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

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


研究成果: Article査読

17 被引用数 (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.

ジャーナルIEEE Sensors Journal
出版ステータスPublished - 2021 6月 15

ASJC Scopus subject areas

  • 器械工学
  • 電子工学および電気工学


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