University of Sussex
Browse
2018_TII_fuel_data.pdf (1.05 MB)

An effective fuel level data cleaning and repairing method for vehicle monitor platform

Download (1.05 MB)
journal contribution
posted on 2023-06-09, 15:40 authored by Daxin Tian, Yukai Zhu, Xuting Duan, Junjie Hu, Zhengguo ShengZhengguo Sheng, Min Chen, Jian Wang, Yunpeng Wang
With energy scarcity and environmental pollution becoming increasingly serious, the accurate estimation of fuel consumption of vehicles has been important in vehicle management and transportation planning towards a sustainable green transition. Fuel consumption is calculated by fuel level data collected from high precision fuel level sensors. However, in the vehicle monitor platform, there are many types of error in the data collection and transmission processes, such as the noise, interference, and collision errors are common in the high speed and dynamic vehicle environment. In this paper, an effective method for cleaning and repairing the fuel level data is proposed, which adopts the threshold to acquire abnormal fuel data, the time quantum to identify abnormal data, and linear interpolation based algorithm to correct data errors. Specifically, a modified Gaussian Mixture Model (GMM) based on the synchronous iteration method is proposed to acquire the thresholds, which uses the Particle Swarm Optimization (PSO) algorithm and the steepest descent algorithm to optimize the parameters of GMM. The experiment results based on the fuel level data of vehicles collected over one month prove the modified GMM is superior to GMM-EM on fuel level data, and the proposed method is effective for cleaning and repairing outliers of fuel level data.

Funding

Bionic communications and networking for connected vehicles; G2114; ROYAL SOCIETY; IE160920

Doing More with Less Wiring: Mission-Critical and Intelligent Communication Protocols for Future Vehicles Using Power Lines; G2132; EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL; EP/P025862/1

History

Publication status

  • Published

File Version

  • Accepted version

Journal

IEEE Transactions on Industrial Informatics

ISSN

1551-3203

Publisher

Institute of Electrical and Electronics Engineers

Issue

1

Volume

15

Page range

410-416

Department affiliated with

  • Engineering and Design Publications

Research groups affiliated with

  • Communications Research Group Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2018-10-30

First Open Access (FOA) Date

2018-10-30

First Compliant Deposit (FCD) Date

2018-10-30

Usage metrics

    University of Sussex (Publications)

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC