Preview

Civil Aviation High Technologies

Advanced search

Analysis of dynamic process models for aviation battery systems

https://doi.org/10.26467/2079-0619-2025-28-3-8-24

Abstract

The paper analyzes existing modeling approaches, including empirical, physicochemical and statistical methods, as well as machine learning methods. The advantages and limitations of models such as the Shepherd’s model, Butler-Volmer equation-based models, regression analysis-based models, and Long Short-Term Memory (LSTM) neural networks are discussed. Special attention is paid to a promising Method of Mathematical Prototyping of Energy Processes (MMPEP), this approach enables the construction of physically accurate models that conform with the fundamental laws of thermodynamics and electrodynamics. Based on MMPEP, a new voltage dynamics model has been developed specifically for lithium-ion aircraft batteries (LIABs), which take into account polarization processes, temperature changes and nonlinear effects. The model proposed in the paper is derived through numerical-analytical transformation of dynamic processes equations obtained by the method of mathematical prototyping of energy processes. A comparative analysis of existing modeling approaches is carried out and the advantages of the proposed MMPEP method are shown. An example of modeling the dynamics of physical and chemical processes in a lithium-ion battery with some limitations is presented. The research results demonstrate that the MMPEP-based models have high accuracy and versatility, which makes them applicable for charge state prediction, failure diagnostics, and digital twin development. The analytical expression presented in the paper expands the classical Shepherd’s model, providing a description of complex dynamic processes. The methodological potential of MMPEP is supported by the possibility of integration with machine learning methods to refine model parameters. Prospects for further research include extending the model to account for battery degradation, developing simplified models for real-time diagnostics systems, and introducing hybrid modeling approaches.

About the Author

S. I. Gavrilenkov
Moscow State Technical University of Civil Aviation
Russian Federation

Stanislav I. Gavrilenkov, Postgraduate Student of the Chair of Electrical Engineering and Aviation Electrical Equipment,

Moscow.



References

1. Levin, A.V., Khalyutin, S.P., Zhmu-rov, B.V. (2015). Trends and prospects of aviation equipment development. Nauchnyy vestnik MGTU GA, no. 213 (3), pp. 50–57. (in Russian)

2. Khalyutin, S.P., Starostin, I.E., Aga-fonkina, I.V. (2023). Generalized method of mathematical prototyping of energy processes for digital twins development. Energies, vol. 16, issue 4, ID: 1933. DOI: 10.3390/en16041933 (accessed: 10.08.2024).

3. Zhou, R., Zhang, Y., Gao, L., Li, J., Wu, X. (2021). Theoretical model of lithium iron phosphate power battery under high-rate discharging for electromagnetic launch. International Journal of Mechanical System Dynamics, vol. 1, no. 2, pp. 220–229. DOI: 10.1002/msd2.12014

4. Li, K., Wei, F., Tseng, K.J., Soong, B. H. (2018). A practical lithium-ion battery model for state of energy and voltage responses prediction incorporating temperature and ageing effects. IEEE Transactions on Industrial Electronics, vol. 65, no. 8, pp. 6696–6708. DOI: 10.1109/TIE.2017.2779411

5. Shepherd, C.M. (1965). Design of pri-mary and secondary cells: II. An equation describing battery discharge. Journal of The Electrochemical Society, vol. 112, no. 7, pp. 657–664. DOI: 10.1149/1.2423659

6. Bard, A.J., Faulkner, L.R. (2001). Electrochemical methods: Fundamentals and applications. 2nd ed. New York: John Wiley and Sons, 864 p.

7. Starostin, I.E., Druzhinin, A.A. (2023). Analytical approximation of solutions of equations of the method of mathematical proto-typing of energy processes by qualitative analysis of these equations. Reliability and Quality of Com-plex Systems, no. 2 (42), pp. 22–31. DOI: 10.21685/2307-4205-2023-2-3 (in Russian)

8. Yu, Z., Hu, X., Yang, S., Xu, Y., Liu, C. (2022). SOH estimation method for lithium-ion battery based on discharge characteristics. International Journal of Electrochemical Science, vol. 17, issue 7. ID: 220725. DOI: 10.20964/2022.07.38 (accessed: 10.08.2024).

9. Starostin, I.E., Druzhinin, A.A., Gav-rilenkov, S.I. (2023). Using supervised machine learning for constructing mathematical models of systems by the method of mathematical proto-typing of energy processes. In: Trudy mezhdunarodnogo simpoziuma “Nadezhnost i Ka¬chest-vo”, vol. 1, pp. 66–72. (in Russian)

10. Campagna, N., Castiglia, V., Miceli, R., Miceli, D., Spataro, C. (2020). Battery models for battery powered applications: A comparative study. Energies, vol. 13, issue 16. ID: 4085. DOI: 10.3390/en13225824 (accessed: 10.08.2024).

11. Supriyono. (2018). Evaluation of the dynamic modeling and discharge performance of a magnesium battery activated by seawater. International Journal of Technology, vol. 4, pp. 663–674.

