Integration of growing self-organizing map and continuous genetic algorithm for grading lithium-ion battery cells

This study attempts to employ growing self-organizing map (GSOM) algorithm and continuous genetic algorithm (CGA)-based SOM (CGASOM) to improve the performance of SOM neural network (SOMnn). The proposed GSOM + CGASOM approach for SOMnn is consisted of two stages. The first stage determines the SOMnn topology using GSOM algorithm while the weights are fine-tuned by using CGASOM algorithm in the second stage. The proposed CGASOM algorithm is compared with other two clustering algorithms using four benchmark data sets, Iris, Wine, Vowel, and Glass. The simulation results indicate that CGASOM algorithm is able to find the better solution. Additionally, the proposed approach has been also employed to grade Lithium-ion cells and characterize the quality inspection rules. The results can assist the battery manufacturers to improve the quality and decrease the costs of battery design and manufacturing.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► This study attempts to employ growing self-organizing map (GSOM) algorithm and continuous genetic algorithm (CGA)-based SOM (GASOM) algorithm to improve the performance of SOM neural network (SOMnn). ► The simulation results indicate that GASOM algorithm is able to find the better solution. ► The proposed approach has been also employed to grade lithium-ion cells and characterize the quality inspection rules. ► The results can assist the battery manufacturers to improve the quality and decrease the costs of battery design and manufacturing.