리튬 이온 배터리의 충전 상태 추정을 위한 LSTM 네트워크 학습 방법 비교
Comparison of Learning Techniques of LSTM Network for State of Charge Estimation in Lithium-Ion Batteries
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초록
안전하고 최적의 배터리 성능을 유지하기 위해 정확한 충전상태(SOC) 추정 기술이 필수적이다. 본 논문에서는 기존의 전류적산 방법이 가지고 있는 문제를 해결하기 위해 시간 종속성을 가지는 인공지능 기반의 LSTM을 이용한 SOC 추정 방법을 적용하였다. 훈련과 검증에 필요한 데이터는 전기적 실험을 통해 일정 크기로 방전된 전류, 전압, 온도를 수집하였고 학습을 위한 입력데이터의 질을 향상시키기 위해 데이터 전처리를 수행하였다. 또한, LSTM 모델의 구조 및 하이퍼파라미터 설정에 따른 학습 능력과 SOC 추정 성능을 비교하였다. 학습한 모델은 UDDS 프로파일을 통해 검증하였으며, RMSE 0.82%,MAX 2.54%의 추정 정확도를 달성하였다.
Abstract
To maintain the safe and optimal performance of batteries, accurate estimation of state of charge (SOC) is critical. Inthis paper, Long short-term memory network (LSTM) based on the artificial intelligence algorithm is applied to addressthe problem of the conventional coulomb-counting method. Different discharge cycles are concatenated to form thedataset for training and verification. In oder to improve the quality of input data for learning, preprocessing wasperformed. In addition, we compared learning ability and SOC estimation performance according to the structure ofLSTM model and hyperparameter setup. The trained model was verified with a UDDS profile and achieved estimatedaccuracy of RMSE 0.82% and MAX 2.54%.
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