Li-ion Batteries Life Prediction Based on Variational Modal Decomposition and DAIPSO-GPR to Solve the Capacity Regeneration Phenomenon

LIU Jinfeng; CHEN Haowei; HERBERT Ho-Ching Iu

doi:10.11999/JEIT211585

Volume 45 Issue 3

Mar. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(3): 1111-1120

LIU Jinfeng, CHEN Haowei, HERBERT Ho-Ching Iu. Li-ion Batteries Life Prediction Based on Variational Modal Decomposition and DAIPSO-GPR to Solve the Capacity Regeneration Phenomenon[J]. Journal of Electronics & Information Technology, 2023, 45(3): 1111-1120. doi: 10.11999/JEIT211585

Citation:

LIU Jinfeng, CHEN Haowei, HERBERT Ho-Ching Iu. Li-ion Batteries Life Prediction Based on Variational Modal Decomposition and DAIPSO-GPR to Solve the Capacity Regeneration Phenomenon[J]. Journal of Electronics & Information Technology, 2023, 45(3): 1111-1120. doi: 10.11999/JEIT211585

Citation:

LIU Jinfeng, CHEN Haowei, HERBERT Ho-Ching Iu. Li-ion Batteries Life Prediction Based on Variational Modal Decomposition and DAIPSO-GPR to Solve the Capacity Regeneration Phenomenon[J]. Journal of Electronics & Information Technology, 2023, 45(3): 1111-1120. doi: 10.11999/JEIT211585

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Li-ion Batteries Life Prediction Based on Variational Modal Decomposition and DAIPSO-GPR to Solve the Capacity Regeneration Phenomenon

doi: 10.11999/JEIT211585

1.
Engineering Research Center of Automotive Electronics Drive Control and System Integration, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China
2.
School of Electrical, Electronic and Computer Engineering, University of Western Australia, Crawley , WA 6009, Australia

Funds: The National Natural Science Foundation of Heilongjiang Province (LH2019E067)

Received Date: 2021-12-28
Rev Recd Date: 2022-05-30

Available Online: 2022-06-15

Publish Date: 2023-03-10

Abstract

Abstract

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.

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References(23)

References

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