TY - GEN
T1 - Data Enhancement and Aging Warning Within Cloud Battery Management System
AU - Tan, Jiaxin
AU - Wei, Zhongbao
AU - Lie, Tek Tjing
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - The Cloud Battery Management systems (CBMS) can potentially harness battery big data to enhance the safety and efficiency of battery utilization. However, data loss during transmission between cloud and edge severely compromises the performance of CBMS. To address the challenge of data loss during transmission, a Self-Attention-based Imputation for Time Series (SAITS) model is introduced for the first time to restore missing voltage, current, and state of charge (SOC) data. Additionally, a residual threshold-based aging warning method is proposed, detecting battery degradation by analyzing discrepancies between model predictions and actual measurements. Experimental results show that the proposed method outperforms existing techniques, with RMSE values of 0.0432V, 0.1424A, and 0.02% for a 20% loss rate. The aging warning mechanism effectively detects early signs of battery aging, improving the robustness of CBMS against data loss and aging effects.
AB - The Cloud Battery Management systems (CBMS) can potentially harness battery big data to enhance the safety and efficiency of battery utilization. However, data loss during transmission between cloud and edge severely compromises the performance of CBMS. To address the challenge of data loss during transmission, a Self-Attention-based Imputation for Time Series (SAITS) model is introduced for the first time to restore missing voltage, current, and state of charge (SOC) data. Additionally, a residual threshold-based aging warning method is proposed, detecting battery degradation by analyzing discrepancies between model predictions and actual measurements. Experimental results show that the proposed method outperforms existing techniques, with RMSE values of 0.0432V, 0.1424A, and 0.02% for a 20% loss rate. The aging warning mechanism effectively detects early signs of battery aging, improving the robustness of CBMS against data loss and aging effects.
KW - aging warning
KW - big data
KW - cloud battery management
KW - data enhancement
UR - https://www.scopus.com/pages/publications/105015516386
U2 - 10.1109/ITEC63604.2025.11097919
DO - 10.1109/ITEC63604.2025.11097919
M3 - Conference contribution
AN - SCOPUS:105015516386
T3 - 2025 IEEE/AIAA Transportation Electrification Conference and Electric Aircraft Technologies Symposium, ITEC+EATS 2025
BT - 2025 IEEE/AIAA Transportation Electrification Conference and Electric Aircraft Technologies Symposium, ITEC+EATS 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE/AIAA Transportation Electrification Conference and Electric Aircraft Technologies Symposium, ITEC+EATS 2025
Y2 - 18 June 2025 through 20 June 2025
ER -