TY - GEN
T1 - Lithium-Ion battery remaining useful life prediction method concerning temperature-influenced charging data
AU - Gong, Sikai
AU - He, Hongwen
AU - Zhao, Xuyang
AU - Shou, Yiwen
AU - Huang, Ruchen
AU - Yue, Hongwei
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Developing the remaining useful life (RUL) prediction technology for lithium-ion batteries can effectively provide information for battery maintenance and diagnosis. Although there has been some development in battery RUL prediction methods like model-based methods and data-driven methods, the influence of temperature on battery system is rarely considered. Besides, in the actual operation of the battery, the data used for RUL prediction is limited. Aiming at these problems, this paper proposed an integrated battery RUL prediction method. This method establishes a temperature correction formula for health indicators and uses neural network (NN) technique to determine the SOH of the battery. Unscented particle filter (UPF) is used with an empirical model to predict when the battery reaches the end-of-life (EOL) point. At last, the RUL value is calculated. Experiment data from the Sandia National Lab is deployed for NN training and method verification, and the results show that the proposed method has high accuracy than the conventional method.
AB - Developing the remaining useful life (RUL) prediction technology for lithium-ion batteries can effectively provide information for battery maintenance and diagnosis. Although there has been some development in battery RUL prediction methods like model-based methods and data-driven methods, the influence of temperature on battery system is rarely considered. Besides, in the actual operation of the battery, the data used for RUL prediction is limited. Aiming at these problems, this paper proposed an integrated battery RUL prediction method. This method establishes a temperature correction formula for health indicators and uses neural network (NN) technique to determine the SOH of the battery. Unscented particle filter (UPF) is used with an empirical model to predict when the battery reaches the end-of-life (EOL) point. At last, the RUL value is calculated. Experiment data from the Sandia National Lab is deployed for NN training and method verification, and the results show that the proposed method has high accuracy than the conventional method.
KW - RUL prediction
KW - aging characteristics
KW - lithium battery
KW - temperature influence
KW - unscented particle filter
UR - https://www.scopus.com/pages/publications/85174008953
U2 - 10.1109/ICECCME57830.2023.10253076
DO - 10.1109/ICECCME57830.2023.10253076
M3 - Conference contribution
AN - SCOPUS:85174008953
T3 - International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023
BT - International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023
Y2 - 19 July 2023 through 21 July 2023
ER -
