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Abstract
Safety accidents caused by Lithium-ion (Li-ion) batteries are numerous in recent years. Therefore, more and more attention has been drawn to the Remaining Useful Life (RUL) prediction and health status monitoring for Li-ion batteries. This paper proposes a deep learning method that combines the Forgetting Online Sequential Extreme Learning Machine (FOS-ELM) with the Hybrid Grey Wolf Optimizer (HGWO) algorithm and attention mechanism for the Prognostic and Health Management (PHM) of Li-ion battery. First, we use the Variational Mode Decomposition (VMD) to denoise the raw data before the training. Then the key parameters optimization of the FOS-ELM model based on the HGWO algorithm is introduced. Finally, we apply the attention mechanism to further improve the accuracy of the algorithm. Compared with traditional neural network methods, the method proposed in this paper has higher efficiency and accuracy.
Highlights
Lithium-ion (Li-ion) battery is a new generation of green high-energy rechargeable battery, which has outstanding advantages such as long lifecycle and no memory effect
This paper mainly compares the performance of our method with other proposed methods on State of Health (SOH) monitoring and Remaining Useful Life (RUL) prediction for Li-ion battery
We use a new HA-FOSELM method to improve the accuracy of battery SOH monitoring and RUL prediction
Summary
Lithium-ion (Li-ion) battery is a new generation of green high-energy rechargeable battery, which has outstanding advantages such as long lifecycle and no memory effect. It has been extensively used in new energy vehicles, aerospace, and other fields. Complex physical and chemical changes will occur within the Li-ion battery in the process of use. The chemical reaction inside the Li-ion will lead to the consumption of battery polarity, electrolyte and separator. The inner lifetime of the Li-ion battery will be gradually reduced due to some irreversible reactions or even failure, which will bring serious problems to the production process and even cause significant losses. Since the battery status was ignored, The associate editor coordinating the review of this manuscript and approving it for publication was Yongquan Sun
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