Big data driven vehicle battery management method: A novel cyber-physical system perspective

Shuangqi Li, Pengfei Zhao

Research output: Contribution to journalArticlepeer-review

35 Citations (SciVal)
173 Downloads (Pure)


The establishment of an accurate battery model is of great significance to improve the reliability of electric vehicles (EVs). However, the battery is a complex electrochemical system with hardly observable and simulatable internal chemical reactions, and it is challenging to estimate the state of battery accurately. This paper proposes a novel flexible and reliable battery management method based on the battery big data platform and Cyber-Physical System (CPS) technology. First of all, to integrate the battery big data resources in the cloud, a Cyber-physical battery management framework is defined and served as the basic data platform for battery modeling issues. And to improve the quality of the collected battery data in the database, this work reports the first attempt to develop an adaptive data cleaning method for the cloud battery management issue. Furthermore, a deep learning algorithm-based feature extraction model, as well as a feature-oriented battery modeling method, is developed to mitigate the under-fitting problem and improve the accuracy of the cloud-based battery model. The actual operation data of electric buses is used to validate the proposed methodologies. The maximum data restoring error can be limited within 1.3% in the experiments, which indicates that the proposed data cleaning method is able to improve the cloud battery data quality effectively. Meanwhile, the maximum SoC estimation error in the proposed feature-oriented battery modeling method is within 2.47%, which highlights the effectiveness of the proposed method.

Original languageEnglish
Article number102064
JournalJournal of Energy Storage
Early online date10 Nov 2020
Publication statusPublished - 31 Jan 2021

Bibliographical note

Publisher Copyright:
© 2020 Elsevier Ltd

Copyright 2020 Elsevier B.V., All rights reserved.


  • Battery energy storage
  • Big data
  • Cyber-physical battery management system
  • Deep learning
  • Electric vehicles

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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