TY - JOUR
T1 - A deep learning approach for state-of-health estimation of lithium-ion batteries based on differential thermal voltammetry and attention mechanism
AU - Zou, Bosong
AU - Wang, Huijie
AU - Zhang, Tianyi
AU - Xiong, Mengyu
AU - Xiong, Chang
AU - Sun, Qi
AU - Wang, Wentao
AU - Zhang, Lisheng
AU - Zhang, Cheng
AU - Ruan, Haijun
N1 - © 2023 Zou, Wang, Zhang, Xiong, Xiong, Sun, Wang, Zhang, Zhang and Ruan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
PY - 2023/6/14
Y1 - 2023/6/14
N2 - Accurate estimation of the State of Health (SOH) of lithium-ion batteries is crucial for ensuring their safe and reliable operation. Data-driven methods have shown excellent performance in estimating SOH, but obtaining high-quality and strongly correlated features remains a major challenge for these methods. Moreover, different features have varying importance in both spatial and temporal scales, and single data-driven models are unable to capture this information, leading to issues with attention dispersion. In this paper, we propose a data-driven method for SOH estimation leveraging the Bi-directional Long Short-Term Memory (Bi-LSTM) that uses the Differential Thermal Voltammetry (DTV) analysis to extract features, and incorporates attention mechanisms (AM) at both temporal and spatial scales to enable the model focusing on important information in the features. The proposed method is validated using the Oxford Battery degradation Dataset, and the results show that it achieves high accuracy and robustness in SOH estimation. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are around 0.4% and 0.3%, respectively, indicating the potential for online application of the proposed method in the cyber hierarchy and interactional network (CHAIN) framework.
AB - Accurate estimation of the State of Health (SOH) of lithium-ion batteries is crucial for ensuring their safe and reliable operation. Data-driven methods have shown excellent performance in estimating SOH, but obtaining high-quality and strongly correlated features remains a major challenge for these methods. Moreover, different features have varying importance in both spatial and temporal scales, and single data-driven models are unable to capture this information, leading to issues with attention dispersion. In this paper, we propose a data-driven method for SOH estimation leveraging the Bi-directional Long Short-Term Memory (Bi-LSTM) that uses the Differential Thermal Voltammetry (DTV) analysis to extract features, and incorporates attention mechanisms (AM) at both temporal and spatial scales to enable the model focusing on important information in the features. The proposed method is validated using the Oxford Battery degradation Dataset, and the results show that it achieves high accuracy and robustness in SOH estimation. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are around 0.4% and 0.3%, respectively, indicating the potential for online application of the proposed method in the cyber hierarchy and interactional network (CHAIN) framework.
KW - feature signal analysis
KW - attention mechanism
KW - state of health
KW - data-driven
KW - Bi-directional long short-term memory
UR - https://www.scopus.com/pages/publications/85163742121
U2 - 10.3389/fenrg.2023.1178151
DO - 10.3389/fenrg.2023.1178151
M3 - Article
SN - 2296-598X
VL - 11
JO - Frontiers in Energy Research
JF - Frontiers in Energy Research
M1 - 1178151
ER -
