Knowledge-data Coupling Driven Method for Data Cleaning and Recovery of Cloud Database of Lithium Batteries on Electric Vehicles

Xiao Wang; Nan Xi Zhou; Fan Yi Zheng; Da Si Zhou; Qi Ya Niu; Chun Shi Yang; Fei Chen

doi:10.1145/3588340.3588540

Knowledge-data Coupling Driven Method for Data Cleaning and Recovery of Cloud Database of Lithium Batteries on Electric Vehicles

Xiao Wang
, Nan Xi Zhou
, Fan Yi Zheng
, Da Si Zhou
, Qi Ya Niu
, Chun Shi Yang
, Fei Chen^*

^*Corresponding author for this work

School of Transportation Science and Engineering

Beihang University
Zeekr Automobile Co. Ltd

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

As the increasing inventory of new energy vehicles, large amounts of operational data have been generated and uploaded to cloud platform. Unfortunately, the numerous databases usually consist of abnormal data or data loss, which indicating that it cannot be applied for modelling and algorithm directly. Thus, the data cleaning and recovery is necessary which gets rid of abnormal data and become consequent especially for guaranteeing the precision and robustness for algorithm. In this article, a knowledge-data coupling driven method is proposed for data cleaning and recovery method, where a coupling model is used for simulating lost data. Based on simulation results originated from experiments, a satisfactory consistency is validated with more than 95% precision for recovery. The proposed method can be further promoted to cloud platform for lithium-ion batteries, fuel cell batteries and other energy storage system.

Original language	English
Title of host publication	Proceedings - 2022 International Conference on Big data, IoT, and Cloud Computing, ICBICC 2022
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450399548
DOIs	https://doi.org/10.1145/3588340.3588540
State	Published - 2 Dec 2022
Event	2022 International Conference on Big data, IoT, and Cloud Computing, ICBICC 2022 - Virtual, Online, China Duration: 2 Dec 2022 → 4 Dec 2022

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2022 International Conference on Big data, IoT, and Cloud Computing, ICBICC 2022
Country/Territory	China
City	Virtual, Online
Period	2/12/22 → 4/12/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Data cleaning
Data recovery
Knowledge-model coupling driven method

Access to Document

10.1145/3588340.3588540

Cite this

Wang, X., Zhou, N. X., Zheng, F. Y., Zhou, D. S., Niu, Q. Y., Yang, C. S., & Chen, F. (2022). Knowledge-data Coupling Driven Method for Data Cleaning and Recovery of Cloud Database of Lithium Batteries on Electric Vehicles. In Proceedings - 2022 International Conference on Big data, IoT, and Cloud Computing, ICBICC 2022 Article 26 (ACM International Conference Proceeding Series). Association for Computing Machinery . https://doi.org/10.1145/3588340.3588540

Wang, Xiao ; Zhou, Nan Xi ; Zheng, Fan Yi et al. / Knowledge-data Coupling Driven Method for Data Cleaning and Recovery of Cloud Database of Lithium Batteries on Electric Vehicles. Proceedings - 2022 International Conference on Big data, IoT, and Cloud Computing, ICBICC 2022. Association for Computing Machinery , 2022. (ACM International Conference Proceeding Series).

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title = "Knowledge-data Coupling Driven Method for Data Cleaning and Recovery of Cloud Database of Lithium Batteries on Electric Vehicles",

abstract = "As the increasing inventory of new energy vehicles, large amounts of operational data have been generated and uploaded to cloud platform. Unfortunately, the numerous databases usually consist of abnormal data or data loss, which indicating that it cannot be applied for modelling and algorithm directly. Thus, the data cleaning and recovery is necessary which gets rid of abnormal data and become consequent especially for guaranteeing the precision and robustness for algorithm. In this article, a knowledge-data coupling driven method is proposed for data cleaning and recovery method, where a coupling model is used for simulating lost data. Based on simulation results originated from experiments, a satisfactory consistency is validated with more than 95\% precision for recovery. The proposed method can be further promoted to cloud platform for lithium-ion batteries, fuel cell batteries and other energy storage system.",

keywords = "Data cleaning, Data recovery, Knowledge-model coupling driven method",

author = "Xiao Wang and Zhou, \{Nan Xi\} and Zheng, \{Fan Yi\} and Zhou, \{Da Si\} and Niu, \{Qi Ya\} and Yang, \{Chun Shi\} and Fei Chen",

note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 2022 International Conference on Big data, IoT, and Cloud Computing, ICBICC 2022 ; Conference date: 02-12-2022 Through 04-12-2022",

year = "2022",

month = dec,

day = "2",

doi = "10.1145/3588340.3588540",

language = "英语",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery ",

booktitle = "Proceedings - 2022 International Conference on Big data, IoT, and Cloud Computing, ICBICC 2022",

