<![CDATA[The rapid growth of the automotive industry has accelerated the adoption of electric vehicles (EVs), in which battery systems play a critical role as the primary energy storage component. Efficient battery charging during the production process is therefore essential to ensure product quality, operational efficiency, and long-term battery performance. This study aims to optimize the battery cell charging process in electric vehicle manufacturing by implementing a smart charging strategy based on deep learning techniques, specifically the LSTM model. Historical charging data and relevant operational variables, including voltage, current, and time characteristics, are utilized to train the LSTM model to predict optimal charging parameters. The proposed approach enables adaptive and intelligent control of charging current and voltage profiles during production. The results demonstrate that the LSTM-based smart charging method improves charging efficiency, reduces potential battery degradation, and enhances manufacturing process consistency compared to conventional charging methods. In conclusion, the application of deep learning–based smart charging provides a promising solution for optimizing EV battery production processes. This research contributes to the development of intelligent battery management systems and supports the advancement of sustainable transportation and EV manufacturing technologies.]]>
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