Gao Kaidi, Xu Jingyun, Li Zuxin, Cai Zhiduan, Jiang Dongming, Zeng Aigang
School of Engineering, Huzhou University, Huzhou City, 516007, China.
Institute of Technology, Huzhou College, Huzhou City, 313000, China.
ACS Omega. 2022 Jul 21;7(30):26701-26714. doi: 10.1021/acsomega.2c03043. eCollection 2022 Aug 2.
To be prepared for the capacity diving phenomena in future capacity deterioration, a hybrid method for predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs) is proposed. First, a novel empirical degradation model is proposed in this paper to improve the generalization applicability and accuracy of the algorithm. A particle filter (PF) algorithm is then implemented to generate the original error series using prognostic results. Next, a discrete wavelet transform (DWT) algorithm is designed to decompose and reconstruct the original error series to improve the data validity by reducing the local noise distribution information. A relatively less approximate component is selected as the reconstructed error series, which preserves the primary evolutionary information. Finally, to make full use of the information contained in the PF algorithm's prognosis results, the support vector regression (SVR) algorithm is utilized to correct the PF prognosis results. The results indicate that long-short-term deterioration progress and RUL prediction tasks can both benefit from significant performance improvements.
为应对未来容量退化中的容量跳水现象，提出了一种用于预测锂离子电池（LIB）剩余使用寿命（RUL）的混合方法。首先，本文提出了一种新颖的经验退化模型，以提高算法的泛化适用性和准确性。然后实施粒子滤波（PF）算法，利用预测结果生成原始误差序列。接下来，设计离散小波变换（DWT）算法对原始误差序列进行分解和重构，通过减少局部噪声分布信息来提高数据有效性。选择一个相对较少近似的分量作为重构误差序列，该序列保留了主要的演化信息。最后，为充分利用PF算法预测结果中包含的信息，利用支持向量回归（SVR）算法对PF预测结果进行校正。结果表明，长期和短期退化过程以及RUL预测任务都能从显著的性能提升中受益。
