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학술지 Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge
Cited 29 time in scopus Download 96 time Share share facebook twitter linkedin kakaostory
조성우, 정순규, 노태문
Energies, v.14 no.21, pp.1-16
21HB1500, 휴대 단말용 급격한 전하방전 저전압 스위칭 소자 원천기술 개발, 노태문
Because lithium-ion batteries are widely used for various purposes, it is important to estimate their state of health (SOH) to ensure their efficiency and safety. Despite the usefulness of model-based methods for SOH estimation, the difficulties of battery modeling have resulted in a greater emphasis on machine learning for SOH estimation. Furthermore, data preprocessing has received much attention because it is an important step in determining the efficiency of machine learning methods. In this paper, we propose a new preprocessing method for improving the efficiency of machine learning for SOH estimation. The proposed method consists of the relative state of charge (SOC) and data processing, which transforms time-domain data into SOC-domain data. According to the correlation analysis, SOC-domain data are more correlated with the usable capacity than time-domain data. Furthermore, we compare the estimation results of SOC-based data and time-based data in feedforward neural networks (FNNs), convolutional neural networks (CNNs), and long short-term memory (LSTM). The results show that the SOC-based preprocessing outperforms conventional time-domain data-based techniques. Furthermore, the accuracy of the simplest FNN model with the proposed method is higher than that of the CNN model and the LSTM model with a conventional method when training data are small.
KSP 제안 키워드
Battery Modeling, Battery state, CNN model, Conventional methods, Convolution neural network(CNN), Correlation Analysis, Data Preprocessing, Data processing, FNN model, Feedforward neural networks, Ion batteries
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