Data quality analysis and improved strategy research on operations management system for electric vehicles

Abstract

It is very important for Operations Management System (OMS) and big data analysis application to improve the data quality of Electric Vehicle (EV) charging service. This paper focuses on the charging transaction record data from the Beijing EV charging OMS, and analyzes error types and distributed locations of the abnormal data. Based on the mathematical logic among various kinds of operation data, the data selection rules and processing method are proposed, and the system improved scheme is given. Through the design and application of data selection and processing module, the abnormal data can be timely detected and corrected. It is also beneficial for the system operation and maintenance to improve the acquisition data quality. The comparative analysis results verify the feasibility and effectivity of the proposed scheme. This research is a necessary guarantee for the big data technology application. Keywords-electric vehicle; operations management system; big data; data quality; data selection and processingA. PURPOSES OF DATA SELECTION • •

Big Data Optimization in Electric Power Systems: A Review

“…In power systems, data could be gathered from different sources such as renewables like solar and wind energies or other portions of energy technologies such as gas and fuel. In this regard, there are several applications of big data in energy domain that could be surveyed as renewables data use in biomass energy (Paro and Fadigas, 2011), marine energy (MacGillivray et al, 2014), (Wood et al, 2010), and wind energy (Billinton and Gao, 2008), (Kaldellis, 2002), energy consumption (Kung and Wang, 2015), or may consider energy demand response such as power demand (Liu et al (2013), and storage capacity (Goyena et al (2009), or could be analyzed as electric vehicles (EVs) (Jiang et al, 2016) such as driving pattern (Wu et al (2010), energy management (Su and Chow (2012), energy efficiency (Midlam-Mohler et al (2009), driving range (Rahimi-Eichi et al, 2015), (Lee and Wu, 2015), battery capacity (Shor, 1994), data quality (Zhang et al, 2015),…”

Energy big data: A survey

“…Zhang and Grijalva [121] proposed a data-driven queuing model for EV charging demand through performing big data analysis on smart meter measurements. In addition, Zhang et al [122] focused on the charging-transaction-record EV data for better charging service, by detecting, analyzing, and correcting the abnormal data with error types and distributed locations. Soares et al [123] presented a novel methodology based on Monte Carlo Simulation (MCS) to estimate the possible EVs states referring to their locations and periods of connection in the power grid.…”