TY - JOUR
T1 - 基于VMD和DAIPSO-GPR解决容量再生现象的锂离子电池寿命预测研究
AU - Liu, Jinfeng
AU - Chen, Haowei
AU - Herbert, Ho Ching Iu
N1 - Funding Information:
The National Natural Science Foundation of Heilongjiang Province (LH2019E067)
Publisher Copyright:
© 2023 Science Press. All rights reserved.
PY - 2023/3
Y1 - 2023/3
N2 - Li-ion Batteries (LiBs) have time-varying, dynamic, and nonlinear characteristics in application, as well as the capacity regeneration phenomenon, leading to inaccurate prediction of the Remaining Useful Life (RUL) of LiBs by the traditional models. This paper combines the Variational Modal Decomposition (VMD) method with Gaussian Process Regression (GPR) and Dynamic Adaptive Immune Particle Swarm Optimization (DAIPSO) to build a RUL prediction model. Firstly, the Health Indicator is extracted by using the time interval of equal discharging voltage difference analysis method, decomposing Health Indicator by using VMD to mine the internal information of the data and reduce the data complexity. For different components, the GPR prediction model is established using different covariance functions, which can effectively capture the long-term declining trend and short-term regeneration phenomenon. The GPR model is optimized using the DAIPSO algorithm to achieve the optimization of the hyperparameters of the kernel function, which establishes a more accurate degradation relationship model to achieve an accurate prediction of RUL, and uncertainty characterization. Finally, NASA battery data is used for verification. The offline prediction results show that the proposed method has high prediction accuracy and generalization adaptability.
AB - Li-ion Batteries (LiBs) have time-varying, dynamic, and nonlinear characteristics in application, as well as the capacity regeneration phenomenon, leading to inaccurate prediction of the Remaining Useful Life (RUL) of LiBs by the traditional models. This paper combines the Variational Modal Decomposition (VMD) method with Gaussian Process Regression (GPR) and Dynamic Adaptive Immune Particle Swarm Optimization (DAIPSO) to build a RUL prediction model. Firstly, the Health Indicator is extracted by using the time interval of equal discharging voltage difference analysis method, decomposing Health Indicator by using VMD to mine the internal information of the data and reduce the data complexity. For different components, the GPR prediction model is established using different covariance functions, which can effectively capture the long-term declining trend and short-term regeneration phenomenon. The GPR model is optimized using the DAIPSO algorithm to achieve the optimization of the hyperparameters of the kernel function, which establishes a more accurate degradation relationship model to achieve an accurate prediction of RUL, and uncertainty characterization. Finally, NASA battery data is used for verification. The offline prediction results show that the proposed method has high prediction accuracy and generalization adaptability.
KW - Dynamic Adaptive Immune Particle Swarm Optimization (DAIPSO)
KW - Gaussian Process Regression (GPR)
KW - Li-ion batteries
KW - Remaining Useful Life (RUL)
KW - Variational Mode Decomposition (VMD)
UR - http://www.scopus.com/inward/record.url?scp=85165220633&partnerID=8YFLogxK
U2 - 10.11999/JEIT211585
DO - 10.11999/JEIT211585
M3 - Article
AN - SCOPUS:85165220633
SN - 1009-5896
VL - 45
SP - 1111
EP - 1120
JO - Dianzi Yu Xinxi Xuebao
JF - Dianzi Yu Xinxi Xuebao
IS - 3
ER -