A method for predicting the long-term degradation trend of lithium batteries

Abstract

The invention discloses a method for predicting the long-term degradation trend of a lithium battery, which includes: normalizing and smoothing the partial degradation trend curve used for the lithium battery as the original data, and obtaining the lithium battery used to input the trained prediction model Samples to be predicted; when the prediction model receives the lithium battery samples to be predicted, it gives the prediction action corresponding to the initial state of the lithium battery sample to be predicted, and the interactive environment used by the prediction model splices the prediction action corresponding to the initial state into the The end of the initial state of the lithium battery sample to be predicted is used as the first forecast trend curve; the first forecast trend curve result is intercepted and the sequence equal to the length of the single state is input to the forecast model as the next moment state, so that the forecast model Given the predicted action corresponding to the state at the next moment, the interactive environment splices the predicted action corresponding to the state at the next moment to the end of the state at the next moment, as the second predicted trend curve, until the final predicted trend curve is obtained.

Description

technical field [0001] The invention relates to a battery degradation trend prediction technology, in particular to a long-term degradation trend prediction method of a lithium battery. Background technique [0002] Fault prediction technology can not only provide decision-making basis for maintenance work such as equipment repair and replacement during the actual use of equipment, but also provide auxiliary decision-making information for the product design process during the performance test stage of equipment. For example, for lithium battery R&D companies, speeding up the process of improving product performance can capture market share more quickly. At the same time, in order to find out the performance characteristics of the new-formula lithium-ion battery, it needs to be tested and measured through a large number of performance testing experiments, and the relevant testing process often requires a lot of time and cost. Therefore, the use of degradation trend / remainin...

AI Technical Summary

Problems solved by technology

At the same time, in order to find out the performance characteristics of the new formula lithium-ion battery, it is necessary to test and measure through a large number of performance test experiments, and the related test process often takes a lot of time and cost

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