A Method of Battery Condition Analysis Based on Agglomerative Hierarchical Clustering Algorithm
A hierarchical clustering and clustering analysis technology, applied in the direction of calculation, measurement of electricity, measurement of electrical variables, etc., can solve the problems of dependence on validity and inability to directly and intuitively reflect the battery operating condition curve, and achieve the effect of ensuring battery safety
Active Publication Date: 2020-12-08
TSINGHUA UNIV
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However, the effectiveness of this method is very dependent on the selection of characteristic parameters, and the shape of the battery operating condition curve cannot be directly reflected only through the characteristic parameters of the battery operating condition; The Method of Cluster Analysis of Operating Condition Curves
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[0019] The present invention proposes a battery operating condition analysis method based on an agglomerative hierarchical clustering algorithm. The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.
[0020] The present invention proposes a battery operating condition analysis method based on agglomerative hierarchical clustering algorithm, the overall process is as follows figure 1 shown, including the following steps:
[0021] 1) Select the cluster analysis factor X of the battery working condition, and obtain the measurement data of X of the battery to be analyzed as the working condition of the battery to be analyzed; the measurement data constitutes a curve of X of the battery to be analyzed with respect to time t.
[0022] 2) Extract the basic curve unit of the cluster analysis factor X in the working condition to be analyzed;
[0023] 3) converting the basic curve unit extracted in step 2) into a...
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The invention provides a battery working condition analysis method based on a cohesion hierarchical clustering algorithm, and belongs to the technical field of batteries. The method comprises the following steps: firstly, selecting a battery condition clustering analysis factor X, and acquiring measurement data of a battery X to be analyzed as a to-be-analyzed condition of the battery; Extractinga basic curve unit of the clustering analysis factor X in the to-be-analyzed working condition, and converting the basic curve unit into to-be-clustered elements; And clustering the to-be-clustered elements by using a cohesion hierarchical clustering algorithm to obtain an operation curve of the battery under the corresponding working condition of each cluster. By means of the method, clustering analysis can be directly conducted on the working condition curve in the actual operation process of the battery without selecting characteristic parameters, and a clustering analysis result can provide guidance for estimating the attenuation rate of actual operation of the battery, improving the configuration and control strategy of a battery system and guaranteeing safe and long-life operation ofthe battery.
Description
technical field [0001] The invention relates to the field of battery technology, in particular to a battery working condition analysis method based on agglomerative hierarchical clustering algorithm. Background technique [0002] Lithium-ion batteries have the advantages of high energy density, long cycle life, low self-discharge rate, and no memory effect. They have become the main driving power sources for pure electric vehicles, plug-in electric vehicles, and hybrid electric vehicles, and are also used in energy storage systems, etc. Various scenarios. The analysis of the actual operating conditions of the battery in various application scenarios can provide a basis for constructing standard operating conditions under laboratory conditions, approximately simulating the actual operating conditions, conducting battery experiments, and evaluating battery performance; at the same time, the actual operating conditions of the battery The analysis can also provide support for t...
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IPC IPC(8): G06K9/62G01R31/367
Inventor 葛昊张剑波李哲
Owner TSINGHUA UNIV