Consistency is an essential factor affecting the operation of lithium-ion battery packs. Pack consistency evaluation is of considerable significance to the usage of batteries. Many existing methods are limited for they are based on a single feature or can only be implemented offline. This paper develops a comprehensive method to evaluate the pack consistency based on multi-feature weighting. Firstly, the features which reflect the static or dynamic characteristics of batteries are excavated. Secondly, a weighted method of multi-feature inconsistency is proposed to evaluate pack consistency. In which case, the entropy weight method is employed to determine the weight. Thirdly, an improved Greenwald-Khanna algorithm based on genetic algorithm and kernel function is developed to cluster batteries. Finally, nine months of electric vehicle data are collated to validate the proposed algorithms. Meanwhile, the main factor affecting consistency change is analyzed. The results show that with the usage of batteries, the difference between the cells becomes more serious, which weakens the pack consistency. Besides, the relationship between the consistency attenuation rate and the driving mileage can be approximated by a first-order function. The higher mileages will aggravate the pack inconsistency. Moreover, it has been proven that the improved clustering algorithm has stronger robustness and classification performance.

中文翻译：

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电动汽车锂离子电池组一致性评价与聚类分析

一致性是影响锂离子电池组运行的重要因素。电池组一致性评价对电池的使用具有重要意义。许多现有的方法都受到限制，因为它们基于单个特征或只能离线实现。本文开发了一种基于多特征权重来评估包一致性的综合方法。首先，挖掘反映电池静态或动态特性的特征。其次，提出了一种多特征不一致的加权方法来评估包的一致性。在这种情况下，采用熵权法来确定权重。第三，开发了一种基于遗传算法和核函数的改进Greenwald-Khanna算法对电池进行聚类。最后，整理了九个月的电动汽车数据以验证所提出的算法。同时分析了影响一致性变化的主要因素。结果表明，随着电池的使用，电芯之间的差异变得更加严重，从而削弱了电池组的一致性。此外，一致性衰减率与行驶里程之间的关系可以用一阶函数近似。更高的里​​程会加剧包装的不一致性。而且，已经证明改进的聚类算法具有更强的鲁棒性和分类性能。这削弱了包装的一致性。此外，一致性衰减率与行驶里程之间的关系可以用一阶函数近似。更高的里​​程会加剧包装的不一致性。而且，已经证明改进的聚类算法具有更强的鲁棒性和分类性能。这削弱了包装的一致性。此外，一致性衰减率与行驶里程之间的关系可以用一阶函数近似。更高的里​​程会加剧包装的不一致。而且，已经证明改进的聚类算法具有更强的鲁棒性和分类性能。