Spacecraft lithium ion battery cycle life prediction method

Abstract

The invention discloses a spacecraft lithium ion battery cycle life prediction method. The method comprises the following steps: firstly collecting capacity data of a lithium ion battery; computing ahealth state time sequence SOHBAT of the lithium ion battery; decomposing the health state time sequence SOHBAT of the lithium ion battery by applying an empirical mode decomposition model; predictinga global degradation trend of the SOHBAT based on an ARIMA model; predicting the local regeneration and fluctuation of the SOHBAT based on the GPR model; integrating prediction results of the ARIMA model and the GPR model, and acquiring the spacecraft lithium ion battery cyclic life prediction. The global degradation trend and the local capacity regeneration and fluctuation phenomenon of the battery health state time sequence SOHBAT can be effectively extracted by utilizing the empirical mode decomposition method, the global trend and local fluctuation phenomenon of the battery health changecan be simulated by utilizing each of the ARIMA model and the GPR model, the capacity regeneration and fluctuation prediction problem in the battery performance degradation can be effectively solved,so that real health degradation trend of the lithium battery can be captured by integrating the models, and the accuracy of the lithium battery long-term prediction is improved.

Description

technical field [0001] The invention relates to the field of cycle life prediction methods for spacecraft lithium-ion batteries, in particular to a method for predicting the cycle life of spacecraft lithium-ion batteries based on a multi-scale ARIMA and GPR fusion model. Background technique [0002] Spacecraft has important strategic significance for social economy and national defense construction, and it plays an important role in the fields of meteorological observation, resource detection, military reconnaissance, geological exploration and positioning and navigation. The space environment is "dangerous", and various unexpected factors can easily cause abnormalities and failures of spacecraft. At the same time, the mechanical strain caused by the use of the spacecraft, the aging of components, and the performance degradation of the energy storage power supply affect the health of the spacecraft all the time. In the context of the rapid development of contemporary aeros...

AI Technical Summary

Problems solved by technology

[0009] The "capacity regeneration" and fluctuation phenomena of lithium batteries make the performance degradation curve very complicated

Existing traditional time series models (such as autoregressive integrated moving average model) and regression models (such as Gaussian process regression) cannot accurately predict the performance degradation curve of lithium batteries, and they are not sensitive to "capacity regeneration" and fluctuations. Poor forecasting accuracy and other issues

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