[논문]딥러닝과 특징 추출 기반 배터리 노화 상태 추정 방법

장문석; 이강석; 배성우

doi:10.6113/tkpe.2022.27.4.332

[국내논문] 딥러닝과 특징 추출 기반 배터리 노화 상태 추정 방법
Battery State-of-Health Estimation Method based on Deep-learning and Feature Engineering 원문보기

전력전자학회 논문지 = The Transactions of the Korean Institute of Power Electronics, v.27 no.4, 2022년, pp.332 - 338

장문석 (Dept. of Electrical Engineering, Hanyang University) , 이강석 (Dept. of Electrical Engineering, Hanyang University) , 배성우 (Dept. of Electrical Engineering, Hanyang University)

Abstract ▼ AI-Helper

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.

주제어

표/그림 (9)

표 TABLE I INVESTIGATED BATTERY DATA SPECIFICATIONS
그림 Fig. 1. Extracting a health indicator from a discharge curve.
그림 Fig. 2. Discharge time in the 3.9∼3.5V range in each discharge cycle.
그림 Fig. 3. Time series features (Discharge Time, Trend, Seasonal, and Residual) graphs.
그림 Fig. 4. Flowchart of the proposed SOH estimation process.
그림 Fig. 5. Architecture of a DNN.
표 TABLE II CORRELATION ANALYSIS
그림 Fig. 6. SOH estimation results before and after using the proposed method (a) estimated results of 1~260 cycles. (b) estimated results of 60~110 cycles.
표 TABLE III SOH ESTIMATION ERROR RATE

참고문헌 (14)

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