Generation method and system for classification model of power battery, and classification method and system for power battery
A classification model and power battery technology, applied in character and pattern recognition, instruments, computer components, etc., can solve cumbersome, computationally difficult, and unsolvable problems
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Embodiment 1
[0113] Such as figure 1 As shown, this embodiment provides a method for generating a classification model of a power battery, including the following steps:
[0114] Step S0, obtaining a total sample set, sampling the total sample set with a sampling rate r to obtain a sample subset, and setting the sample subset as a training sample set;
[0115] Step S1, obtaining the external characteristic data of each battery in the training sample set, the external characteristic data including charging voltage, charging current, discharging voltage, discharging current, battery internal resistance, SOC and historical charge and discharge times;
[0116] Step S2, performing preprocessing on the external characteristic data to generate a corresponding data matrix, specifically including sequential data invalidation processing, data normalization processing and data matrix processing;
[0117] Step S3, using the data matrix of more than half of the batteries in the training sample set to ...
Embodiment 2
[0146] Such as Figure 4 As shown, this embodiment provides a system for generating a classification model of a power battery, including a sampling module 0 , a data acquisition module 1 , a preprocessing module 2 , a model generation module 3 and a verification module 4 .
[0147] The sampling module 0 is configured to obtain a total sample set, use a sampling rate r to sample the total sample set to obtain a sample subset, and set the sample subset as a training sample set.
[0148] The data acquisition module 1 is used to acquire the external characteristic data of each battery in the training sample set, the external characteristic data includes charging voltage, charging current, discharging voltage, discharging current, battery internal resistance, SOC and historical charge and discharge times.
[0149] The preprocessing module 2 is configured to preprocess the external characteristic data to generate a corresponding data matrix, specifically including data invalidation ...
Embodiment 3
[0174] Such as Figure 7 As shown, this embodiment provides a classification method for a power battery, using the classification model generated by the method for generating a classification model of a power battery in Example 1 to classify the battery to be tested to determine the classification of the battery to be tested. into a group in the grouping results, specifically including the following steps:
[0175] Step T1, obtaining the external characteristic data of the battery to be tested;
[0176] Step T2, preprocessing the external characteristic data to generate a corresponding data matrix;
[0177] Step T3, calculating the Euclidean distance between the data matrix of the battery to be tested and the representative values of all groups in the grouping result as the fourth Euclidean distance, according to the fourth Euclidean distance and the maximum Euclidean distance Reed distance D max The magnitude relationship of determines that the battery under test is clas...
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