基于时间序列聚类的动力电池配组方法研究

项目名称： 基于时间序列聚类的动力电池配组方法研究

项目编号： No.61471151

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 无线电电子学、电信技术

项目作者： 何志伟

作者单位： 杭州电子科技大学

项目金额： 85万元

中文摘要： 动力电池配组技术是电动汽车等领域的关键技术之一，成功的动力电池配组可以提高提高二次电池质量、降低企业的服务成本、减少环境污染。本项目提出一种基于电池多种充放电特性时间序列聚类的动力电池配组方法：通过对充放电特性时间序列进行非监督学习，抽取出能表征原始时间序列的若干特征子形状；进而基于这些特征子形状，基于合适的距离度量，采用合适的聚类算法完成动力电池的配组。主要研究内容：（1）时间序列的特征子形状非监督学习方法；（2）基于特征子序列集合的时间序列聚类方法；（3）基于时间序列聚类的动力电池配组技术。通过本项目的研究，有望揭示表征动力电池一致性的关键外在特性；提出一种高效高性能的基于特征子形状集合的时间序列聚类算法，有效降低时间序列中噪声对聚类效果的影响；在保证动力电池配组效率的同时，提高动力电池配组成功率。

中文关键词： 信号处理；时间序列聚类；特征子形状；电池配组

英文摘要： Power battery grouping is one of the key issues to the fields of electric vehicles, etc.A successful grouping of batteries can improve the quality of the batter pack, reduce the cost of business services, and reduce environmental pollutions. The project proposes a time series clustering based battery grouping method, which selects the battery charge/discharge characteristics as the time series. The featured shapes which are sub-sequences of the original time series are extracted to characterize the original time sequence; battery grouping is then accomplished by a proper clustering algorithm based on these featured shapes, using an appropriate distance measure. The main research contents of this project: (1) featured shapes unsupervised learning methods for time series; (2) clustering method based on the featured shapes; (3) battery grouping technology based on time series clustering. By studying this project, we are expected to reveal the key external characteristics of batteries to the consistency of them; to propose an efficient high-performance time series clustering algorithm based on featured shapes, which can reduce the influence of noises existing in the time series; and to improve the performance while ensuring the efficiency for power battery grouping.

英文关键词： Signal Processing;Time Series Clustering;Featured Shapes;Battery Grouping