This thesis investigates the optimization of electric vehicle battery parameters within a methodological framework that combines statistical analysis, clustering, and constraint-based optimization. The study begins with the construction of a comprehensive dataset, followed by systematic data preparation and codification, ensuring consistency and comparability across variables. Statistical tools such as Stepwise regression, LASSO, Pearson, and Spearman correlations are applied to identify dependencies between battery parameters and vehicle specifications, while a dependency matrix and hierarchical clustering are used to capture both the intensity and the structure of these relationships. Building on this analytical foundation, a custom optimization algorithm was developed to integrate both continuous and discrete variables into a unified model.