纯电动汽车电驱动系统故障诊断研究进展

doi:10.19562/j.chinasae.qcgc.2024.01.007

Abstract

Abstract:

In order to comprehensively review the current status and clarify the future trend of fault diagnosis in the electric drive system of pure electric vehicles， this paper first introduces the basic structure， functions and development history of the electric drive system of pure electric vehicles； then summarizes in detail the types and causes of faults of crucial components of the electric drive system of pure electric vehicles， and analyzes the main research status quo of fault diagnosis methods for key components of the electric drive system of pure electric vehicles. Then the domestic and international research progress and development of the diagnosis methods of the pure electric vehicle electric drive system are reviewed in detail from the four aspects of expert knowledge-driven， model-driven， signal-driven and data-driven， with the advantages and disadvantages of different methods compared. Finally， the problems faced by the fault diagnosis of electric drive system of pure electric vehicles and the development direction are analyzed and foreseen， and it is further discussed and pointed out that the future research on the fault diagnosis of electric drive system of pure electric vehicles can be focused on variable condition coupled fault diagnosis， micro-fault and pre-fault diagnosis， real-time online fault diagnosis， intelligent operation and maintenance， unknown fault diagnosis and system self-healing technology， etc.

Key words: pure electric vehicle, electrical drive powertrain system, fault diagnosis

Pengbo Zhang, Renxiang Chen, Yiming Shao, Shizheng Sun, Kaibo Yan. Research Review of Fault Diagnosis for Electric Drive Powertrain System of Pure Electric Vehicles[J].Automotive Engineering, 2024, 46(1): 61-74.

Figures/Tables 6

References 90

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故障诊断方法大类	具体诊断方法	代表文献	优点	缺点
模型驱动	数值模型驱动	［22， 24， 25， 27-30， 31-35］	1.实用性、稳定性强； 2.可重复操作且成本低； 3.效率高，响应速度快； 4.可诊断多种类型的故障	1.故障诊断准确性受模型精度的影响较大； 2.模型的维护需要不断更新和改进； 3.非稳态、非线性系统难适用； 4.复杂耦合故障诊断难度大
模型驱动	有限元驱动	［23， 26，36-38］	1.实用性、稳定性强； 2.可重复操作且成本低； 3.效率高，响应速度快； 4.可诊断多种类型的故障
信号驱动	电流信号驱动	［41， 42，44-49，56-57，59］	1.电气信号易获取，成本低； 2.故障诊断准确率高； 3.可实时监测	1.信号易受干扰，影响诊断结果； 2.不同故障可能对应同一种信号响应，诊断识别难度大； 3.特殊信号的传感器部署、监测及数据采集难度大且成本高
	电压信号驱动	［40， 43，58］
	振动信号驱动	［51，53-55］
	多信号融合	［50，52］
数据驱动	深度学习	［62-63，72，83］	1.诊断速度快，可实时监测，鲁棒性强； 2.无须事先建立数学模型，适用于多种不同类型的故障； 3.可通过不断学习来提高诊断准确度	1.对数据的质量要求高，需要对数据进行预处理和特征提取； 2.受数据质量和传感器精度等因素的影响，可能会出现误诊断； 3.需要较高的计算能力； 4.诊断结果可解释性较差
	人工神经网络	［64］
	卷积神经网络	［65，68-70，74，76，81，86］
	贝叶斯网络	［66］
	金字塔池网络	［67］
	生成对抗网络	［71］
	深度置信网络	［77］
	组合逻辑	［78］
	自编码器	［79，82］
	随机森林	［80］
	机器学习	［84］
	概率神经网络	［85］

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Research Review of Fault Diagnosis for Electric Drive Powertrain System of Pure Electric Vehicles

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