Algorithm-driven optimization of lithium-ion battery thermal modeling

Detailed modeling of battery thermal behaviour has high computational demand due to the presence of multiscale and multi-physics phenomena. For battery module/pack level simulation, a simple and accurate battery heat generation estimation is urgently required. This paper investigates the optimization of thermal numerical modeling for cylindrical 21,700 lithium-ion batteries with a nominal capacity of 5 Ah. A 3-dimensional battery model was built in the Multiphysics simulation software, COMSOL. The heat source of the model adopts a commonly used heat generation model incorporating irreversible and reversible heat. Correction factors, as a function of the state of charge, were introduced to the calculation of irreversible heat item. The Particle Swarm Optimization (PSO) algorithm, written in MATLAB, was coupled with the COMSOL numerical model to minimize the prediction error by varying correction factors. Battery surface temperature data under the continuous discharge tests (0.5C-3.5C) and dynamic loads were experimentally obtained and used to validate the model. The simulation results of the unoptimized model showed a discrepancy of up to 5 degrees C with the experimental data. After optimization, the prediction error was reduced to less than 0.5 degrees C on average. The optimized model was applied to predict the thermal behaviour of a battery module (16 aged cells) using oil-based immersion cooling. The pristine battery module with coolant flow velocities of 0.01 m/s was chosen as the baseline. The results indicate that the aged battery modules with internal resistance of 50 m omega and 75 m omega require coolant flow velocities of 0.05 m/s and 0.12 m/s, respectively, to achieve the baseline temperature. The study highlights a high-precision and low-computational cost approach for heat generation calculation of lithium-ion batteries is provided, which contributes to the development of battery thermal management systems.

Automated summary

This study provides a high-precision and low-computational cost approach for heat generation calculation of lithium-ion batteries, which contributes to the development of battery thermal management systems.