A deep learning-based approach for battery life classification

Battery technology advancement has been a significant obstacle in several energy fields. Research on battery management is crucial, both in theory and in practice, particularly for estimating battery states. Batteries exhibit robust, time-varying, and non-linear characteristics that are intricate. Hence, precisely determining their state and health status poses a difficult challenge. In this paper, we proposes a Long Short-Term Memory deep neural network for the classification of the battery life based on measurable data, specifically the current-voltage charge-discharge characteristics. Compared to other methods that are currently available in the literature, the proposed approach is able to achieve superior tracking performances, as evidenced by the experimental results obtained by using real measured data in the laboratory.