基于WOA-VMD和香农熵的锂电池早期故障诊断研究

Research on Early Fault Diagnosis of Lithium Battery Based on WOA-VMD and Shannon Entropy

针对当前电池管理系统无法诊断早期故障的问题,本文提出了一种基于WOA-VMD和香农熵的锂电池早期故障诊断方法。首先引入鲸鱼优化算法对变分模态分解算法进行参数寻优,提高变分模态分解算法的分解效果,使之分解得到包含更多故障特征信息的本征模态函数分量,再对单体电池电压信号进行分解重构,减少测量噪声和额外激励电压造成的影响。进而采用滑动窗口计算单体电压的香农熵极差和单体电压离差的总体香农熵,设置合适的阈值进行早期故障诊断。经过实际车辆数据验证,该方法可以提前10 min左右进行故障预警,且对于无故障车辆不会产生虚假预警,具有较强的鲁棒性和可靠性。

A lithium battery early fault diagnosis method based on WOA-VMD and Shannon entropy is pro-posed in this paper to solve the problem of current battery management systems being unable to diagnose early faults.Firstly,the whale optimization algorithm is introduced to optimize the parameters of the variational mode de-composition algorithm to improve its decomposition performance and obtain intrinsic mode function components con-taining more fault feature information.Then,the voltage signal of the individual battery is decomposed and recon-structed to reduce the impact of measurement noise and additional excitation voltage.Furthermore,a sliding window is used to calculate the Shannon entropy range of individual voltage and the overall Shannon entropy of individual voltage dispersion to set appropriate thresholds for early fault diagnosis.After verification with actual vehicle data,this method can provide fault warning about 10 minutes in advance without generating false warnings for vehicles without faults.It has strong robustness and reliability.