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Capacity estimation of lithium-ion batteries for various aging states through knowledge transfer SCIE SCOPUS

Title
Capacity estimation of lithium-ion batteries for various aging states through knowledge transfer
Authors
CHUN, HUIYONGKIM, JUNGSOOKIM, MINHOLEE , JANGWOOLEE, TAEKYUNGHAN, SOOHEE
Date Issued
2022-10
Publisher
Institute of Electrical and Electronics Engineers
Abstract
In this study, we propose a new capacity estimation scheme for various aging states of lithium-ion batteries based on an inverted bottleneck network (IBN) that learns electrochemical knowledge. Most existing capacity estimation schemes that employ simple models have limitations in accuracy because they cannot reflect the complex aging states inside the batteries. An electrochemical model is sufficiently sophisticated to estimate the capacity of lithium-ion batteries accurately; however, it is computationally expensive. Therefore, we transfer the knowledge of an electrochemical model that deals with the physico-chemical behavior of the batteries to a small-sized IBN. Then, the proposed neural network that learns both the synthetic and experimental data can estimate the capacity of different aging states accurately with a lower computational time. Further, the specific structure of the IBN allows the neural network to extract visible information, called an attention map, which represents the decision basis of the neural network when estimating capacity. We propose an estimation score for determining the reliability of the capacity estimation result by analyzing the attention map and input voltage data. We measured capacity data from 12 real 37 Ah-standard batteries with different aging states to validate the efficacy of the proposed approach. The resulting estimation errors were about 0.445 Ah, which correspond to only 1.202 % errors based on the 37 Ah capacity.
URI
https://oasis.postech.ac.kr/handle/2014.oak/110551
DOI
10.1109/TTE.2021.3130665
ISSN
2332-7782
Article Type
Article
Citation
IEEE Transactions on Transportation Electrification, 2022-10
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한수희HAN, SOOHEE
Dept of Electrical Enginrg
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