A novel method for electric vehicle insulation detection based on the extended Kalman filter algorithm

IF 5.6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement Pub Date : 2024-03-06 DOI:10.1016/j.measurement.2024.114419

Zhi Li , Xiangyu Cui , Zhicheng He , Eric Li , Yufan Wang

{"title":"A novel method for electric vehicle insulation detection based on the extended Kalman filter algorithm","authors":"Zhi Li , Xiangyu Cui , Zhicheng He , Eric Li , Yufan Wang","doi":"10.1016/j.measurement.2024.114419","DOIUrl":null,"url":null,"abstract":"<div><p>Addressing the critical need for enhanced safety in the burgeoning electric vehicle market, this study presents a novel insulation detection method based on the Extended Kalman Filter (EKF) algorithm. To resolve the conflict between the response speed and detection accuracy of the insulation detection system caused by Y-capacitors, the Levenberg–Marquardt (L-M) algorithm is employed to effectively estimate the parameters of the feedback voltage model developed for an unbalanced electrical bridge. The state equation of the insulation testing system has been constructed, and the EKF algorithm is applied innovatively to monitor insulation resistance and Y-capacitance, demonstrating superior anti-interference capabilities. Simulation experiments have underscored the significant contributions of the L-M algorithm in expanding the detection scope of the system. Bench tests confirmed the ability of the approach to monitor changes in insulation resistance and Y-capacitance rapidly and accurately. Under normal conditions, the maximum relative error for insulation resistance measurement is 1.56%, with a response time of 1.5 s.</p></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"229 ","pages":"Article 114419"},"PeriodicalIF":5.6000,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Measurement","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S026322412400304X","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

Addressing the critical need for enhanced safety in the burgeoning electric vehicle market, this study presents a novel insulation detection method based on the Extended Kalman Filter (EKF) algorithm. To resolve the conflict between the response speed and detection accuracy of the insulation detection system caused by Y-capacitors, the Levenberg–Marquardt (L-M) algorithm is employed to effectively estimate the parameters of the feedback voltage model developed for an unbalanced electrical bridge. The state equation of the insulation testing system has been constructed, and the EKF algorithm is applied innovatively to monitor insulation resistance and Y-capacitance, demonstrating superior anti-interference capabilities. Simulation experiments have underscored the significant contributions of the L-M algorithm in expanding the detection scope of the system. Bench tests confirmed the ability of the approach to monitor changes in insulation resistance and Y-capacitance rapidly and accurately. Under normal conditions, the maximum relative error for insulation resistance measurement is 1.56%, with a response time of 1.5 s.

查看原文本刊更多论文

基于扩展卡尔曼滤波算法的电动汽车绝缘检测新方法

为了满足蓬勃发展的电动汽车市场对提高安全性的迫切需求，本研究提出了一种基于扩展卡尔曼滤波（EKF）算法的新型绝缘检测方法。为解决 Y 型电容器引起的绝缘检测系统响应速度和检测精度之间的矛盾，采用了 Levenberg-Marquardt (L-M) 算法，以有效估计为不平衡电桥开发的反馈电压模型的参数。构建了绝缘检测系统的状态方程，并创新性地将 EKF 算法应用于监测绝缘电阻和 Y 电容，展示了卓越的抗干扰能力。仿真实验强调了 L-M 算法在扩大系统检测范围方面的重要贡献。工作台测试证实了该方法能够快速、准确地监测绝缘电阻和 Y 电容的变化。在正常条件下，绝缘电阻测量的最大相对误差为 1.56%，响应时间为 1.5 秒。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Measurement 工程技术-工程：综合

CiteScore

10.20

自引率

12.50%

发文量

1589

审稿时长

12.1 months

期刊介绍： Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.