Researchers from the Université de Toulouse III – Paul Sabatier in France, including Johan Sebastian Suarez Sepúlveda, Edgar Hernando Sepúlveda-Oviedo, Bruno Jammes, and Corinne Alonso, have conducted a study comparing various optimization techniques for improving the accuracy of lithium-ion battery models. Their work, published in the journal Applied Energy, focuses on enhancing the performance of equivalent electrical models used to estimate key battery states such as state of charge, aging, and service life.
The study centers on the 2RC model, which balances physical interpretability and computational simplicity. The researchers applied this model to twelve batteries using four publicly available datasets from reputable research institutions. The methodology involved four stages: selecting the 2RC model, collecting experimental charge-discharge cycle data, applying various optimization techniques to minimize the error between experimental data and model estimates, and evaluating the accuracy and computational efficiency of these techniques.
The researchers compared traditional, metaheuristic, and bio-inspired optimization methods, including least squares optimization, particle swarm optimization, simulated annealing, and several nature-inspired variants. They found that bio-inspired techniques achieved greater accuracy than traditional methods without significantly increasing computational cost. Particle swarm optimization, in particular, demonstrated superior performance in terms of precision and robustness against local minima.
The integration of advanced optimization strategies significantly enhances the fidelity of equivalent electrical models. This improvement is crucial for accurate estimation of internal states in lithium-ion batteries, which are widely used in electric vehicles and aerospace systems. The practical applications of this research include better battery management systems, improved state of charge estimation, and more accurate predictions of battery aging and service life. These advancements can lead to more efficient and reliable energy storage solutions, benefiting the broader energy sector.
Source: Suarez Sepúlveda, J. S., Sepúlveda-Oviedo, E. H., Jammes, B., & Alonso, C. (2023). Parameter identification of lithium-ion batteries: A comparative study of various models and optimization techniques for battery modeling. Applied Energy, 335, 120849.
This article is based on research available at arXiv.