基于变分模态分解和高斯过程回归的锂离子电池剩余寿命预测方法

锂离子电池在实际工作中常处于间歇工作状态，存在容量再生现象，其性能退化呈现非单调性和随机性，无法采用传统的单一模型准确进行预测。针对上述问题，研究一种基于变分模态分解（Variational Mode Decomposition， VMD）和高斯过程回归（Gaussian Process Regression， GPR）的锂离子电池剩余寿命预测方法。首先，利用VMD将锂离子电池容量退化数据分解为一系列相对平稳的分量，并获取电池退化趋势分量及容量再生分量。然后针对不同分量的具体特性，构建合适的GPR预测模型以提高单个分量预测精度。最后，将分量预测结果叠加获取容量预测结果，进而实现电池剩余寿命预测。基于NASA研究中心锂电池容量退化数据进行实验分析，结果表明本文方法相比于直接采用GPR模型，降低了容量预测误差，并有效提高了剩余寿命预测精度。

Remaining Useful Life Prediction of Lithium-ion Batteries #br# Based on VMD and GPR Algorithm

The lithium-ion batteries often suffer from sudden and occasional capacity regeneration due to the complex and discontinued working conditions. Thus, the capacity degradation data shows the nonlinear and nonstationary trend, which makes it difficult to achieve high prediction accuracy. Therefore, a remaining useful life(RUL) prediction method of lithium-ion batteries based on variational mode decomposition (VMD) and Gaussian process regression(GPR) is proposed to treat this problem. Firstly, VMD is employed to decompose the capacity degradation data and to extract the global degradation, local regeneration, and random fluctuation components. Then, different GPR prediction models are built for each component by choosing suitable kernel functions. Lastly, the predicted components are superimposed to obtain the capacity prediction result and the predicted RUL. The proposed method is validated through a case study using NASA dataset. Results show that the proposed method outperforms the GPR models without VMD decomposing.