用信号分解提升电池健康度估计精度，误差低至0.26%
Optimal Signal Decomposition-based Multi-Stage Learning for Battery Health Estimation
- 基于优化变分模态分解提取多频段电池信号特征
- 多阶段学习实现时空特征联合分析，均方误差仅0.26%
- 适合电池管理、寿命预测等工业场景应用
电池健康度估计对保障电池安全、降低使用成本至关重要。然而，受电池非线性老化规律及容量恢复现象影响，精准估计仍具挑战。本文提出一种基于最优信号分解的多阶段机器学习方法（OSL），通过优化变分模态分解（OVMD）从原始电池信号中提取不同频率成分，并采用多阶段学习机制有效分析电池的时空特征。在公开电池老化数据集上的实验表明，OSL表现优异，平均误差仅为0.26%，显著优于采用次优信号分解或无分解的对比算法。该方法充分考虑实际电池运行中的复杂问题，可直接集成于真实电池管理系统，对电池状态监控与优化具有重要应用价值。
原文摘要 · Abstract (English)
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.
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