@Article{cmc.2020.011098,
AUTHOR = {Yudong Wang, Jie Tan, Zhenjie Liu, Allah Ditta},
TITLE = {Lithium-Ion Battery Screening by K-Means with DBSCAN for  Denoising},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {3},
PAGES = {2111--2122},
URL = {http://www.techscience.com/cmc/v65n3/40158},
ISSN = {1546-2226},
ABSTRACT = {Batteries are often packed together to meet voltage and capability needs. 
However, due to variations in raw materials, different ages of equipment, and manual 
operation, there is inconsistency between batteries, which leads to reduced available 
capacity, variability of resistance, and premature failure. Therefore, it is crucial to pack 
similar batteries together. The conventional approach to screening batteries is based on
their capacity, voltage and internal resistance, which disregards how batteries perform 
during manufacturing. In the battery discharge process, real time discharge voltage 
curves (DVCs) are collected as a set of unlabeled time series, which reflect how the 
battery voltage changes. However, few studies have focused on DVC based battery 
screening. In this paper, we provide an effective approach for battery screening. First, we 
apply interpolation on DVCs and give a method to transform them into slope sequences. 
Then, we use density-based spatial clustering of applications with noise (DBSCAN) for 
denoising and treat the remaining data as input to the K-means algorithm for screening. 
Finally, we provide the experimental results and give our evaluation. It is proved that our 
method is effective.},
DOI = {10.32604/cmc.2020.011098}
}