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http://dx.doi.org/10.6113/TKPE.2022.27.4.332

Battery State-of-Health Estimation Method based on Deep-learning and Feature Engineering  

Chang, Moon-Seok (Dept. of Electrical Engineering, Hanyang University)
Lee, Gang-Seok (Dept. of Electrical Engineering, Hanyang University)
Bae, Sungwoo (Dept. of Electrical Engineering, Hanyang University)
Publication Information
The Transactions of the Korean Institute of Power Electronics / v.27, no.4, 2022 , pp. 332-338 More about this Journal
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.
Keywords
Health indicator; State-of-health; Feature engineering; Li-ion battery; Deep neural network;
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