| Title |
Battery State-of-Health Estimation Method based on Deep-learning and Feature Engineering |
| Authors |
Moon-Seok Chang ; Gang-Seok Lee ; Sungwoo Bae |
| DOI |
https://doi.org/10.6113/TKPE.2022.27.4.332 |
| Keywords |
Health indicator; State-of-health; Feature engineering; Li-ion battery; Deep neural network |
| Abstract |
This study proposes a battery state-of-health estimation method by applying a feature extraction technique. The technique that can improve estimation performance is the process of identifying and extracting meaningful data. To apply a data-driven-based aging state estimation method to batteries, health indicators are used as training data. However, limitations occur in extracting health indicators from charge/discharge cycles. This study proposes a deep-learning-based battery state-of-health estimation method that applies feature extraction techniques to compensate for this problem. According to the performance evaluation result of the proposed method, it has a low estimation error of 0.3887% based on an absolute error evaluation method. |