Cleaning and Processing on the Electric Vehicle Telematics Data

Shuai Sun (), Jun Bi and Cong Ding
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Shuai Sun: Beijing Jiaotong University
Jun Bi: Beijing Jiaotong University
Cong Ding: Beijing Jiaotong University

A chapter in Smart Service Systems, Operations Management, and Analytics, 2020, pp 1-6 from Springer

Abstract: Abstract The development of the Internet of VehiclesInternet of Vehicles (IoV) (IoV) enables companies to collect an increasing amount of telematics dataTelematics data , which creates plenty of new business opportunities. How to improve the integrity and precision of electric vehicle telematics data to effectively support the operation and management of vehicles is one of the thorniest problems in the electric vehicle industry. With the purpose of accurately collecting and calculating the driving mileage of electric vehicles, a series of data cleaning and processingData cleaning and processing methodologies were conducted on the real-world electric vehicle telematics data. More specifically, descriptive statistics was conducted on the data, and the statistical results showed the quality of the data in general. Above all, the driving mileage data were segmented according to the rotate speed of the electric motor, and the anomaly threshold of the driving mileage data was obtained by the box-plot methodBox-plot method . Then, the typical anomalies in the data were screened out by the threshold and analysed, respectively. Ultimately, the real-time and offline abnormal processing algorithms are designed to process real-time and offline data, respectively. After debugging and improvement, these two sets of abnormal processing algorithms we designed have been able to run on a company’s big data cloud platform. According to the feedback of the operation results of real-world massive data, the two sets of algorithms can effectively improve the statistical accuracy of driving mileage data of electric vehicle.

Keywords: Internet of Vehicles; Telematics data; Data cleaning and processing; Box-plot method (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/978-3-030-30967-1_1

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