Blockchain-Based Data Collection with Efficient Anomaly Detection for Estimating Battery State-of-Health

Ruochen Jin, Bo Wei, Yongmei Luo*, Tao Ren, Ruoqian Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


The number of electric vehicles in various countries has shown exponential growth so that the related industries to face the tremendous pressure of power batteries disposal. Efficient secondary use and recycling of power batteries require effective collection of battery data and reasonable estimation of battery state-of-health (SOH). In this paper, we propose a framework to collect battery charging data from different stakeholders with an anomaly detection method based on Isolation Forest with two features. Besides a score-based mechanism is adopted to do data screening and capture the data with good quality. Unlike prior works, our proposed method can exploit crowdsourced data to reduce the significant effort of battery data sensing and provide a data source scoring mechanism based on blockchain to improve the data quality and meet the requirement of reasonable estimation. In order to verify the effectiveness of the proposed collection method, a charge data test set is constructed based on the NASA battery data set. The simulation results indicate that the method increases the F-measure criteria up to 25.65% compared to the well-known anomaly detection algorithms. In addition, the proposed collection method outperforms the traditional method up to 10.9% in reducing the relative error when being used for SOH estimation.

Original languageEnglish
Article number9380777
Pages (from-to)13455-13465
Number of pages11
JournalIEEE Sensors Journal
Issue number12
Publication statusPublished - 2021 Jun 15


  • Electric vehicle
  • anomaly detection
  • battery charging
  • blockchain
  • state-of-health

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


Dive into the research topics of 'Blockchain-Based Data Collection with Efficient Anomaly Detection for Estimating Battery State-of-Health'. Together they form a unique fingerprint.

Cite this