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- W3193746442 abstract "This article describes a method for quickly separating the same set of unhealthy batteries and predicting the healthof unknown battery sample by machine learning. The IEC60896-22-2004 and GB/T19638.2-2005 standardsspecify the capacity standards and testing methods for lead-acid batteries. The standard capacity test methodrequires a constant current discharge of for 10 hours. The test method is difficult to apply to a large number ofin-used battery groups in practice. In this paper, an alternative method based on machine learning is studied. Themethod is to perform fast 5 minute high current charging and 5 minute high current discharge on the battery groupwhich have been voltage balanced, extract the characteristics of charge and discharge, and map each batterycharacteristic through SOM neural network to 2D plane and then separated by cluster analysis for batteries ofdifferent capacity properties. Furthermore, through multiple machine learning and acquisition of real capacityaccording to standard methods, a training set for supervised learning is established, and SOM neural networkcluster center or a time series similarity search algorithm is used to quickly evaluate the capacity of unknownbattery samples." @default.
- W3193746442 created "2021-08-30" @default.
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- W3193746442 date "2021-08-20" @default.
- W3193746442 modified "2023-09-23" @default.
- W3193746442 title "Research on Rapid Detection of Battery Health Based on Som Neural Network" @default.
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