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摘要
The battery cells are an important part of electric and hybrid vehicles, and their deterioration due to aging or malfunction directly affects the life cycle and performance of the whole battery system. Therefore, an early detection of deviation in performance of the battery cells is an important task and its correct solution could significantly improve the whole vehicle performance. This paper presents a computational strategy for the detection of deviation of battery cells, due to aging or malfunction. The detection is based on periodically processing a predetermined number of data collected in data blocks that are obtained during the real operation of the vehicle. The first step is data compression, when the original large amount of data is reduced to smaller number of cluster centers. This is done by a newly proposed sequential clustering algorithm that arranges the clusters in decreasing order of their volumes. The next step is using a fuzzy inference procedure for weighted approximation of the cluster centers to create one-dimensional models for each battery cell that represents the voltage-current relationship. This creates an equal basis for the further comparison of the battery cells. Finally, the detection of the deviated battery cells is treated as a similarity-analysis problem, in which the pair distances between all battery cells are estimated by analyzing the estimations for voltage from the respective fuzzy models. All these three steps of the computational procedure are explained in the paper and applied to real experimental data for the detection of deviation of five battery cells. Discussions and suggestions are made for a practical application aimed at designing a monitoring system for the detection of deviations.
- 出版日期2014-3
