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电动汽车锂电池模块设计中相似性能电池聚类的综合方法
Wei Li, Siqi Chen, Xiongbin Peng, Mi Xiao, Liang Gao, Akhil Garg, Nengsheng Bao
工程（英文） ›› 2019, Vol. 5 ›› Issue (4) : 795-802.
PDF(1229 KB)
PDF(1229 KB)
电动汽车锂电池模块设计中相似性能电池聚类的综合方法
A Comprehensive Approach for the Clustering of Similar-Performance Cells for the Design of a Lithium-Ion Battery Module for Electric Vehicles
新能源汽车的核心组成部分为能量存储系统，该系统由多个锂电池模块组成，为车辆传动系统提供主要动力。然而模块中的单体电池由于生产制造的缺陷，在性能上往往表现出差异。这些差异的存在会导致电池模块的不完全充放电以及温度分布的不均匀，进而导致循环寿命和电池容量随着时间的推移而降低。为解决这一问题，本工作采用实验和数值方法对性能相似的电池进行了全面的聚类研究，从而得到了电化学性能更好的电池模块。首先通过模块拆解实验来测量电池性能参数，并基于k-均值聚类与支持向量聚类算法设计电池模块，每个模块均由12块电池组成。然后在风冷条件下测量一定时间内电池模块的实际温升，验证聚类设计的效果。研究发现第三类（支持向量聚类）电池模块的性能最佳，充放电最高观测温度为32 ℃。相比之下，其他电池模块的最高温度值要更高：第一类（厂家原装）电池模块为40 ℃，第二类（厂家原装）电池模块为36 ℃，以及第四类（k-均值聚类）电池模块为35 ℃。
An energy-storage system comprised of lithium-ion battery modules is considered to be a core component of new energy vehicles, as it provides the main power source for the transmission system. However, manufacturing defects in battery modules lead to variations in performance among the cells used in series or parallel configuration. This variation results in incomplete charge and discharge of batteries and non-uniform temperature distribution, which further lead to reduction of cycle life and battery capacity over time. To solve this problem, this work uses experimental and numerical methods to conduct a comprehensive investigation on the clustering of battery cells with similar performance in order to produce a battery module with improved electrochemical performance. Experiments were first performed by dismantling battery modules for the measurement of performance parameters. The k-means clustering and support vector clustering (SVC) algorithms were then employed to produce battery modules composed of 12 cells each. Experimental verification of the results obtained from the clustering analysis was performed by measuring the temperature rise in the cells over a certain period, while air cooling was provided. It was found that the SVC-clustered battery module in Category 3 exhibited the best performance, with a maximum observed temperature of 32 ℃. By contrast, the maximum observed temperatures of the other battery modules were higher, at 40 ℃ for Category 1 (manufacturer), 36 ℃ for Category 2 (manufacturer), and 35 ℃ for Category 4 (k-means-clustered battery module).
Clustering algorithm / Battery module / Equalization / Electric vehicle
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