Edge computing for vehicle battery management: Cloud-based online state estimation

Shuangqi Li, Hongwen He, Zhongbao Wei, Pengfei Zhao

Research output: Contribution to journalArticlepeer-review

17 Citations (SciVal)
52 Downloads (Pure)


The adoption of electric vehicles (EVs), including battery EVs and hybrid EVs, makes it possible to reduce fossil fuel consumption and greenhouse gas emission. However, an accurate battery model and an effective battery management system should be established to enable this benefit. This paper proposes a novel cloud-assisted online battery management method based on artificial intelligence and edge computing technologies. Integration of cloud computation and big data resources into real-time vehicle battery management is realized by establishing a novel cloud-edge battery management system (CEBMS). A deep learning algorithm-based cloud data mining and battery modeling method is developed to estimate the voltage and energy state of the battery. The accuracy of the established cloud battery model outperforms the onboard battery management system by utilizing multi-sources information from different EVs. Meanwhile, a cloud-assisted battery management method is established at edge nodes in the onboard battery management unit to realize real-time state estimation locally. By using precise battery state estimation provided by the cloud platform, vehicle battery model accuracy can be significantly improved. The performance of the proposed battery management method is verified by a vehicle big data platform and battery pack experimental test bench. Experimental results justify the effectiveness of the proposed method in battery state estimation, which can help the EVs use and manage the battery more effectively.

Original languageEnglish
Article number105502
JournalJournal of Energy Storage
Issue numberPart B
Early online date7 Sept 2022
Publication statusPublished - 15 Nov 2022

Bibliographical note

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


  • Battery energy storage
  • Battery management system
  • Deep learning
  • Edge computing
  • Electric vehicle
  • State estimation

ASJC Scopus subject areas

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


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