306-2619, DOI: 10.1016/ j.apenergy.2021.117022.
- [25] Komarnicki P., Lombardi P., Styczynski Z., Elektrische Energiespeichersysteme – Flexibilitätsoptionen für Smart Grids, Springer Verlag (2021), DOI: 10.1007/978-3-662-62802-7.
- [26] Balischewski S., Hauer I., Wolter M., Wenge C., Lombardi P., Komarnicki P., Battery storage services that minimize wind farm operating costs: A case study, 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, 26–29 September, Turin, pp. 1–61 (2017), DOI: 10.1109/ISGTEurope.2017.8260130.
- [27] Parol M., Wójtowicz T., Księżyk K., Wenge C., Balischewski S., Arendarski B., Optimum management of power and energy in low voltage microgrids using evolutionary algorithms and energy storage, International Journal of Electrical Power and Energy Systems, vol. 119 (2020), DOI: 10.1016/ j.ijepes.2020.105886.
- [28] Wei Z.-B., Zhao J., He H.-W., Ding G.-L., Cui H.-Y., Liu L.-C., Future smart battery and management. Advanced sensing from external to embedded multi-dimensional measurement, Journal of Power Sources, vol. 489, no. 1, 229462 (2021), DOI: 10.1016/j.jpowsour.2021.229462.
- [29] Johansson D., Andersson J., Wickman B., Björefors F., Sobkowiak A., Kasemo B., Nanoplasmonic sensing of Pb-acid and li-ion batteries, Sensors and Electronic Instrumentation Advances, pp. 57–59 (2016), http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-346564, accessed May 2021.
- [30] Wenge C., Pietracho R., Balischewski S., Arendarski B., Lombardi P., Komarnicki P., Kasprzyk L., Multi usage applications of li-ion battery storage in a large photovoltaic plant: A practical experience, Energies, vol. 13, no. 18, 4590 (2020), DOI: 10.3390/en13184590.
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- [32] https://de.statista.com/infografik/25389/prognose-zur-weltweiten-nachfrage-nach-lithium-ionen-batterien-fuer-elektrofahrzeuge-nach-regionen/, accessed September 2021.
- [33] Shamarova N., Komarnicki P., Wenge C.,Comparative study of state of charge estimation algorithms forlithium-ion battery, IOP Conference Series: Material Science and Engineering, International Conference: Actual Issues of Mechanical Engineering, October 27–29, Saint-Petersburg, Russian Federation, vol. 1111, no. 1, 012053 (2020), DOI: 10.1088/1757-899X/1111/1/012053.
- [34] Hallmann M., Wenge C., Balischewski S., Komarnicki P., Methods for lithium-based battery energy storage state of charge estimation part I: Overview, Archives of Electrical Engineering, vol. 71, no. 1, pp. 139–157 (2022).
- [35] https://www.acesproject.eu/about-aces-project/, accessed September 2021
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-55b1add8-1392-4d06-9e02-27a6a405c9f0