基于大数据的动力锂电池可靠性关键技术研究综述

doi:10.19799/j.cnki.2095-4239.2023.0316

摘要/Abstract

摘要：

锂离子电池作为电动汽车的主流储能元件，其可靠性下降将导致电动汽车性能异常退化或故障频发，甚至引发安全事故，发展先进的电池故障诊断与健康状态预估技术已成为动力锂电池可靠性领域的研究热点，而大数据与电动汽车的深度融合为电池可靠性关键技术发展提供了新思路。因此，本文首先介绍新能源汽车大数据平台的数据特点与数据清洗方法，简要回顾了大数据背景下可靠性关键技术在电动汽车与大数据平台的应用现状。然后围绕动力锂电池可靠性关键技术中电池故障诊断与健康状态预估研究，以数据驱动模型为核心，整理了基于大数据的电池故障诊断和健康状态预估的研究现状与方法，分析了电池故障诊断中基于机器学习、统计学、信号学、融合模型的优势与不足；对电池健康预估中基于历史运行数据、增量容量分析法提取特征的理论基础与电池健康预估模型进行综述。最后总结了当前研究在数据清洗、电池故障诊断和健康状态预估方面的局限性与面临的挑战，展望动力锂电池可靠性关键技术的未来发展方向。

关键词: 大数据, 锂离子电池, 可靠性, 故障诊断, 健康状态

Abstract:

Lithium-ion batteries are the mainstream energy storage component for electric vehicles. The reduced reliability of lithium-ion batteries leads to abnormal performance degradation or frequent failures for electric vehicles, resulting in accidents that threaten safety. The study of battery fault diagnosis and the state of health estimation technology has become a research hotspot in the field of lithium-ion battery reliability. The deep integration of big data and electric vehicles has provided new insights into the development of key technologies for improving the reliability of lithium-ion batteries. Herein, the data characteristics of the big data platform for new energy vehicles and the data cleaning methods they utilize are first introduced. The application of key reliability technologies based on the findings from big data in electric vehicles and big data platforms is briefly reviewed. Furthermore, the previous research on battery fault diagnosis and state of health estimation analyzing the reliability of lithium-ion batteries is reviewed. Considering a data-driven model as the core method of inquiry, the research status and methods used to analyze big data pertaining to the fault diagnosis and state of health estimation of lithium-ion batteries are discussed. The advantages and disadvantages of machine learning, statistics, signaling, and fusion models in battery fault diagnosis are discussed. The theoretical basis for extracting features based on historical operating data and incremental capacity analysis is reviewed, and the battery state of health estimation models are sorted appropriately. Finally, the limitations and challenges of the current research in data cleaning, fault diagnosis, and health status prediction of lithium-ion batteries are summarized. Thus, this paper provides the future direction for the development of key reliability technologies for estimating the reliability of lithium-ion batteries.

Key words: big data, lithium-ion battery, reliability, fault diagnosis, state of health

中图分类号:

TM 912

李放, 闵永军, 张涌. 基于大数据的动力锂电池可靠性关键技术研究综述[J]. 储能科学与技术, 2023, 12(6): 1981-1994.

Fang LI, Yongjun MIN, Yong ZHANG. Review of key technology research on the reliability of power lithium batteries based on big data[J]. Energy Storage Science and Technology, 2023, 12(6): 1981-1994.

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数据类型	数据内容	数据类型	数据内容
数据时间	2022/3/13 5:48	电机转速/(r/min)	1900
车辆状态	启动	电机转矩/(N·m)	114
充电状态	未充电	电机温度/℃	29
车速/(km/h)	49.8	电机控制器输入电压/V	667.9
累计里程/km	151287.3	电机控制器直流母线电流/A	33
总电压/V	653.5	最高电压单体代号	10
总电流/A	36.6	最高单体电压/V	3.308
SOC/%	97	最低电压单体代号	77
DC-DC状态	工作	最低单体电压/V	3.303
绝缘电阻/kΩ	19012	探针最高温度/℃	35
加速踏板行程/%	22	探针最低温度/℃	32
制动踏板状态	制动关	最高报警等级	无故障
定位状态	有效定位(东经/北纬)	通用报警标志	—
定位信息(经纬度)	120.53°/31.30°	单体电压(0～335号)/V	3.303～3.308
电机控制器温度/℃	29	探针温度(0～63号)/℃	32～35

故障诊断方法	算法类型	参考文献	优点	缺点
机器学习	聚类方法	[25-27]	避免了单一阈值引起的虚警，准确定位故障	聚类参数的选取无明确规则
	神经网络	[28-30]	预测精准度高，可实现未来状态预测	需求数据多，易陷入过拟合，调参复杂
	集成学习	[31-32]	预测精准度高，可实现未来状态预测	需求数据多，易陷入过拟合，调参复杂
统计学和信号学	统计指标	[33-34]	方法简单，移植性好	阈值选择困难，易产生虚警或预警滞后
	熵理论	[15]、[35]、 [37-39]	数据波动时能有效检测异常	数据波动达到一定程度故障才能被检测
	相关系数	[36]	算法复杂度低，占用内存小	受噪声影响较大
	信号分解	[40]	故障诊断灵敏，能识别早期细微故障征兆	在线应用受限
	状态表示法	[41]	故障诊断灵敏，能识别早期细微故障征兆	在线应用受限
融合模型	统计指标+神经网络	[42]	统计学与神经网络的故障诊断结果互为验证	单体参数的分布情况未知
	统计指标/信号分解+聚类方法	[43]、[45]、[47]	分级诊断节约平台内存，可在早期提前发现潜在故障	融合诊断模型构建复杂
	信号分解+孤立森林	[46]	分级诊断节约平台内存，可在早期提前发现潜在故障	融合诊断模型构建复杂

SOH预估方法	算法类型/IC曲线提取方法	参考文献	优点	缺点	量化指标(SOH预估)
基于运行数据	神经网络	[51-53]、[57]	预测精度高，对序列数据有良好的拟合跟踪能力	需求数据多，易陷入过拟合，调参复杂	最大相对误差为4.5%^[51]，0.053%^[53]；最大RMSE为0.232%^[52]，2%^[57]
	贝叶斯网络	[19]	概率模型实现了泛化与训练的平衡	核函数的选取对模型性能影响较大	最大绝对误差(mean absolute error，MAE)为4%^[19]；平均相对误差为1.75%^[56]
	高斯过程回归	[56]	概率模型实现了泛化与训练的平衡	核函数的选取对模型性能影响较大	最大绝对误差(mean absolute error，MAE)为4%^[19]；平均相对误差为1.75%^[56]
	箱线图	[58]	易于实现，移植性好	无法量化电池SOH	—
基于增量容量分析法	离散IC提取	[62]	IC提取更简单，减少内存消耗	计算易受噪声影响	—
	结合等效电路	[12]、[63]	提高了SOH预估精度	参数辨识复杂	最大MAE为1.5%^[63]
	结合单体串并联的电路结构	[64]	考虑了单体与电池模组的关系	单体不一致会影响模型性能	平均RMSE为2.04%
	结合插值法	[67]	弥补了离散数据的缺点	IC曲线光滑性不高	最大RMSE为1.61%
	结合支持向量回归	[69]	改善了由于精度导致无法提取IC曲线的情况	支持向量回归处理大数据运行时间过长	平均相对误差为4%

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