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

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

Jinfeng Liu, Haowei Chen, Ho Ching Iu Herbert

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Translated title of the contributionLi-ion Batteries Life Prediction Based on Variational Modal Decomposition and DAIPSO-GPR to Solve the Capacity Regeneration Phenomenon
Original languageChinese (Traditional)
Pages (from-to)1111-1120
Number of pages10
JournalDianzi Yu Xinxi Xuebao
Volume45
Issue number3
DOIs
Publication statusPublished - Mar 2023

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