Blockchain-Based Data Collection With Efficient Anomaly Detection for Estimating Battery State-of-Health
Blockchain-Based Data Collection With Efficient Anomaly Detection for Estimating Battery State-of-Health
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DOI：
10.1109/jsen.2021.3066785
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发表时间：
2021-06
影响因子：
4.3
通讯作者：
Ruochen Jin;Bo Wei;Yongmei Luo;Tao Ren;Ruoqian Wu
中科院分区：
文献类型：
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作者：
Ruochen Jin;Bo Wei;Yongmei Luo;Tao Ren;Ruoqian Wu
各国电动汽车保有量呈指数级增长，使相关行业面临动力电池处置的巨大压力。动力电池的高效二次利用和回收需要有效收集电池数据并合理估计电池健康状态（SOH）。在本文中，我们提出了一个框架，通过基于具有两个特征的隔离森林的异常检测方法来收集不同利益相关者的电池充电数据。此外，还采用基于评分的机制来进行数据筛选并捕获高质量的数据。与之前的工作不同，我们提出的方法可以利用众包数据来减少电池数据传感的大量工作，并提供基于区块链的数据源评分机制，以提高数据质量并满足合理估计的要求。为了验证所提出的收集方法的有效性，基于NASA电池数据集构建了充电数据测试集。仿真结果表明，与众所周知的异常检测算法相比，该方法将F-measure标准提高了25.65%。此外，在用于 SOH 估计时，所提出的收集方法在降低相对误差方面优于传统方法高达 10.9%。
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.