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영어

This study emphasizes the necessity of artificial intelligence for rapid and accurate battery state-of-charge (SOC) prediction, a critical parameter in battery condition prediction. We compared and evaluated time series models previously used for SOC prediction, namely LSTM, GRU, and Transformer. In addition to model comparison, we experimented with data preprocessing techniques suitable for battery SOC prediction. The study utilized NASA's aging dataset comprising different cells under various experimental conditions. A Sliding Window technique was employed to multiply data and evaluate model performance. The results showed that the GRU model most effectively predicted battery SOC without data multiplication. However, after applying the Sliding Window technique to generate more learning data, the Transformer model outperformed others with an average RMSE of 0.032 and MAE of 0.006 across all batteries. This research paves the way for advancements in AI technology based on Transformer models for improved analysis of battery conditions, which can benefit manufacturing and recycling processes.