Modeling and Simulation of Thermal Effects on Electrical Behavior in Lithium-Ion Cells
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Thermal effects exert a crucial influence on the electrical behavior of lithium-ion batteries, significantly impacting key parameters such as the open circuit voltage curve, internal impedance, and cell degradation rate. Furthermore, these effects may give rise to electrolyte loss, resulting in a reduction in capacity. The cycling of batteries inherently generates internal heat, establishing a direct relationship between cell temperature and power demand. This article aims to provide a methodology to model electrothermal relations and temperature influence on electrical behavior in lithium-ion cells, as well as a simulation of extended cell operation under arbitrary power loads, presenting a novel approach not previously explored. It does this by considering three models: the Bernardi model for heat generation within the cell, a thermal lumped model for the cell’s temperature, and the Vogel-Fulcher-Tammann model for the capacity change as a function of temperature. These models are then connected to a state-of-the-art open circuit voltage model of a cell, providing a connection from the thermal world back into the electrical world. Experiments with different power demands occur on the simulation, including estimation of thermal parameters with relative errors under 1%, visualizing the effects of the integrated models and potential for real-cell applications.
How to Cite
##plugins.themes.bootstrap3.article.details##
energy storage, batteries, electrothermal, simulation, lithium ion
Barcellona, S., Colnago, S., Montrasio, P., & Piegari, L. (2022). Integrated electro-thermal model for li-ion battery packs. Electronics, 11(10). doi: 10.3390/electronics11101537
Bernardi, D., Pawlikowski, E., & Newman, J. (1985, January). A General Energy Balance for Battery Systems. Journal of The Electrochemical Society, 132(1), 5–12. doi: 10.1149/1.2113792
Damay, N., Forgez, C., Bichat, M.-P., Friedrich, G., & Ospina, A. (2013). Thermal modeling and experimental validation of a large prismatic li-ion battery. In Iecon 2013 - 39th annual conference of the ieee industrial electronics society (p. 4694-4699). doi: 10.1109/IECON.2013.6699893
Diederichsen, K. M., Buss, H. G., & McCloskey, B. D. (2017, May). The Compensation Effect in the Vogel–Tammann–Fulcher (VTF) Equation for Polymer-Based Electrolytes. Macromolecules, 50(10), 3831–3840. doi: 10.1021/acs.macromol.7b00423
Forgez, C., Vinh Do, D., Friedrich, G., Morcrette, M., & Delacourt, C. (2010, May). Thermal modeling of a cylindrical LiFePO4/graphite lithium-ion battery. Journal of Power Sources, 195(9), 2961–2968. doi: 10.1016/j.jpowsour.2009.10.105
Garcıa-Coln, L. S., del Castillo, L. F., & Goldstein, P. (1989, October). Theoretical basis for the Vogel-Fulcher- Tammann equation. Physical Review B, 40(10), 7040–7044. doi: 10.1103/physrevb.40.7040
He, F., Li, X., & Ma, L. (2014, May). Combined experimental and numerical study of thermal management of battery module consisting of multiple Li-ion cells. International Journal of Heat and Mass Transfer, 72, 622–629. doi: 10.1016/j.ijheatmasstransfer.2014.01.038
Hoelle, S., Dengler, F., Zimmermann, S., & Hinrichsen, O. (2023, jan). 3d thermal simulation of lithium-ion battery thermal runaway in autoclave calorimetry: Development and comparison of modeling approaches. Journal of The Electrochemical Society, 170(1), 010509. doi: 10.1149/1945-7111/acac06
Hou, J., Yang, M., Wang, D., & Zhang, J. (2020). Fundamentals and challenges of lithium ion batteries at temperatures between −40◦ and 60◦c. Advanced Energy Materials, 10(18), 1904152. doi: https://doi.org/10.1002/aenm.201904152
Lam, L., Bauer, P., & Kelder, E. (2011, October). A practical circuit-based model for Li-ion battery cells in electric vehicle applications. In 2011 ieee 33rd international telecommunications energy conference (intelec). IEEE. doi: 10.1109/intlec.2011.6099803
Li, D., Wang, L., Duan, C., Li, Q., & Wang, K. (2022). Temperature prediction of lithium-ion batteries based on electrochemical impedance spectrum: A review. International Journal of Energy Research, 46(8), 10372- 10388. doi: https://doi.org/10.1002/er.7905
Maleki, H., & Howard, J. N. (2006, October). Effects of overdischarge on performance and thermal stability of a Li-ion cell. Journal of Power Sources, 160(2), 1395–1402. doi: 10.1016/j.jpowsour.2006.03.043
Paccha-Herrera, E., Calderon-Munoz, W. R., Orchard, M., Jaramillo, F., & Medjaher, K. (2020, August). Thermal Modeling Approaches for a LiCoO2 Lithium-ion Battery— A Comparative Study with Experimental Validation. Batteries, 6(3), 40. doi: 10.3390/batteries6030040
Pola, D., Guajardo, F., Jofre, E., Quintero, V., Perez, A., Acuna, D., & Orchard, M. (2016). Particle-filtering based state-of-health estimation and end-of-life prognosis for lithium-ion batteries at operation temperature. In Annual Conference of the Prognostics and Health Management Society 2016 (p. 10).
Pola, D. A., Navarrete, H. F., Orchard, M. E., Rabie, R. S., Cerda, M. A., Olivares, B. E., . . . Perez, A. (2015). Particle-Filtering-Based Discharge Time Prognosis for Lithium-Ion Batteries With a Statistical Characterization of Use Profiles. IEEE Transactions on Reliability, 64(2), 710–720. doi: http://dx.doi.org/10.1109/TR.2014.2385069
Qian, X., Xuan, D., Zhao, X., & Shi, Z. (2019, November). Heat dissipation optimization of lithium ion battery pack based on neural networks. Applied Thermal Engineering, 162, 114289. doi: 10.1016/j.applthermaleng.2019.114289
Spitthoff, L., Shearing, P. R., & Burheim, O. S. (2021). Temperature, ageing and thermal management of lithium-ion batteries. Energies, 14(5). doi: 10.3390/en14051248
Spitthoff, L., Wahl, M. S., Lamb, J. J., Shearing, P. R., Vie, P. J. S., & Burheim, O. S. (2023, April). On the Relations between Lithium-Ion Battery Reaction Entropy, Surface Temperatures and Degradation. Batteries, 9(5), 249. doi: 10.3390/batteries9050249
Wang, Y., Chen, X., Li, C., Yu, Y., Zhou, G., Wang, C., & Zhao, W. (2023). Temperature prediction of lithium ion battery based on artificial neural network model. Applied Thermal Engineering, 228, 120482. doi: 10.1016/j.applthermaleng.2023.120482
Yang, Z., Patil, D., & Fahimi, B. (2019). Electrothermal modeling of lithium-ion batteries for electric vehicles. IEEE Transactions on Vehicular Technology, 68(1), 170-179. doi: 10.1109/TVT.2018.2880138
Zhang, Y., Song, W., & Feng, Z. (2013). An Energy Efficiency Evaluation Research Based on Heat Generation Behavior of Lithium-Ion Battery. Journal of The Electrochemical Society, 160(11), A1927–A1930. doi: 10.1149/2.021311jes
Zhu, G., Kong, C., Wang, J. V., Kang, J., Yang, G., & Wang, Q. (2023). A fractional-order model of lithium-ion battery considering polarization in electrolyte and thermal effect. Electrochimica Acta, 438, 141461. doi: https://doi.org/10.1016/j.electacta.2022.141461
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.