This paper presents a novel data pre-processing model for estimating the state of health (SOH) of lithium-ion (Li-ion) batteries. The proposed method converts one-dimensional (1D) voltage data into two-dimensional (2D) data using a sliding window technique, creating a new dataset. This dataset is then used to automatically extract health-related features in the machine learning (ML) training process. The SOH is estimated by forecasting the battery voltage in the subsequent cycle. The performance of the proposed technique is evaluated using the NASA public dataset for Li-ion battery degradation analysis in four different scenarios. The results show a significant reduction in the root-mean-squared error (RMSE) of SOH estimation, demonstrating the effectiveness of the proposed method in automating the SOH estimation process and advancing the development of battery digital twins. The method eliminates the need for manual feature extraction and evaluation, making it a crucial step towards more efficient and accurate battery management systems (BMS).This paper presents a novel data pre-processing model for estimating the state of health (SOH) of lithium-ion (Li-ion) batteries. The proposed method converts one-dimensional (1D) voltage data into two-dimensional (2D) data using a sliding window technique, creating a new dataset. This dataset is then used to automatically extract health-related features in the machine learning (ML) training process. The SOH is estimated by forecasting the battery voltage in the subsequent cycle. The performance of the proposed technique is evaluated using the NASA public dataset for Li-ion battery degradation analysis in four different scenarios. The results show a significant reduction in the root-mean-squared error (RMSE) of SOH estimation, demonstrating the effectiveness of the proposed method in automating the SOH estimation process and advancing the development of battery digital twins. The method eliminates the need for manual feature extraction and evaluation, making it a crucial step towards more efficient and accurate battery management systems (BMS).