基于充电电压的串联电池组早期多故障诊断

doi:10.19799/j.cnki.2095-4239.2023.0366

摘要/Abstract

摘要：

及时检测和准确识别电池组中不同类型的故障对电动汽车和电池储能系统的安全运行至关重要。早期故障诊断是电池管理系统(battery management system，BMS)预防锂离子电池热失控的关键，针对现有故障诊断方法无法通过微弱的电压波动识别早期故障的问题，提出基于充电电压的早期多故障诊断方法，实现对电压轻微变化的故障电池进行检测。首先，对电池数据进行预处理和特征提取，选择电流、荷电状态(state of charge，SOC)、温度、总电压作为输入量，建立基于极限梯度提升算法(extreme gradient boost，XGboost)的电压预测模型，并与长短期记忆网络(long short-term memory，LSTM)、支持向量机(support vector machine，SVM)和随机森林(random forest，RF)方法进行对比，结果表明，基于XGboost的电压预测方法能够在提升预测精度的同时，有效减少计算时间；然后，将测量与预测得到的电压残差作为故障特征，利用均值归一化(mean normalization，MN)方法放大故障特征，根据不同故障的MN值特性，实现电池组早期故障的分类；最后，通过基于密度的带有噪声的空间聚类算法(density-based spatial clustering of applications with noise，DBSCAN)对电池组多故障进行自动分类与定位，仿真结果表明，基于DBSCAN的聚类方法能够在250 s内识别多故障，实现对潜在电池单元故障的精准分类与定位。

关键词: 故障诊断, 充电电压, 电池组, 电压预测, 均值归一化

Abstract:

The timely detection and accurate identification of various fault types in battery packs are critical to the safe operation of electric vehicles and battery energy storage systems. Early fault diagnosis is the key to preventing the thermal runaway of lithium-ion batteries in a battery management system. However, existing fault diagnosis methods cannot identify early faults through weak voltage fluctuations. This study proposes an early multiple-fault diagnosis method based on the charging voltage to detect fault batteries with slight voltage changes. First, the battery data are preprocessed, and the features are extracted. The current, state of charge, temperature, and total voltage are selected as the input variables. Next, a voltage prediction model based on the extreme gradient boost (XGBoost) algorithm is established. Compared with the long short-term memory, support vector machine, and random forest methods, the XGBoost-based voltage prediction method effectively reduces the calculation time while improving the prediction accuracy. The voltage residuals obtained through measurement and prediction are used as the fault features, and the mean normalization (MN) method is employed to amplify them. The early faults of the battery packs are classified according to the MN value characteristics of different faults. Finally, density-based spatial clustering of applications with noise (DBSCAN) is used to automatically classify and locate the multiple faults of the battery packs. In conclusion, the clustering method based on DBSCAN identifies multiple faults within 250 s and realizes the accurate classification and location of potential battery unit faults.

Key words: fault diagnosis, charge voltage, battery pack, voltage prediction, mean normalization

中图分类号:

TM 912

段双明, 常智博. 基于充电电压的串联电池组早期多故障诊断[J]. 储能科学与技术, 2023, 12(10): 3221-3229.

Shuangming DUAN, Zhibo CHANG. Early multiple-fault diagnosis of series battery pack based on charging voltage[J]. Energy Storage Science and Technology, 2023, 12(10): 3221-3229.

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电压预测方法	MAE	MSE	计算时间/s
LSTM	1.37×10^-3	3.75×10^-6	75.1
SVM	1.77×10^-3	4.72×10^-6	0.94
RF	4.56×10^-4	3.25×10^-7	6.35
XGboost	2.42×10^-4	1.03×10^-7	1.34

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