Lithium battery life estimation method based on IMM-UPF

Abstract

The invention discloses a lithium battery life estimation method based on IMM-UPF. The method uses a new fusion model interactive multi-model for fusion calculation of different attenuation models. Inconsideration that Kalman filtering may cause a large error because of the non-Gaussian and nonlinear attenuation trend of the lithium battery, the method filters each model by using unscented particle filtering, thereby on the one hand solving the lack of particles in particle filtering during resampling, and on the other hand, achieving a more accurate prediction result than the Kalman filtering. The provided IMM-UPF method is verified by the comparison of simulation results and experimental data. The results show that the method can improve the accuracy of lithium battery life prediction.

Description

technical field [0001] The invention relates to the technical field of lithium batteries, in particular to an IMM-UPF-based lithium battery life estimation method. Background technique [0002] With the rapid development of electric vehicles, lithium batteries have attracted much attention as their mainstream power source. State of health (SOH) is one of the key parameters of lithium batteries, and it is a direct parameter for users to evaluate the current battery life. [0003] At present, the life prediction of lithium batteries mainly adopts the methods based on physical principle modeling and data modeling to predict the capacity decay of lithium batteries. However, it is difficult to establish accurate analytical models for complex dynamic systems, especially those with uncertain noise. The method based on data modeling can capture the internal relationship in the data and learn the trend of change in the data, without the need for specific knowledge of material prope...

AI Technical Summary

Problems solved by technology

However, for complex dynamic systems, especially those with uncertain noise, it is difficult to establish accurate analytical models

The method based on data modeling can capture the internal relationship in the data and learn the trend of change in the data, without the need for specific knowledge of material properties, structure, failure mechanism, etc., avoiding the development of overly complex physical models, commonly used A single empirical model may achieve good prediction results at different stages, but it cannot describe the changing trend of the entire life cycle of lithium batteries well

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