TY - JOUR
T1 - Online State of Health Estimation with Deep Learning Frameworks Based on Short and Random Battery Charging Data Segments
AU - Zhao, Lei
AU - Du, Xuzhi
AU - Yang, Zhigang
AU - Xia, Chao
AU - Xue, Jinwei
AU - Hoque, Muhammad Jahidul
AU - Fu, Wuchen
AU - Yan, Xiao
AU - Miljkovic, Nenad
N1 - Publisher Copyright:
© 2023 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited.
PY - 2023
Y1 - 2023
N2 - Lithium-ion (Li-ion) batteries find wide application across various domains, ranging from portable electronics to electric vehicles (EVs). Reliable online estimation of the battery’s state of health (SOH) is crucial to ensure safe and economical operation of battery-powered devices. Here, we developed three deep learning models to investigate their potential for online SOH estimation using partial and random charging data segments (voltage and charging capacity). The models employed were developed from the feed-forward neural network (FNN), the convolutional neural network (CNN) and the long short-term memory (LSTM) neural network, respectively. We show that the proposed deep learning frameworks can provide flexible and reliable online SOH estimation. Particularly, the LSTM-based estimation model exhibits superior performance across the test set in both direct learning and transfer learning scenarios, while the CNN and FNN-based models show slightly diminished performance, especially in the complex transfer learning scenario. The LSTM-based model achieves a maximum estimation error of 1.53% and 2.19% in the direct learning and transfer learning scenarios, respectively, with an average error as low as 0.28% and 0.30%. Our work highlights the potential for conducting online SOH estimation throughout the entire life cycle of Li-ion batteries based on partial and random charging data segments.
AB - Lithium-ion (Li-ion) batteries find wide application across various domains, ranging from portable electronics to electric vehicles (EVs). Reliable online estimation of the battery’s state of health (SOH) is crucial to ensure safe and economical operation of battery-powered devices. Here, we developed three deep learning models to investigate their potential for online SOH estimation using partial and random charging data segments (voltage and charging capacity). The models employed were developed from the feed-forward neural network (FNN), the convolutional neural network (CNN) and the long short-term memory (LSTM) neural network, respectively. We show that the proposed deep learning frameworks can provide flexible and reliable online SOH estimation. Particularly, the LSTM-based estimation model exhibits superior performance across the test set in both direct learning and transfer learning scenarios, while the CNN and FNN-based models show slightly diminished performance, especially in the complex transfer learning scenario. The LSTM-based model achieves a maximum estimation error of 1.53% and 2.19% in the direct learning and transfer learning scenarios, respectively, with an average error as low as 0.28% and 0.30%. Our work highlights the potential for conducting online SOH estimation throughout the entire life cycle of Li-ion batteries based on partial and random charging data segments.
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U2 - 10.1149/1945-7111/acf8ff
DO - 10.1149/1945-7111/acf8ff
M3 - Article
AN - SCOPUS:85174271464
SN - 0013-4651
VL - 170
JO - Journal of the Electrochemical Society
JF - Journal of the Electrochemical Society
IS - 9
M1 - 090537
ER -