Data cleaning and restoring method for vehicle battery big data platform

Shuangqi Li, Hongwen He, Pengfei Zhao, Shuang Cheng

Research output: Contribution to journalArticlepeer-review

10 Citations (SciVal)
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Battery is one of the most important and costly devices in electric vehicles (EVs). Developing an efficient battery management method is of great significance to enhancing vehicle safety and economy. Recently developed big-data and cloud platform computing technologies bring a bright perspective for efficient utilization and protection of vehicle batteries. However, a reliable data transmission network and a high-quality cloud battery dataset are indispensable to enable this benefit. This paper makes the first effort to systematically solve data quality problems in cloud-based vehicle battery monitoring and management by developing a novel integrated battery data cleaning framework. In the first stage, the outlier samples are detected by analyzing the temporal features in the battery data time series. The outlier data in the dataset can be accurately detected to avoid their impacts on battery monitoring and management. Then, the abnormal samples, including the noise polluted data and missing value, are restored by a novel future fusion data restoring model. The real electric bus operation data collected by a cloud-based battery monitoring and management platform are used to verify the performance of the developed data cleaning method. More than 93.3% of outlier samples can be detected, and the data restoring error can be limited to 2.11%, which validates the effectiveness of the developed methods. The proposed data cleaning method provides an effective data quality assessment tool in cloud-based vehicle battery management, which can further boost the practical application of the vehicle big data platform and Internet of vehicle.

Original languageEnglish
Article number119292
JournalApplied Energy
Early online date30 May 2022
Publication statusPublished - 15 Aug 2022

Bibliographical note

Funding Information:
This work is supported by the National Nature Science Foundation of China (No. U1864202 ).


  • Battery management system
  • Battery state estimation
  • Big data
  • Data cleaning
  • Electric vehicles
  • Internet of vehicle

ASJC Scopus subject areas

  • Building and Construction
  • Mechanical Engineering
  • Energy(all)
  • Management, Monitoring, Policy and Law


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