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Batteries and Fuel Cells

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A battery is a galvanic cell that is used as a source of electrical power for specific applications. Modern batteries exist in a multitude of forms to accommodate various applications, from tiny button batteries such as those that power wristwatches to the very large batteries used to supply backup energy to municipal power grids. Some batteries are designed for single-use applications and cannot be recharged (primary cells), while others are based on conveniently reversible cell reactions that...

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The Effect of Charging and Discharging Lithium Iron Phosphate-graphite Cells at Different Temperatures on Degradation

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Lithium-ion battery RUL prediction based on optimized VMD-SSA-PatchTST algorithm.

Pei Tang¹, Zetao Qiu², Zhongran Yao³

¹School of Automotive Engineering, Yancheng Institute of Technology, Yanchen, 224051, China.

Scientific Reports

|July 23, 2025

Summary

This summary is machine-generated.

This study introduces a new method for predicting lithium-ion battery remaining useful life (RUL) by combining signal decomposition with the PatchTST model. The advanced WOA-VMD-SSA-PatchTST approach significantly improves RUL prediction accuracy and reliability.

Keywords:

Lithium-ion battery Patch time series transformer Remaining useful life Variational modal decomposition

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Area of Science:

Battery Engineering
Artificial Intelligence
Signal Processing

Background:

Accurate remaining useful life (RUL) prediction is crucial for lithium-ion battery safety and system reliability.
Existing methods often struggle with the complex degradation patterns of batteries.

Purpose of the Study:

To develop a novel forecasting framework for enhanced lithium-ion battery RUL prediction.
To integrate modal decomposition techniques with the PatchTST deep learning model for improved accuracy.

Main Methods:

Spearman correlation identified key features related to battery capacity.
Variational Mode Decomposition (VMD), optimized by Whale Optimization Algorithm (WOA), decomposed capacity sequences.
PatchTST network, with hyperparameters tuned by Sparrow Search Algorithm (SSA), predicted RUL.

Main Results:

The proposed WOA-VMD-SSA-PatchTST model demonstrated superior performance compared to baseline models (CNN, GRU, PatchTST).
Experimental validation on NASA datasets confirmed the model's enhanced prediction accuracy and robustness.

Conclusions:

The fusion of modal decomposition and PatchTST offers a powerful approach for battery RUL forecasting.
This framework significantly advances the reliability and safety of lithium-ion battery systems.