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新能源汽车充电设施故障缺陷特征及智能诊断分析

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新能源汽车充电设施频繁出现的故障和缺陷已成为制约行业发展的关键因素,不仅影响了车主的充电体验,还可能对新能源汽车的推广和普及造成不利影响.文章对充电设施故障类型进行了分析,采用FMEA(失效模式及效应分析)进一步明确了故障模式及其对系统的影响,基于Drools规则引擎的策略实现了对充电设施故障的自动化检测和诊断.试验结果表明,该策略在识别如电模块输入电流过冲、供电模块故障等关键故障类型时表现出色,诊断准确度高达94.6%,具有较好的应用前景.
Characteristics and Intelligent Diagnosis Analysis of Faults and Defects in Charging Facilities for New Energy Vehicles
The frequent malfunctions and defects of charging facilities for new energy vehicles have become a key factor restricting the development of the industry.These malfunctions not only affect the charging experience of car owners,but may also have adverse effects on the promotion and popularization of new energy vehicles.This study first conducted statistical analysis on the types of charging facility faults,and further clarified the fault modes and their impact on the system using FMEA analysis.Based on the Drools rule engine strategy,automatic detection and diagnosis of charging facility faults were achieved.The experimental results show that the system performs well in identifying key fault types such as input current overshoot of electrical modules and power supply module faults,with a diagnostic accuracy of up to 94.6%,and has good application prospects.

new energy vehiclescharging facilitiesfault defectsDrools rule engineFMEA analysis

张维娟、刘侠、王洋、玛喜毕力格

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国网河北省电力有限公司营销服务中心,河北石家庄 050000

深圳市国电科技通信有限公司,广东深圳 518000

新能源汽车 充电设施 故障缺陷 Drools规则引擎 FMEA分析

2024

电力系统装备
《机电商报》社

电力系统装备

影响因子:0.008
ISSN:1671-8992
年,卷(期):2024.(12)