Lithium battery detection data preprocessing method based on multi-feature fusion
The invention belongs to the technical field of lithium ion battery remaining service life prediction, and relates to a lithium battery detection data preprocessing method based on multi-feature fusion. According to the preprocessing method, a lithium ion battery test data set is adopted, and a gray...
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| creator | MOU JIANHUI GAO LIXI TANG YI WANG SHUHAO |
| description | The invention belongs to the technical field of lithium ion battery remaining service life prediction, and relates to a lithium battery detection data preprocessing method based on multi-feature fusion. According to the preprocessing method, a lithium ion battery test data set is adopted, and a gray correlation analysis method is introduced to extract a lithium battery test data set of an equal-voltage drop time sequence and a discharge battery equal-temperature rise time sequence which have strong correlation with a battery capacity index according to the change condition of each index of the battery during charging and discharging; and carrying out feature modeling on the two time sequence indexes, and providing a health factor based on fusion of the two time sequences so as to extract the simplest data for subsequent residual service life prediction and reveal the inherent rule of battery life attenuation. The method aims to solve the problems of high dimensionality, data redundancy, low calculation effici |
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According to the preprocessing method, a lithium ion battery test data set is adopted, and a gray correlation analysis method is introduced to extract a lithium battery test data set of an equal-voltage drop time sequence and a discharge battery equal-temperature rise time sequence which have strong correlation with a battery capacity index according to the change condition of each index of the battery during charging and discharging; and carrying out feature modeling on the two time sequence indexes, and providing a health factor based on fusion of the two time sequences so as to extract the simplest data for subsequent residual service life prediction and reveal the inherent rule of battery life attenuation. 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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING MEASURING MEASURING ELECTRIC VARIABLES MEASURING MAGNETIC VARIABLES PHYSICS TESTING |
| title | Lithium battery detection data preprocessing method based on multi-feature fusion |
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