Optimal Signal Decomposition-based Multi-Stage Learning for Battery Health Estimation

TL;DR

This paper introduces OSL, a multi-stage machine learning approach utilizing optimal signal decomposition to improve battery health estimation accuracy, effectively capturing complex aging patterns and outperforming existing methods.

Contribution

The paper presents a novel OSL method combining optimized variational mode decomposition with multi-stage learning for enhanced battery health estimation.

Findings

OSL achieves a mean error of 0.26%.

OSL outperforms existing algorithms significantly.

The method is suitable for real-world battery management systems.

Abstract

Battery health estimation is fundamental to ensure battery safety and reduce cost. However, achieving accurate estimation has been challenging due to the batteries' complex nonlinear aging patterns and capacity regeneration phenomena. In this paper, we propose OSL, an optimal signal decomposition-based multi-stage machine learning for battery health estimation. OSL treats battery signals optimally. It uses optimized variational mode decomposition to extract decomposed signals capturing different frequency bands of the original battery signals. It also incorporates a multi-stage learning process to analyze both spatial and temporal battery features effectively. An experimental study is conducted with a public battery aging dataset. OSL demonstrates exceptional performance with a mean error of just 0.26%. It significantly outperforms comparison algorithms, both those without and those with suboptimal signal decomposition and analysis. OSL considers practical battery challenges and can be integrated into real-world battery management systems, offering a good impact on battery monitoring and optimization.