We propose a framework to optimize the parameters of a battery model ensuring that the synthetic data generated by a dedicated simulator closely aligns with empirical data obtained from laboratory experiments. Our framework is divised to handle distinct data cases, simulators and charging instances. We assume battery parameters can be classified in terms of their effect on battery aging. Accordingly, a two-stage optimization is considered. First, we optimize non-degradation parameters with respect to a specific metric and data set. Then, with these optimal values fixed, we optimize degradation parameters with respect to a different metric and data set. The results show that our two-stage optimization performs superiorly against other parameter configurations. We expect our tool to extend the experimental data obtained in a laboratory with new synthetic-generated data covering the full range of real-world scenarios. This supplemented data set is essential for training AI models to accurately predict the battery behavior and life expectancy across diverse scenarios.