Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge

Sungwoo Jo, Sunkyu Jung and Taemoon Roh
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Sungwoo Jo: Semiconductor MCE Technology Center, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
Sunkyu Jung: Semiconductor MCE Technology Center, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
Taemoon Roh: Semiconductor MCE Technology Center, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea

Energies, 2021, vol. 14, issue 21, 1-16

Abstract: 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.

Keywords: data preprocessing; data-driven approaches; lithium-ion battery; neural network; state of charge; SOH estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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