12. Barsegyan, K.R., Perepeliza, M.A., Onishchenko, D.O. (2022). Mathematical mod-el creation for the lithium-ion battery and its comparing with analogs. Izvestiya MGTU MA-MI, vol. 16, no. 1, pp. 81–88. DOI: 10.17816/2074-0530-104574 (in Russian)

13. Hochreiter, S., Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, vol. 9, issue 8, pp. 1735–1780. DOI: 10.1162/neco.1997.9.8.1735

14. Caliwag, A.C., Lim, W. (2019). Hybrid VARMA and LSTM method for lithium-ion battery state-of-charge and output voltage forecasting in electric motorcycle applications. IEEE Access, vol. 7, pp. 59680–59689. DOI: 10.1109/ACCESS.2019.2914188 (accessed: 10.08.2024).

15. Kolosnitsyn, D.V., Savvina, A.A., Khramtsova, L.A., Kuzmina, E.V., Karase-va, E.V., Kolosnitsyn, V.S. (2021). Simulation and estimation of lithium-sulfur battery charge state using fuzzy neural network. Electrochemical Energetics, vol. 21, no. 2, pp. 96–107. DOI: 10.18500/1608-4039-2021-21-2-96-107 (in Russian)

16. Doyle, M., Fuller, T.F., Newman, J. (1993). Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical Society, vol. 140, no. 6. ID: 1526. DOI: 10.1149/1.2221597 (accessed: 10.08.2024).

17. Safari, M., Delacourt, C. (2011). Modeling of a commercial Graphite/LiFePO4 cell using a simplified electrochemical and thermal model. Journal of The Electrochemical Society, vol. 158, no. 5. ID: A562–A571. DOI: 10.1149/1.3567007 (accessed: 10.08.2024).

18. Punt, E.A. (2024). Algorithm for the synthesis of equations of thermal conductivity of lithium-ion accumulator for finite volumes during division. Civil Aviation High Technologies, vol. 27, no. 4, pp. 50–62. DOI: 10.26467/2079-0619-2024-27-4-50-62 (in Russian)

19. Gu, W.B., Wang, C.Y. (2000). Thermal and electrochemical coupled modeling of a lithium-ion cell. Electrochemical Engine Center, vol. 99–25, no. 1, pp. 748–762.

20. Etkin, V.A. (2021). To a unified thermodynamic theory of real transfer processes. Information Processes, Systems, and Techno-logies, no. 2 (20), pp. 9–18. DOI: 10.52529/27821617_2021_2_2_09 (in Russian)

21. Eykhoff, P. (1975). Systems identification: Parameters and state estimation Eind-hoven, London: Wiley-Interscience, 555 p.

22. Prigogine, I., Defay, R. (1954). Chemical Thermodynamics. Published by Longman, 419 p.

23. Starostin, I.E., Khalyutin, S.P., Parievskiy, V.V. (2022). Types and forms of representation of the basic equations of the method of mathematical prototyping of energy processes. Elektropitaniye, no. 4, pp. 4–14. (in Russian)

24. Starostin, I.E., Stepankin, A.G. (2019). Software implementation of modern nonequilibrium thermodynamics methods and simulation system for physicochemical processes Simula-tionNonEqProcSS v.0.1.0: Monograph. Beau Bassin, Mauritius: Lambert Academic Publishing, 127 p. (in Russian)

25. Etkin, V.A. (2022). Ergodynamic theory of biological systems evolution. Information Processes, Systems, and Technologies, vol. 3, no. 1 (22), pp. 12–24. DOI: 10.52529/27821617_2022_3_1_12 (in Russian)

26. Davidov, A.O., Zhmurov, B.V. (2016). Method for diagnosing aviation electrochemical batteries. In: Trudy mezhdunarodnogo simpoziuma “Nadezhnost i Kachestvo”, vol. 2, pp. 78–80. (in Russian)

27. Starostin, I.E., Druzhinin, A.A. (2023). The concept of a software and technological platform for digital twins based on energy dynamics methods. In: 2023 5th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE), pp. 1–6. DOI: 10.1109/REEPE57272.2023.10086710

28. Etkin, V.A. (2008). Energodynamics (Synthesis of theories of energy transfer and conversion). St. Petersburg: Nauka, 409 p. (in Russian)

29. Kedrinskiy, I.A., Yakovlev, V.G. (2002). Li-ion batteries. Krasnoyarsk: Platina, 268 p. (in Russian)

30. Bagotskiy, V.S. (1988). Fundamentals of Electrochemistry. Moscow: Khimiya, 401 p. (in Russian)


Review

For citations:


Gavrilenkov S.I. Analysis of dynamic process models for aviation battery systems. Civil Aviation High Technologies. 2025;28(3):8-24. (In Russ.) https://doi.org/10.26467/2079-0619-2025-28-3-8-24

Views: 14


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2079-0619 (Print)
ISSN 2542-0119 (Online)