Swarm Evolutionary Technique for Dynamic Response Modeling of Lithium Cell Using Updated Multi-Objective Ant-Lion Optimizer
https://doi.org/10.55989/RVRG6908
Autors: S. A. Bruce-Allison, B. A. Wokoma, E. N. Osegi
Date: May 15, 2023
Edition: May 2023 Edition
Volume: 4
Issue: 1
Pages: 6 (239 - 244)
Abstract:
In this research paper, a swarm evolutionary modeling technique called the Updated Multi-Objective Ant-Lion Optimizer (UMO-ALO) is applied to the modeling of Open Circuit Voltage (OCV) state-of-charge (SOC) of Lithium-ion cells with cathode-anode composition (LiMnO2/Li4TiO). The model approximates the battery SOC by considering several battery cell internal physical parameters and a linear fitness function governed by two objectives involving the charge and discharge response of the battery cell. Bound limited coefficients of the physical parameters are used in the optimization process. The UMO-ALO uses a special update procedure to reduce the computational run-time of standard MO-ALO and hence speeds up the generic optimizer. Simulations were performed using a very small real laboratory data obtained from the relevant field studies and for different trial-run configurations of UMO-ALO. The results from these simulations show very good fitness responses close to zero margins for both trial-run configurations.
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