基于贝叶斯理论的电动汽车非车载充电机远程计量方法的研究

周頔; 郑文斌; 李林潼; 魏明晨; 屈曦颂; 郑惠政

doi:10.3969/j.issn.1000-1158.2023.07.16

PDF(7323 KB)

计量学报 ›› 2023, Vol. 44 ›› Issue (7) : 1107-1112. DOI: 10.3969/j.issn.1000-1158.2023.07.16

电磁学计量

基于贝叶斯理论的电动汽车非车载充电机远程计量方法的研究

周頔¹,郑文斌²,李林潼¹,魏明晨²,屈曦颂¹,郑惠政¹

作者信息 +

Research on Remote Measurement Method of Off-board Conductive Charger for Electric Vehicle Based on Bayes Theory

ZHOU Di¹,ZHENG Wen-bin²,LI Lin-tong¹,WEI Ming-chen²,QU Xi-song¹,ZHENG Hui-zeng¹

Author information +

文章历史 +

摘要

基于贝叶斯理论展开分析,探究了非车载充电机的远程计量误差评估方法和不确定度评定方法,提出了基于贝叶斯方法的非车载充电机远程计量算法。算法基于网状充电过程数据,利用充电机与电动汽车多对多的网状关系,实现网状参数比对与量值传递。应用该算法对测试用例数据和实车充电数据进行计算处理,超差判定结果正确,实际估算误差不高于3%,证实了该算法在非车载充电机远程计量方面的有效性。

Abstract

It conducts analysis based on Bayesian theory. The evaluation method of measurement uncertainty for off-board conductive charger is studied. It is proposed of a remote measurement algorithm based on Bayesian method. This algorithm realizes quantity-value transfer based on mesh charging process data. The result of the algorithm is correct in actual experiment. The actual estimate is off by less than 3%. The validity of the algorithm in remote measurement of off-board conductive charger is verified.

导出引用

周頔,郑文斌,李林潼,魏明晨,屈曦颂,郑惠政. 基于贝叶斯理论的电动汽车非车载充电机远程计量方法的研究[J]. 计量学报. 2023, 44(7): 1107-1112 https://doi.org/10.3969/j.issn.1000-1158.2023.07.16

ZHOU Di,ZHENG Wen-bin,LI Lin-tong,WEI Ming-chen,QU Xi-song,ZHENG Hui-zeng. Research on Remote Measurement Method of Off-board Conductive Charger for Electric Vehicle Based on Bayes Theory[J]. Acta Metrologica Sinica. 2023, 44(7): 1107-1112 https://doi.org/10.3969/j.issn.1000-1158.2023.07.16

中图分类号： TB971

参考文献

［1］纪红刚, 周凌. 电动汽车充电桩直流计量装置远程校验方法的研究［J］. 计量与测试技术, 2017, 44(6): 69-71.
Ji H G, Zhou L. A Research of Remote Calibration Methods for DC Metering of EV Charing Station［J］. Metrology & Measurement Technique, 2017, 44(6): 69-71.
［2］Chen Z R,Li F C,Xu X G,et al. Research on Remote Calibration System of DC Metering Device for Electric Vehicle Charging Piles Based on Embedded［C］//
2019 IEEE 3rd Information Technology,Networking,Electronic and Automation Control Conference,Jinan,China,2019.
［3］Yang L, Zhang D, Lin G, et al. Research on remote calibration and online monitoring system of electric energy metering device［J］. Journal of physics Conference series, 2019, 1303(1): 12119.
［4］Jebroni Z, Chadli H, Tidhaf B, et al. Design of a calibration board integrated in a Smart Electrical Energy Meter (hardware part)［C］//International Conference on Wireless Technologies. IEEE, 2017.
［5］Jebroni Z, Chadli H, Chadli E, et al. Remote calibration system of a smart electrical energy meter［J］. Journal of Electrical Systems, 2017, 13(4): 806-823.
［6］Liu F, He Q, Hu S, et al. Estimation of Smart Meters Errors Using Meter Reading Data［C］//2018 Conference on Precision Electromagnetic Measurements (CPEM 2018). 2018.
［7］朱银昕. 电动汽车充电站计量检定装置在线核查研究［D］. 长沙:湖南大学, 2019.
［8］Jiao Y, Li H, Hu C, et al. Data-Driven Evaluation for Error States of Standard Electricity Meters on Automatic Verification Assembly Line［J］. IEEE Transactions on Industrial Informatics, 2019, 15(9): 4999-5010.
［9］Chen S, Zhou D, Ye J, et al. A New Calibration Approach for Charging Facilities for Electric Vehicles via Machine Learning［C］//2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE, 2020.
［10］陈蓝生. 基于云平台的充电桩虚拟智能计量检测与管理装置设计［J］. 计量学报, 2019, 40(S1): 116-121.
Chen L S. Design of Virtual Intelligent Measuring and Management Device for Charging Pile Based on Cloud Platform［J］. Acta Metrologica Sinica, 2019, 40(S1): 116-121.
［11］蔡常青, 王健, 陈杭杭, 等. 非自动衡器测量不确定度评估及其在符合性判定中的应用［J］. 计量学报, 2021, 42(1): 59-65.
Cai C Q, Wang J, Chen H H, et al. Measurement Uncertainty EvaIuation Of NOn-Automatic Weighing Instrument and its Application in Conformity Assessment ［J］. Acta Metrologica Sinica, 2021, 42(1): 59-65.
［12］张遥奇, 任昀, 李娅, 等. 一种纯水机内置在线监测仪表的现场校准方法［J］. 计量学报, 2020, 41(1): 115-120.
Zhang Y Q, Ren J, Li Y, et al. A Calibration Method for Inner Installed On-line Monitoring Instrument of Pure Water Machine ［J］. Acta Metrologica Sinica, 2020, 41(1): 115-120.
［13］Ferrero A, Prioli M, Salicone S. Conditional Random-Fuzzy Variables Representing Measurement Results［J］. IEEE Transactions on Instrumentation & Measurement, 2015, 64(5): 1170-1178.
［14］Salicone S, Prioli M. Measuring uncertainty within the theory of evidence［M］. Cham: Springer, 2018.
［15］Ferrero A, Salicone S. The random-fuzzy variables: a new approach to the expression of uncertainty in measurement［J］. IEEE Transactions on Instrumentation & Measurement, 2004, 53(5): 1370-1377.
［16］Ferrero A, Salicone S, Jetti H V. Bayesian approach to uncertainty evaluation: is it always working［C］//19th International Congress of Metrology (CIM2019). 2019.
［17］Harsha Vardhana J, Alessandro F, Simona S. A modified Bayes theorem for reliable conformity assessment in industrial metrology［J］. Measurement, 2021, 184: 109967-1.
［18］Kok G J, Veen A M H, Harris P M, et al. Bayesian analysis of a flowmeter calibration problem［J］. Metrologia, 2015, 52(2): 392-399.