- author = "Rafael I. {De la Sotta} and Pablo A. Estevez and Jorge R. Vergara and Williams R. Calderon-Munoz",
- title = "Model-based battery thermal parameter optimization using symbolic regression",
- journal = "Journal of Energy Storage",
- year = "2023",
- volume = "73",
- pages = "109243",
- keywords = "genetic algorithms, genetic programming, Lithium-ion battery cooling, Battery thermal model, Symbolic regression",
- ISSN = "2352-152X",
-
URL = "
https://www.sciencedirect.com/science/article/pii/S2352152X23026415",
-
DOI = "
doi:j.est.2023.109243",
- size = "12 pages",
-
abstract = "Lithium-ion battery packs are commonly used in
electromobility and distributed energy storage.
However, keeping both cell temperature and inter-cell
temperature differences within operating ranges is
important to avoid a negative impact on performance and
lifespan. For this reason, modelling the thermal
behaviour of battery packs in different operating
conditions is crucial for both design and practical
use.
we optimize the parameters of a battery thermal model using a symbolic regression method based on evolutionary algorithms. We obtain power-type expressions for the drag coefficient, friction factor, and heat transfer correlation (Nusselt number), as a function of the Reynolds number and the cell spacing factor, plus the Prandtl number in the case of the Nusselt number. The model is adjusted with CFD simulations of battery modules containing up to 102 cells to obtain the steady-state temperature distribution. Compared to CFD simulations, the proposed model gets a mean absolute percentage error of 2.39 percent in estimating the temperature in a 53-cell battery pack. The adjusted parametric thermal model is validated with experimental data for cooling down a battery pack and a single cell, obtaining a mean absolute percentage error of 1.26 percent and 4.68 percent, respectively, in estimating the dynamic temperature balance.
The adjusted parametric thermal model and the mathematical expressions obtained for the drag coefficient, friction factor, and Nusselt number, can be useful for designing battery thermal management systems in electromobility and distributed energy storage.",
-
notes = "also known as \cite{DELASOTTA2023109243}
Department of Electrical Engineering, Universidad de Chile, Tupper 2007, Santiago, Chile",
