摘要:限于单体的功率和能量,电池组必须由成百上千的单体串并联而成,同时依赖于有效的电池管理技术,从而保证车辆 行驶的动力性和经济性。然而制造误差和使用环境的不同导致电池单体存在不一致性,这使得准确估计所有单体电池荷电状 态(State of charge,SOC)变得困难,从而导致电池发生过充和过放。因此,融合电池组模型和聚类算法,提出一种新的锂离 Abstract:Limited by the power and energy of a single cell, a battery pack must consist of hundreds or even thousands of cells in series and parallel, and at the same time rely on an effective battery management system to ensure enough driving power and economy of vehicles. However, due to the manufacturing process and

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... ating environment, there exist parameter inconsistencies among battery cells, which brings challenges to accurately estimating the state of charge (SOC) of all the cells in the pack, and results in over-charge and over-discharge. To estimate the SOC inconsistencies in lithium-ion battery packs, a new method is proposed by combing the battery pack modeling and the clustering algorithm, which can greatly reduce the computational complexity while ensuring accuracy. Based on the features of charging data, the bisecting k-means algorithm is adopted to categorize cells into different levels, reducing the objects that need to be considered. Then, a battery pack model considering parameter discrepancies is established to estimate SOC values of cells. The estimation accuracy and computational complexity have been verified by using both simulation and real-test data, and compared with existing models. The results indicate that the SOC estimation error of a single cell is less than 0.03 by using the proposed method, and the computational efficiency can be improved by 3-6 times. Key words:lithium-ion battery;parameters inconsistency;clustering algorithm;battery pack model;SOC estimation 0 前言 * 随着全球经济快速发展,车辆需求量剧增,清  天地科技股份有限公司科技创新创业资金专项(2019-TD-QN036)和国 家自然科学基金(51875054)资助项目。20200529 收到初稿,20200703 收到修改稿 洁、安全、高效的新能源的开发、存储和利用技术 成为当前社会的研究热点 [1] 。相比于传统内燃机车 辆,电动车辆以其经济环保的优势得到快速发展。 动力电池技术作为电动车辆的关键技术之一,为保 证其能够在复杂的路况和环境下安全高效工作,需 要配置高效的电池管理系统 (Battery management 月 2020 年 9 月 向兆军等:基于电池组模型和聚类算法的锂离子电池组 SOC 不一致估计 155