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Wang, X, Zhou, NX, Zheng, FY, Zhou, DS, Niu, QY, Yang, CS & Chen, F 2022, Knowledge-data Coupling Driven Method for Data Cleaning and Recovery of Cloud Database of Lithium Batteries on Electric Vehicles. in Proceedings - 2022 International Conference on Big data, IoT, and Cloud Computing, ICBICC 2022., 26, ACM International Conference Proceeding Series, Association for Computing Machinery , 2022 International Conference on Big data, IoT, and Cloud Computing, ICBICC 2022, Virtual, Online, China, 2/12/22. https://doi.org/10.1145/3588340.3588540

Knowledge-data Coupling Driven Method for Data Cleaning and Recovery of Cloud Database of Lithium Batteries on Electric Vehicles. / Wang, Xiao; Zhou, Nan Xi; Zheng, Fan Yi et al.
Proceedings - 2022 International Conference on Big data, IoT, and Cloud Computing, ICBICC 2022. Association for Computing Machinery , 2022. 26 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Knowledge-data Coupling Driven Method for Data Cleaning and Recovery of Cloud Database of Lithium Batteries on Electric Vehicles

AU - Wang, Xiao

AU - Zhou, Nan Xi

AU - Zheng, Fan Yi

AU - Zhou, Da Si

AU - Niu, Qi Ya

AU - Yang, Chun Shi

AU - Chen, Fei

PY - 2022/12/2

Y1 - 2022/12/2

N2 - As the increasing inventory of new energy vehicles, large amounts of operational data have been generated and uploaded to cloud platform. Unfortunately, the numerous databases usually consist of abnormal data or data loss, which indicating that it cannot be applied for modelling and algorithm directly. Thus, the data cleaning and recovery is necessary which gets rid of abnormal data and become consequent especially for guaranteeing the precision and robustness for algorithm. In this article, a knowledge-data coupling driven method is proposed for data cleaning and recovery method, where a coupling model is used for simulating lost data. Based on simulation results originated from experiments, a satisfactory consistency is validated with more than 95% precision for recovery. The proposed method can be further promoted to cloud platform for lithium-ion batteries, fuel cell batteries and other energy storage system.

AB - As the increasing inventory of new energy vehicles, large amounts of operational data have been generated and uploaded to cloud platform. Unfortunately, the numerous databases usually consist of abnormal data or data loss, which indicating that it cannot be applied for modelling and algorithm directly. Thus, the data cleaning and recovery is necessary which gets rid of abnormal data and become consequent especially for guaranteeing the precision and robustness for algorithm. In this article, a knowledge-data coupling driven method is proposed for data cleaning and recovery method, where a coupling model is used for simulating lost data. Based on simulation results originated from experiments, a satisfactory consistency is validated with more than 95% precision for recovery. The proposed method can be further promoted to cloud platform for lithium-ion batteries, fuel cell batteries and other energy storage system.

KW - Data cleaning

KW - Data recovery

KW - Knowledge-model coupling driven method

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M3 - 会议稿件

AN - SCOPUS:85179893180

T3 - ACM International Conference Proceeding Series

BT - Proceedings - 2022 International Conference on Big data, IoT, and Cloud Computing, ICBICC 2022

PB - Association for Computing Machinery

T2 - 2022 International Conference on Big data, IoT, and Cloud Computing, ICBICC 2022

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ER -

Wang X, Zhou NX, Zheng FY, Zhou DS, Niu QY, Yang CS et al. Knowledge-data Coupling Driven Method for Data Cleaning and Recovery of Cloud Database of Lithium Batteries on Electric Vehicles. In Proceedings - 2022 International Conference on Big data, IoT, and Cloud Computing, ICBICC 2022. Association for Computing Machinery . 2022. 26. (ACM International Conference Proceeding Series). doi: 10.1145/3588340.3588540

Knowledge-data Coupling Driven Method for Data Cleaning and Recovery of Cloud Database of Lithium Batteries on Electric Vehicles

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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