Li-ion Batteries Life Prediction Based on Variational Modal Decomposition and DAIPSO-GPR to Solve the Capacity Regeneration Phenomenon
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摘要: 锂离子电池应用时表现出的时变、动态、非线性等特征，以及容量再生现象，导致传统模型对锂离子电池剩余使用寿命(RUL)预测的准确性低，该文将变分模态分解(VMD)和高斯过程回归(GPR)以及动态自适应免疫粒子群(DAIPSO)结合，建立RUL预测模型。首先利用等压降放电时间分析法，提取健康因子，利用VMD对其进行分解处理，挖掘数据内在信息，降低数据复杂度，并针对不同分量，利用不同协方差函数建立GPR预测模型，有效捕获了数据的长期下降趋势和短期再生波动。利用DAIPSO算法优化GPR模型，实现核函数超参数的优化，建立了更准确的退化关系模型，最终实现剩余使用寿命的准确预测，以及不确定性表征。最后利用NASA电池数据进行验证，离线预测结果表明所提方法具有较高预测精度和泛化适应能力。
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关键词:
- 锂离子电池 /
- 剩余使用寿命 /
- 变分模态分解 /
- 高斯过程回归 /
- 动态自适应免疫粒子群
Abstract: Li-ion Batteries (LiBs) have time-varying, dynamic, and nonlinear characteristics in application, as well as the capacity regeneration phenomenon, leading to inaccurate prediction of the Remaining Useful Life (RUL) of LiBs by the traditional models. This paper combines the Variational Modal Decomposition (VMD) method with Gaussian Process Regression (GPR) and Dynamic Adaptive Immune Particle Swarm Optimization (DAIPSO) to build a RUL prediction model. Firstly, the Health Indicator is extracted by using the time interval of equal discharging voltage difference analysis method, decomposing Health Indicator by using VMD to mine the internal information of the data and reduce the data complexity. For different components, the GPR prediction model is established using different covariance functions, which can effectively capture the long-term declining trend and short-term regeneration phenomenon. The GPR model is optimized using the DAIPSO algorithm to achieve the optimization of the hyperparameters of the kernel function, which establishes a more accurate degradation relationship model to achieve an accurate prediction of RUL, and uncertainty characterization. Finally, NASA battery data is used for verification. The offline prediction results show that the proposed method has high prediction accuracy and generalization adaptability. -
表 1 模型预测误差
电池序号 评价指标 RMSE MAPE EOL EOP RUL PRUL RULerror B0005 0.0136 0.0064 124 125 44 45 1 B0006 0.0153 0.0077 109 110 29 30 1 B0007 0.0136 0.0063 166 168 86 88 2 B0018 0.0148 0.0075 97 96 37 36 1 
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表 2 VMD-DAIPSO-GPR与其他RUL预测方法的比较
电池序号 方法 训练数据长度 $ {\text{RU}}{{\text{L}}_{{\text{error}}}} $ $ {\text{P}}{{\text{E}}_{\text{r}}} $(%) B0005 LSTM 80 4 8.4 AUKF-GASVR 80 3 6.3 MK-RVM 80 2 4.2 VMD-DAIPSO-GPR 80 1 2.1
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