Electrochemical simulation of lithium-ion batteries: a novel computational approach for optimizing performance

Document Type : Research Paper

Authors

1 Department of Chemical and Environmental Engineering, University of Cincinnati, Cincinnati, OH 45221, United States

2 Battery and Energy Generator Research Lab, K.N. Toosi University of Technology, Iran

3 Assistant Professor, Niroo Research Institute, Tehran, Iran

Abstract

The accurate simulation of lithium-ion batteries is crucial for optimizing their performance, increasing their lifespan, and mitigating environmental concerns, as they play a vital role in powering electric vehicles, renewable energy systems, and portable electronics. This research presents a novel computational technique for simulating the internal processes of lithium-ion batteries, focusing on the electrochemical equilibrium and dynamics of these batteries. By leveraging the electrochemical method and simplifying complex differential equations through logical assumptions, the study develops a versatile tool for predicting the temporal and spatial distribution of electrode concentration, potential, and electrolyte dynamics in one dimension. The computational approach, executed in a C++ programming environment using computational fluid dynamics and the finite volume method, enables the simulation of diverse lithium-ion batteries.
The research addresses the challenges posed by these batteries, including the quest for increased energy and power density, effective heat management, and control and monitoring complexities. By providing valuable insights into optimizing battery performance, this study contributes to the development of sustainable energy storage solutions.
The proposed approach has the potential to shape a sustainable energy narrative, particularly in the context of all-electric and hybrid vehicles, and mitigating environmental concerns.

Keywords

Main Subjects


[1] Weiss M, Ruess R, Kasnatscheew J, Levartovsky Y, Levy NR, Minnmann P, et al. Fast charging of lithium-ion batteries: a review of materials aspects. Advanced Energy Materials. 2021;11(33):2101126.
[2] Nzereogu P, Omah A, Ezema F, Iwuoha E, Nwanya A. Anode materials for lithium-ion batteries: A review. Applied Surface Science Advances. 2022;9:100233.
[3] Kulova TL, Fateev VN, Seregina EA, Grigoriev AS. A brief review of post-lithium-ion batteries. International Journal of Electrochemical Science. 2020;15(8):7242–7259.
[4] Tao T, Lu S, Chen Y. A review of advanced flexible lithium-ion batteries. Advanced materials technologies. 2018;3(9):1700375.
[5] Kim T, Song W, Son DY, Ono LK, Qi Y. Lithiumion batteries: outlook on present, future, and hybridized technologies. Journal of materials chemistry A. 2019;7(7):2942–2964.
[6] Guo W, Sun Z, Vilsen SB, Meng J, Stroe DI. Review of “grey box” lifetime modeling for lithium-ion battery: Combining physics and data-driven methods. Journal of Energy Storage. 2022;56:105992.
[7] Laue V, R¨oder F, Krewer U. Practical identifiability of electrochemical P2D models for lithiumion batteries. Journal of Applied Electrochemistry. 2021;51(9):1253–1265.
[8] Alkhedher M, Al Tahhan AB, Yousaf J, Ghazal M, Shahbazian-Yassar R, Ramadan M. Electrochemical and thermal modeling of lithium-ion batteries: A review of coupled approaches for improved thermal performance and safety lithium-ion batteries. Journal of Energy Storage. 2024;86:111172.
[9] Kim K, Lee G, Chun H, Baek J, Pyeon H, Kim M, et al. Electrochemical–mechanical coupled model for computationally efficient prediction of longterm capacity fade of lithium-ion batteries. Journal of Energy Storage. 2024;86:111224.
[10] Fan C, Liu K, Ren Y, Peng Q. Characterization and identification towards dynamic-based electrical modeling of lithium-ion batteries. Journal of Energy Chemistry. 2024;92:738–758.
[11] Liu K, Gao Y, Zhu C, Li K, Fei M, Peng C, et al. Electrochemical modeling and parameterization towards control-oriented management of lithium-ion batteries. Control Engineering Practice. 2022;124:105176.
[12] Hosseininasab S, Lin C, Pischinger S, Stapelbroek M, Vagnoni G. State-of-health estimation of lithium-ion batteries for electrified vehicles using a reduced-order electrochemical model. Journal of Energy Storage. 2022;52:104684.
[13] Rojas C, Oca L, Lopetegi I, Iraola U, Carrasco J. A critical look at efficient parameter estimation methodologies of electrochemical models for Lithium-Ion cells. Journal of Energy Storage. 2024;80:110384.
[14] You HW, Bae JI, Cho SJ, Lee JM, Kim SH. Analysis of equivalent circuit models in lithium-ion batteries. AIP Advances. 2018;8(12).
[15] Zhang L, Peng H, Ning Z, Mu Z, Sun C. Comparative research on RC equivalent circuit models for lithium-ion batteries of electric vehicles. Applied Sciences. 2017;7(10):1002.
[16] Graule A, Oehler F, Schmitt J, Li J, Jossen A. Development and evaluation of a physicochemical equivalent circuit model for lithium-ion batteries. Journal of The Electrochemical Society. 2024;171(2):020503.
[17] Antony AJ, Selvajyothi K. A comparative performance analysis of electrical equivalent circuit models with the hysteresis effect of lithium iron phosphate batteries. International Journal of Green Energy. 2024;21(7):1476–1499.
[18] Amir S, Gulzar M, Tarar MO, Naqvi IH, Zaffar NA, Pecht MG. Dynamic equivalent circuit model to estimate state-of-health of lithium-ion batteries. IEEE Access. 2022;10:18279–18288.
[19] Khaleghi S, Hosen MS, Karimi D, Behi H, Beheshti SH, Van Mierlo J, et al. Developing an online data-driven approach for prognostics and health management of lithium-ion batteries. Applied Energy. 2022;308:118348.
[20] Wang S, Jin S, Bai D, Fan Y, Shi H, Fernandez C. A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries. Energy Reports. 2021;7:5562–5574.
[21] Lin M, Yan C, Wang W, Dong G, Meng J, Wu J. A data-driven approach for estimating state-ofhealth of lithium-ion batteries considering internal resistance. Energy. 2023;277:127675.
[22] Khumprom P, Yodo N. A data-driven predictive prognostic model for lithium-ion batteries based on a deep learning algorithm. Energies. 2019;12(4):660.
[23] Abu-Seif MA, Abdel-Khalik AS, Hamad MS, Hamdan E, Elmalhy NA. Data-Driven modeling for Li-ion battery using dynamic mode decomposition. Alexandria Engineering Journal. 2022;61(12):11277–11290.
[24] Dao TS, Vyasarayani CP, McPhee J. Simplification and order reduction of lithium-ion battery model based on porous-electrode theory. Journal of Power Sources. 2012;198:329–337.
[25] Versteeg HK. An introduction to computational fluid dynamics the finite volume method, 2/E. Pearson Education India; 2007.
[26] Doyle M, Fuller TF, Newman J. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society. 1993;140(6):1526.