大数据驱动动力电池智能安全管理与控制方法研究

doi:10.19562/j.chinasae.qcgc.2023.10.007

Abstract

Abstract:

For the research on safety risk management and control of new energy vehicle power batteries， this paper discusses in detail the failure mechanism and types of power battery systems， clarifies the coupling relationship between battery consistency and safety based on big data statistical analysis， and summarizes the data-driven safety state prediction， fault diagnosis and warning method. Finally， a "vehicle-cloud"-integration-based safety control strategy is proposed for real-vehicle battery systems. This paper aims to provide theoretical guidance for realizing real-time monitoring of battery safety status and risk warning for real vehicles.

Key words: electric vehicles, power battery, fault diagnosis, data-driven, control strategy

Jichao Hong,Fengwei Liang,Haixu Yang,Kerui Li. Research on Intelligent Safety Management and Control Methods for Big-data-driven Battery Systems[J].Automotive Engineering, 2023, 45(10): 1845-1861.

Figures/Tables 16

诊断方法		优点	缺点
机器学习	人工神经网络^［79］	对模型的不确定性不敏感，精确度高，能高度捕捉非线性，对温度和其他不确定因素有适应性，易于在硬件中实现	建模复杂，特别是对于具有大量隐藏层和许多输入特征的模型，不考虑电池和工作条件的泛化是困难的，需要大量的内存来储存数据和算法
	随机森林分类器^［80］	分类能力强，计算成本低	对电池故障数据的质量和数量要求很高
	支持向量机^［81］	准确度高，建模复杂度较低，能够高度捕捉锂电池的非线性，需要的训练数据较少，诊断速度快	数据预处理费时且复杂，核函数选择和参数微调具有挑战性，模型的适应性和可靠性是一个问题
	高斯过程回归^［82］	准确性好，非参数性灵活，提供协方差来产生不确定性水平	诊断效果对内核函数的选择高度敏感，需要专业知识，计算复杂度高
	逻辑回归^［83］	准确性好，易于实施	适应大量的特征向量是一种挑战
信号处理	频谱分析^［84］	可以不用求取输出的时域表达式，使用方便；可以研究系统的稳定性和瞬态性能，便于分析系统特性；对线性和非线性状态都适用	只适用于分析和处理平稳信号，无法处理非平稳信号；结果不够直观，不易理解；无法体现发生的时刻，较难准确定位故障的发生
信号处理	小波变换^［85］	窗口大小随频率变化，在时域和频域中均具有局部分析功能，适合任何信号（平稳或非平稳）的分析	处理二维图像时有很大的局限性；对于高维函数不能最优表示
信息融合	样本熵^［86］	增加系统的生存能力；降低信息模糊度；扩展空间和时间覆盖范围；对故障信息描述更全面可靠	数据通信量较大；抗干扰能力差；寻找到合适的信息融合算法较为困难

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故障诱因	故障类型	故障危害
机械变形，过充/放电故障，活性材料诱因，热故障	内短路故障	内短路故障发生时通常伴随着放电和放热，是诱发热失控的主要故障
连接故障，机械变形，老化故障，水浸泡	外短路故障	电池温度将迅速上升，最终导致热失控
传感器故障，不一致性故障，充电器故障，充/放电结束时大速率充/放电	过充/放电故障	轻则降低电池寿命，重则出现材料相变、电解质分解等现象
连接部位因振动、碰撞、环境侵蚀而松动，出现老化故障	连接故障	导致电池内阻急剧增加，表现出电池不平衡和温度过热
老化故障，绝缘层受振动和碰撞磨损，环境侵蚀	绝缘故障	不仅损坏电池系统，而且高压导电电路也会威胁到乘客的生命
环境侵蚀，老化故障，传感器缺陷	传感器故障	导致电池管理系统误判当前电池状态，混淆系统功能
温度传感器故障，系统部件损坏	冷却系统故障	整个电池系统的温度将迅速上升

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Research on Intelligent Safety Management and Control Methods for Big-data-driven Battery Systems

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